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Momenaei B, Mansour HA, Kuriyan AE, Xu D, Sridhar J, Ting DSW, Yonekawa Y. ChatGPT enters the room: what it means for patient counseling, physician education, academics, and disease management. Curr Opin Ophthalmol 2024; 35:205-209. [PMID: 38334288 DOI: 10.1097/icu.0000000000001036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
PURPOSE OF REVIEW This review seeks to provide a summary of the most recent research findings regarding the utilization of ChatGPT, an artificial intelligence (AI)-powered chatbot, in the field of ophthalmology in addition to exploring the limitations and ethical considerations associated with its application. RECENT FINDINGS ChatGPT has gained widespread recognition and demonstrated potential in enhancing patient and physician education, boosting research productivity, and streamlining administrative tasks. In various studies examining its utility in ophthalmology, ChatGPT has exhibited fair to good accuracy, with its most recent iteration showcasing superior performance in providing ophthalmic recommendations across various ophthalmic disorders such as corneal diseases, orbital disorders, vitreoretinal diseases, uveitis, neuro-ophthalmology, and glaucoma. This proves beneficial for patients in accessing information and aids physicians in triaging as well as formulating differential diagnoses. Despite such benefits, ChatGPT has limitations that require acknowledgment including the potential risk of offering inaccurate or harmful information, dependence on outdated data, the necessity for a high level of education for data comprehension, and concerns regarding patient privacy and ethical considerations within the research domain. SUMMARY ChatGPT is a promising new tool that could contribute to ophthalmic healthcare education and research, potentially reducing work burdens. However, its current limitations necessitate a complementary role with human expert oversight.
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Affiliation(s)
- Bita Momenaei
- Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Hana A Mansour
- Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Ajay E Kuriyan
- Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - David Xu
- Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Jayanth Sridhar
- University of California Los Angeles, Los Angeles, California, USA
| | | | - Yoshihiro Yonekawa
- Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania
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Lim JS, Tan SS, Yeo YL, Hong M, Teo AWJ, Lee YF, Ting DSW, Aung T, Husain R. Replacing the postoperative week 1 visit after routine phacoemulsification with a telephone consult. Can J Ophthalmol 2024:S0008-4182(24)00096-6. [PMID: 38604239 DOI: 10.1016/j.jcjo.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 02/17/2024] [Accepted: 03/19/2024] [Indexed: 04/13/2024]
Abstract
OBJECTIVE To assess the safety of replacing the postoperative week 1 (POW1) clinic visit with a nurse-conducted telephone call. DESIGN Retrospective observational study that included cases from January 2019 to June 2021. PARTICIPANTS Patients who had undergone uncomplicated phacoemulsification surgery with an unremarkable postoperative day 1 (POD1) examination. METHODS All patients were seen in clinic on POD1 by an ophthalmologist. They then had a telephone conversation with a nurse at POW1 and subsequently an in-person postoperative month 1 (POM1) clinic consultation with an ophthalmologist. Main outcome measure was the incidence of unexpected management changes related to cataract surgery within POM1. Data also were collected on the reasons for unscheduled patient-initiated visits, additional procedures or medications, and postoperative visual acuity worse than 6/12 at POM1. RESULTS Of the 20,475 patients, 541 patients (2.64%) had an unexpected management change within POM1. There were 565 patients (2.76%) who had self-initiated unscheduled visits between POD1 to POM1. There were 23 patients (0.11%) who required additional surgery within POM1 and 1 patient (0.005%) with endophthalmitis. The most common indication for additional surgical procedures was retained lens material (7 patients, 30.43%). Visual acuity was worse than 6/12 in 1,199 patients (6.22%), with the most common causes attributed to preexisting ocular conditions. CONCLUSIONS These results suggest that replacing the POW1 visit with a nurse-conducted telephone consult for patients who have undergone uncomplicated phacoemulsification surgery and had a normal POD1 consultation is safe.
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Affiliation(s)
- Jane S Lim
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
| | | | - Yi Lin Yeo
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
| | | | | | - Yi Fang Lee
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
| | - Daniel S W Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
| | - Tin Aung
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
| | - Rahat Husain
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore.
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Foo VHX, Lim GYS, Liu YC, Ong HS, Wong E, Chan S, Wong J, Mehta JS, Ting DSW, Ang M. Deep learning for detection of Fuchs endothelial dystrophy from widefield specular microscopy imaging: a pilot study. Eye Vis (Lond) 2024; 11:11. [PMID: 38494521 PMCID: PMC10946096 DOI: 10.1186/s40662-024-00378-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 02/18/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND To describe the diagnostic performance of a deep learning (DL) algorithm in detecting Fuchs endothelial corneal dystrophy (FECD) based on specular microscopy (SM) and to reliably detect widefield peripheral SM images with an endothelial cell density (ECD) > 1000 cells/mm2. METHODS Five hundred and forty-seven subjects had SM imaging performed for the central cornea endothelium. One hundred and seventy-three images had FECD, while 602 images had other diagnoses. Using fivefold cross-validation on the dataset containing 775 central SM images combined with ECD, coefficient of variation (CV) and hexagonal endothelial cell ratio (HEX), the first DL model was trained to discriminate FECD from other images and was further tested on an external set of 180 images. In eyes with FECD, a separate DL model was trained with 753 central/paracentral SM images to detect SM with ECD > 1000 cells/mm2 and tested on 557 peripheral SM images. Area under curve (AUC), sensitivity and specificity were evaluated. RESULTS The first model achieved an AUC of 0.96 with 0.91 sensitivity and 0.91 specificity in detecting FECD from other images. With an external validation set, the model achieved an AUC of 0.77, with a sensitivity of 0.69 and specificity of 0.68 in differentiating FECD from other diagnoses. The second model achieved an AUC of 0.88 with 0.79 sensitivity and 0.78 specificity in detecting peripheral SM images with ECD > 1000 cells/mm2. CONCLUSIONS Our pilot study developed a DL model that could reliably detect FECD from other SM images and identify widefield SM images with ECD > 1000 cells/mm2 in eyes with FECD. This could be the foundation for future DL models to track progression of eyes with FECD and identify candidates suitable for therapies such as Descemet stripping only.
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Affiliation(s)
- Valencia Hui Xian Foo
- Singapore National Eye Centre, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
| | | | - Yu-Chi Liu
- Singapore National Eye Centre, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, Ophthalmology and Visual Science Academic Clinical Research Program, Singapore, Singapore
| | - Hon Shing Ong
- Singapore National Eye Centre, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, Ophthalmology and Visual Science Academic Clinical Research Program, Singapore, Singapore
| | - Evan Wong
- Singapore National Eye Centre, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
| | - Stacy Chan
- Singapore Eye Research Institute, Singapore, Singapore
| | - Jipson Wong
- Singapore Eye Research Institute, Singapore, Singapore
| | - Jodhbir S Mehta
- Singapore National Eye Centre, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, Ophthalmology and Visual Science Academic Clinical Research Program, Singapore, Singapore
| | - Daniel S W Ting
- Singapore National Eye Centre, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, Ophthalmology and Visual Science Academic Clinical Research Program, Singapore, Singapore
| | - Marcus Ang
- Singapore National Eye Centre, 11 Third Hospital Avenue, Singapore, 168751, Singapore.
- Singapore Eye Research Institute, Singapore, Singapore.
- Duke-NUS Medical School, Ophthalmology and Visual Science Academic Clinical Research Program, Singapore, Singapore.
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Poh SSJ, Sia JT, Yip MYT, Tsai ASH, Lee SY, Tan GSW, Weng CY, Kadonosono K, Kim M, Yonekawa Y, Ho AC, Toth CA, Ting DSW. Artificial Intelligence, Digital Imaging, and Robotics Technologies for Surgical Vitreoretinal Diseases. Ophthalmol Retina 2024:S2468-6530(24)00044-7. [PMID: 38280425 DOI: 10.1016/j.oret.2024.01.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 01/14/2024] [Accepted: 01/19/2024] [Indexed: 01/29/2024]
Abstract
OBJECTIVE To review recent technological advancement in imaging, surgical visualization, robotics technology, and the use of artificial intelligence in surgical vitreoretinal (VR) diseases. BACKGROUND Technological advancements in imaging enhance both preoperative and intraoperative management of surgical VR diseases. Widefield imaging in fundal photography and OCT can improve assessment of peripheral retinal disorders such as retinal detachments, degeneration, and tumors. OCT angiography provides a rapid and noninvasive imaging of the retinal and choroidal vasculature. Surgical visualization has also improved with intraoperative OCT providing a detailed real-time assessment of retinal layers to guide surgical decisions. Heads-up display and head-mounted display utilize 3-dimensional technology to provide surgeons with enhanced visual guidance and improved ergonomics during surgery. Intraocular robotics technology allows for greater surgical precision and is shown to be useful in retinal vein cannulation and subretinal drug delivery. In addition, deep learning techniques leverage on diverse data including widefield retinal photography and OCT for better predictive accuracy in classification, segmentation, and prognostication of many surgical VR diseases. CONCLUSION This review article summarized the latest updates in these areas and highlights the importance of continuous innovation and improvement in technology within the field. These advancements have the potential to reshape management of surgical VR diseases in the very near future and to ultimately improve patient care. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Stanley S J Poh
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Josh T Sia
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
| | - Michelle Y T Yip
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
| | - Andrew S H Tsai
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Shu Yen Lee
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Gavin S W Tan
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Christina Y Weng
- Department of Ophthalmology, Baylor College of Medicine, Houston, Texas
| | | | - Min Kim
- Department of Ophthalmology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Yoshihiro Yonekawa
- Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Allen C Ho
- Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Cynthia A Toth
- Departments of Ophthalmology and Biomedical Engineering, Duke University, Durham, North Carolina
| | - Daniel S W Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore; Byers Eye Institute, Stanford University, Palo Alto, California.
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Bhatnagar A, Ting DSW, Weng CY. Treatment Options for Diabetic Macular Edema. Int Ophthalmol Clin 2024; 64:57-69. [PMID: 38146881 DOI: 10.1097/iio.0000000000000518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
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Wang Y, Du R, Xie S, Chen C, Lu H, Xiong J, Ting DSW, Uramoto K, Kamoi K, Ohno-Matsui K. Machine Learning Models for Predicting Long-Term Visual Acuity in Highly Myopic Eyes. JAMA Ophthalmol 2023; 141:1117-1124. [PMID: 37883115 PMCID: PMC10603576 DOI: 10.1001/jamaophthalmol.2023.4786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/01/2023] [Indexed: 10/27/2023]
Abstract
Importance High myopia is a global concern due to its escalating prevalence and the potential risk of severe visual impairment caused by pathologic myopia. Using artificial intelligence to estimate future visual acuity (VA) could help clinicians to identify and monitor patients with a high risk of vision reduction in advance. Objective To develop machine learning models to predict VA at 3 and 5 years in patients with high myopia. Design, Setting, and Participants This retrospective, single-center, cohort study was performed on patients whose best-corrected VA (BCVA) at 3 and 5 years was known. The ophthalmic examinations of these patients were performed between October 2011 and May 2021. Thirty-four variables, including general information, basic ophthalmic information, and categories of myopic maculopathy based on fundus and optical coherence tomography images, were collected from the medical records for analysis. Main Outcomes and Measures Regression models were developed to predict BCVA at 3 and 5 years, and a binary classification model was developed to predict the risk of developing visual impairment at 5 years. The performance of models was evaluated by discrimination metrics, calibration belts, and decision curve analysis. The importance of relative variables was assessed by explainable artificial intelligence techniques. Results A total of 1616 eyes from 967 patients (mean [SD] age, 58.5 [14.0] years; 678 female [70.1%]) were included in this analysis. Findings showed that support vector machines presented the best prediction of BCVA at 3 years (R2 = 0.682; 95% CI, 0.625-0.733) and random forest at 5 years (R2 = 0.660; 95% CI, 0.604-0.710). To predict the risk of visual impairment at 5 years, logistic regression presented the best performance (area under the receiver operating characteristic curve = 0.870; 95% CI, 0.816-0.912). The baseline BCVA (logMAR odds ratio [OR], 0.298; 95% CI, 0.235-0.378; P < .001), prior myopic macular neovascularization (OR, 3.290; 95% CI, 2.209-4.899; P < .001), age (OR, 1.578; 95% CI, 1.227-2.028; P < .001), and category 4 myopic maculopathy (OR, 4.899; 95% CI, 1.431-16.769; P = .01) were the 4 most important predicting variables and associated with increased risk of visual impairment at 5 years. Conclusions and Relevance Study results suggest that developing models for accurate prediction of the long-term VA for highly myopic eyes based on clinical and imaging information is feasible. Such models could be used for the clinical assessments of future visual acuity.
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Affiliation(s)
- Yining Wang
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Ran Du
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
- Department of Ophthalmology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
| | - Shiqi Xie
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Changyu Chen
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Hongshuang Lu
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Jianping Xiong
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Daniel S. W. Ting
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Kengo Uramoto
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Koju Kamoi
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kyoko Ohno-Matsui
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
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Vujosevic S, Ting DSW. Is Central Retina Thickness the Most Relevant Parameter in the Management of Diabetic Macular Edema? Retina 2023; 43:1639-1643. [PMID: 37603419 DOI: 10.1097/iae.0000000000003914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
Affiliation(s)
- Stela Vujosevic
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
- Eye Clinic, IRCCS MultiMedica, Milan, Italy
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore
- Duke-NUS Medical School, Singapore
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Yeung AWK, Torkamani A, Butte AJ, Glicksberg BS, Schuller B, Rodriguez B, Ting DSW, Bates D, Schaden E, Peng H, Willschke H, van der Laak J, Car J, Rahimi K, Celi LA, Banach M, Kletecka-Pulker M, Kimberger O, Eils R, Islam SMS, Wong ST, Wong TY, Gao W, Brunak S, Atanasov AG. The promise of digital healthcare technologies. Front Public Health 2023; 11:1196596. [PMID: 37822534 PMCID: PMC10562722 DOI: 10.3389/fpubh.2023.1196596] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 09/04/2023] [Indexed: 10/13/2023] Open
Abstract
Digital health technologies have been in use for many years in a wide spectrum of healthcare scenarios. This narrative review outlines the current use and the future strategies and significance of digital health technologies in modern healthcare applications. It covers the current state of the scientific field (delineating major strengths, limitations, and applications) and envisions the future impact of relevant emerging key technologies. Furthermore, we attempt to provide recommendations for innovative approaches that would accelerate and benefit the research, translation and utilization of digital health technologies.
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Affiliation(s)
- Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, University of Hong Kong, Hong Kong, China
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Ali Torkamani
- Department of Integrative Structural and Computational Biology, Scripps Research Translational Institute, La Jolla, CA, United States
| | - Atul J. Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States
| | - Benjamin S. Glicksberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Björn Schuller
- Department of Computing, Imperial College London, London, United Kingdom
- Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Blanca Rodriguez
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Daniel S. W. Ting
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - David Bates
- Department of General Internal Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Eva Schaden
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Medical University of Vienna, Vienna, Austria
| | - Hanchuan Peng
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Harald Willschke
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Medical University of Vienna, Vienna, Austria
| | - Jeroen van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Josip Car
- Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
- Centre for Population Health Sciences, LKC Medicine, Nanyang Technological University, Singapore, Singapore
| | - Kazem Rahimi
- Deep Medicine Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Leo Anthony Celi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Maciej Banach
- Department of Preventive Cardiology and Lipidology, Medical University of Lodz (MUL), Lodz, Poland
- Department of Cardiology and Adult Congenital Heart Diseases, Polish Mother’s Memorial Hospital Research Institute (PMMHRI), Lodz, Poland
| | - Maria Kletecka-Pulker
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Institute for Ethics and Law in Medicine, University of Vienna, Vienna, Austria
| | - Oliver Kimberger
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Medical University of Vienna, Vienna, Austria
| | - Roland Eils
- Digital Health Center, Berlin Institute of Health (BIH), Charité – Universitätsmedizin Berlin, Berlin, Germany
| | | | - Stephen T. Wong
- Department of Systems Medicine and Bioengineering, Houston Methodist Cancer Center, T. T. and W. F. Chao Center for BRAIN, Houston Methodist Academic Institute, Houston Methodist Hospital, Houston, TX, United States
- Departments of Radiology, Pathology and Laboratory Medicine and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, United States
| | - Tien Yin Wong
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Wei Gao
- Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Atanas G. Atanasov
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Jastrzebiec, Poland
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RaviChandran N, Teo ZL, Ting DSW. Artificial intelligence enabled smart digital eye wearables. Curr Opin Ophthalmol 2023; 34:414-421. [PMID: 37527195 DOI: 10.1097/icu.0000000000000985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Abstract
PURPOSE OF REVIEW Smart eyewear is a head-worn wearable device that is evolving as the next phase of ubiquitous wearables. Although their applications in healthcare are being explored, they have the potential to revolutionize teleophthalmology care. This review highlights their applications in ophthalmology care and discusses future scope. RECENT FINDINGS Smart eyewear equips advanced sensors, optical displays, and processing capabilities in a wearable form factor. Rapid technological developments and the integration of artificial intelligence are expanding their reach from consumer space to healthcare applications. This review systematically presents their applications in treating and managing eye-related conditions. This includes remote assessments, real-time monitoring, telehealth consultations, and the facilitation of personalized interventions. They also serve as low-vision assistive devices to help visually impaired, and can aid physicians with operational and surgical tasks. SUMMARY Wearables such as smart eyewear collects rich, continuous, objective, individual-specific data, which is difficult to obtain in a clinical setting. By leveraging sophisticated data processing and artificial intelligence based algorithms, these data can identify at-risk patients, recognize behavioral patterns, and make timely interventions. They promise cost-effective and personalized treatment for vision impairments in an effort to mitigate the global burden of eye-related conditions and aging.
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Affiliation(s)
| | - Zhen Ling Teo
- Singapore National Eye Center, Singapore Eye Research Institute
| | - Daniel S W Ting
- AI and Digital Innovations
- Singapore National Eye Center, Singapore Eye Research Institute
- Duke-NUS Medical School, National University Singapore, Singapore
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Heindl LM, Li S, Ting DSW, Keane PA. Artificial intelligence in ophthalmological practice: when ideal meets reality. BMJ Open Ophthalmol 2023; 8:e001129. [PMID: 37493688 PMCID: PMC10255244 DOI: 10.1136/bmjophth-2022-001129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023] Open
Affiliation(s)
- Ludwig M Heindl
- Department of Ophthalmology, University of Cologne, Koln, Germany
| | - Senmao Li
- Department of Ophthalmology, University of Cologne, Koln, Germany
- Department of Ophthalmology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Daniel S W Ting
- Singapore National Eye Center, Duke-NUS Medical School, Singapore
- Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore
| | - Pearse A Keane
- Medical Retina, Moorfields Eye Hospital NHS Foundation Trust, London, UK
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Vasseneix C, Nusinovici S, Xu X, Hwang JM, Hamann S, Chen JJ, Loo JL, Milea L, Tan KBK, Ting DSW, Liu Y, Newman NJ, Biousse V, Wong TY, Milea D, Najjar RP. Deep Learning System Outperforms Clinicians in Identifying Optic Disc Abnormalities. J Neuroophthalmol 2023; 43:159-167. [PMID: 36719740 DOI: 10.1097/wno.0000000000001800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
BACKGROUND The examination of the optic nerve head (optic disc) is mandatory in patients with headache, hypertension, or any neurological symptoms, yet it is rarely or poorly performed in general clinics. We recently developed a brain and optic nerve study with artificial intelligence-deep learning system (BONSAI-DLS) capable of accurately detecting optic disc abnormalities including papilledema (swelling due to elevated intracranial pressure) on digital fundus photographs with a comparable classification performance to expert neuro-ophthalmologists, but its performance compared to first-line clinicians remains unknown. METHODS In this international, cross-sectional multicenter study, the DLS, trained on 14,341 fundus photographs, was tested on a retrospectively collected convenience sample of 800 photographs (400 normal optic discs, 201 papilledema and 199 other abnormalities) from 454 patients with a robust ground truth diagnosis provided by the referring expert neuro-ophthalmologists. The areas under the receiver-operating-characteristic curves were calculated for the BONSAI-DLS. Error rates, accuracy, sensitivity, and specificity of the algorithm were compared with those of 30 clinicians with or without ophthalmic training (6 general ophthalmologists, 6 optometrists, 6 neurologists, 6 internists, 6 emergency department [ED] physicians) who graded the same testing set of images. RESULTS With an error rate of 15.3%, the DLS outperformed all clinicians (average error rates 24.4%, 24.8%, 38.2%, 44.8%, 47.9% for general ophthalmologists, optometrists, neurologists, internists and ED physicians, respectively) in the overall classification of optic disc appearance. The DLS displayed significantly higher accuracies than 100%, 86.7% and 93.3% of clinicians (n = 30) for the classification of papilledema, normal, and other disc abnormalities, respectively. CONCLUSIONS The performance of the BONSAI-DLS to classify optic discs on fundus photographs was superior to that of clinicians with or without ophthalmic training. A trained DLS may offer valuable diagnostic aid to clinicians from various clinical settings for the screening of optic disc abnormalities harboring potentially sight- or life-threatening neurological conditions.
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Affiliation(s)
- Caroline Vasseneix
- Visual Neuroscience Group (CV, SN, DT, TYW, DM, RPN), Singapore Eye Research Institute, Singapore; Duke NUS Medical School (DT, TYW, DM, RPN), National University of Singapore, Singapore; Institute of High Performance Computing (XX, YL), Agency for Science, Technology and Research (A*STAR), Singapore; Department of Ophthalmology (J-MH), Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam-si, Korea (the Republic of); Department of Ophthalmology (SH), Rigshospitalet, University of Copenhagen, Kobenhavn, Denmark ; Departments of Ophthalmology and Neurology (JJC), Mayo Clinic Rochester, Minnesota; Singapore National Eye Centre (JLL, DT, TYW, DM), Singapore; Berkeley University (LM), Berkeley, California; Department of Emergency Medicine (KT), Singapore General Hospital, Singapore; Departments of Ophthalmology, Neurology and Neurological Surgery (NJN, VB), Emory University School of Medicine, Atlanta, Georgia; and Department of Ophthalmology (RPN), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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12
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Li Y, Yip MYT, Ting DSW, Ang M. Artificial intelligence and digital solutions for myopia. Taiwan J Ophthalmol 2023; 13:142-150. [PMID: 37484621 PMCID: PMC10361438 DOI: 10.4103/tjo.tjo-d-23-00032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 03/16/2023] [Indexed: 07/25/2023] Open
Abstract
Myopia as an uncorrected visual impairment is recognized as a global public health issue with an increasing burden on health-care systems. Moreover, high myopia increases one's risk of developing pathologic myopia, which can lead to irreversible visual impairment. Thus, increased resources are needed for the early identification of complications, timely intervention to prevent myopia progression, and treatment of complications. Emerging artificial intelligence (AI) and digital technologies may have the potential to tackle these unmet needs through automated detection for screening and risk stratification, individualized prediction, and prognostication of myopia progression. AI applications in myopia for children and adults have been developed for the detection, diagnosis, and prediction of progression. Novel AI technologies, including multimodal AI, explainable AI, federated learning, automated machine learning, and blockchain, may further improve prediction performance, safety, accessibility, and also circumvent concerns of explainability. Digital technology advancements include digital therapeutics, self-monitoring devices, virtual reality or augmented reality technology, and wearable devices - which provide possible avenues for monitoring myopia progression and control. However, there are challenges in the implementation of these technologies, which include requirements for specific infrastructure and resources, demonstrating clinically acceptable performance and safety of data management. Nonetheless, this remains an evolving field with the potential to address the growing global burden of myopia.
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Affiliation(s)
- Yong Li
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology and Visual Sciences, Duke-NUS Medical School, National University of Singapore, Singapore
| | - Michelle Y. T. Yip
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Daniel S. W. Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology and Visual Sciences, Duke-NUS Medical School, National University of Singapore, Singapore
| | - Marcus Ang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology and Visual Sciences, Duke-NUS Medical School, National University of Singapore, Singapore
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13
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Sharma A, Wu L, Bloom S, Stanga P, RaviChandran N, Ting DSW, Parolini B, Matello V, Rezaei KA. RWC Update: Artificial Intelligence and Smart Eyewearables for Healthy Longevity; Choroidal Hemangioma Widefield Optical Coherence Tomography. Ophthalmic Surg Lasers Imaging Retina 2023; 54:74-77. [PMID: 36780639 DOI: 10.3928/23258160-20221219-02] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
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14
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Ting DSJ, Deshmukh R, Ting DSW, Ang M. Big data in corneal diseases and cataract: Current applications and future directions. Front Big Data 2023; 6:1017420. [PMID: 36818823 PMCID: PMC9929069 DOI: 10.3389/fdata.2023.1017420] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 01/16/2023] [Indexed: 02/04/2023] Open
Abstract
The accelerated growth in electronic health records (EHR), Internet-of-Things, mHealth, telemedicine, and artificial intelligence (AI) in the recent years have significantly fuelled the interest and development in big data research. Big data refer to complex datasets that are characterized by the attributes of "5 Vs"-variety, volume, velocity, veracity, and value. Big data analytics research has so far benefitted many fields of medicine, including ophthalmology. The availability of these big data not only allow for comprehensive and timely examinations of the epidemiology, trends, characteristics, outcomes, and prognostic factors of many diseases, but also enable the development of highly accurate AI algorithms in diagnosing a wide range of medical diseases as well as discovering new patterns or associations of diseases that are previously unknown to clinicians and researchers. Within the field of ophthalmology, there is a rapidly expanding pool of large clinical registries, epidemiological studies, omics studies, and biobanks through which big data can be accessed. National corneal transplant registries, genome-wide association studies, national cataract databases, and large ophthalmology-related EHR-based registries (e.g., AAO IRIS Registry) are some of the key resources. In this review, we aim to provide a succinct overview of the availability and clinical applicability of big data in ophthalmology, particularly from the perspective of corneal diseases and cataract, the synergistic potential of big data, AI technologies, internet of things, mHealth, and wearable smart devices, and the potential barriers for realizing the clinical and research potential of big data in this field.
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Affiliation(s)
- Darren S. J. Ting
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom,Birmingham and Midland Eye Centre, Birmingham, United Kingdom,Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, United Kingdom,*Correspondence: Darren S. J. Ting ✉
| | - Rashmi Deshmukh
- Department of Cornea and Refractive Surgery, LV Prasad Eye Institute, Hyderabad, India
| | - Daniel S. W. Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore,Department of Ophthalmology and Visual Sciences, Duke-National University of Singapore (NUS) Medical School, Singapore, Singapore
| | - Marcus Ang
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore,Department of Ophthalmology and Visual Sciences, Duke-National University of Singapore (NUS) Medical School, Singapore, Singapore
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15
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Foo LL, Lim GYS, Lanca C, Wong CW, Hoang QV, Zhang XJ, Yam JC, Schmetterer L, Chia A, Wong TY, Ting DSW, Saw SM, Ang M. Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children. NPJ Digit Med 2023; 6:10. [PMID: 36702878 PMCID: PMC9879938 DOI: 10.1038/s41746-023-00752-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 01/10/2023] [Indexed: 01/27/2023] Open
Abstract
Our study aims to identify children at risk of developing high myopia for timely assessment and intervention, preventing myopia progression and complications in adulthood through the development of a deep learning system (DLS). Using a school-based cohort in Singapore comprising of 998 children (aged 6-12 years old), we train and perform primary validation of the DLS using 7456 baseline fundus images of 1878 eyes; with external validation using an independent test dataset of 821 baseline fundus images of 189 eyes together with clinical data (age, gender, race, parental myopia, and baseline spherical equivalent (SE)). We derive three distinct algorithms - image, clinical and mix (image + clinical) models to predict high myopia development (SE ≤ -6.00 diopter) during teenage years (5 years later, age 11-17). Model performance is evaluated using area under the receiver operating curve (AUC). Our image models (Primary dataset AUC 0.93-0.95; Test dataset 0.91-0.93), clinical models (Primary dataset AUC 0.90-0.97; Test dataset 0.93-0.94) and mixed (image + clinical) models (Primary dataset AUC 0.97; Test dataset 0.97-0.98) achieve clinically acceptable performance. The addition of 1 year SE progression variable has minimal impact on the DLS performance (clinical model AUC 0.98 versus 0.97 in primary dataset, 0.97 versus 0.94 in test dataset; mixed model AUC 0.99 versus 0.97 in primary dataset, 0.95 versus 0.98 in test dataset). Thus, our DLS allows prediction of the development of high myopia by teenage years amongst school-going children. This has potential utility as a clinical-decision support tool to identify "at-risk" children for early intervention.
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Affiliation(s)
- Li Lian Foo
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Gilbert Yong San Lim
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
| | - Carla Lanca
- grid.418858.80000 0000 9084 0599Escola Superior de Tecnologia da Saúde de Lisboa (ESTeSL), Instituto Politécnico de Lisboa, Lisboa, Portugal ,grid.10772.330000000121511713Comprehensive Health Research Center (CHRC), Escola Nacional de Saúde Pública, Universidade Nova de Lisboa, Lisboa, Portugal
| | - Chee Wai Wong
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Duke-NUS Medical School, National University of Singapore, Singapore, Singapore ,grid.415572.00000 0004 0620 9577Asia Pacific Eye Centre, Gleneagles Hospital, Singapore, Singapore
| | - Quan V. Hoang
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Duke-NUS Medical School, National University of Singapore, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore ,grid.21729.3f0000000419368729Dept. of Ophthalmology, Columbia University, Columbia, SC USA
| | - Xiu Juan Zhang
- grid.10784.3a0000 0004 1937 0482Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Jason C. Yam
- grid.10784.3a0000 0004 1937 0482Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China ,grid.490089.c0000 0004 1803 8779Hong Kong Eye Hospital, Hong Kong, China ,grid.415197.f0000 0004 1764 7206Department of Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong, China ,grid.10784.3a0000 0004 1937 0482Hong Kong Hub of Paediatric Excellence, The Chinese University of Hong Kong, Hong Kong, China ,Department of Ophthalmology, Hong Kong Children’s Hospital, Hong Kong, China
| | - Leopold Schmetterer
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Audrey Chia
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Tien Yin Wong
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Daniel S. W. Ting
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Seang-Mei Saw
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Marcus Ang
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
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16
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Chen D, Ran Ran A, Fang Tan T, Ramachandran R, Li F, Cheung CY, Yousefi S, Tham CCY, Ting DSW, Zhang X, Al-Aswad LA. Applications of Artificial Intelligence and Deep Learning in Glaucoma. Asia Pac J Ophthalmol (Phila) 2023; 12:80-93. [PMID: 36706335 DOI: 10.1097/apo.0000000000000596] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/06/2022] [Indexed: 01/28/2023] Open
Abstract
Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence-based tools have the potential to improve and standardize the assessment of glaucoma but development of these algorithms is difficult given the multimodal and variable nature of the diagnosis. Currently, most algorithms are focused on a single imaging modality, specifically screening and diagnosis based on fundus photos or optical coherence tomography images. Use of anterior segment optical coherence tomography and goniophotographs is limited. The majority of algorithms designed for disease progression prediction are based on visual fields. No studies in our literature search assessed the use of artificial intelligence for treatment response prediction and no studies conducted prospective testing of their algorithms. Additional challenges to the development of artificial intelligence-based tools include scarcity of data and a lack of consensus in diagnostic criteria. Although research in the use of artificial intelligence for glaucoma is promising, additional work is needed to develop clinically usable tools.
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Affiliation(s)
- Dinah Chen
- Department of Ophthalmology, NYU Langone Health, New York City, NY
- Genentech Inc, South San Francisco, CA
| | - An Ran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Lam Kin Chung, Jet King-Shing Ho Glaucoma Treatment And Research Centre, The Chinese University of Hong Kong, Hong Kong, China
| | - Ting Fang Tan
- Singapore Eye Research Institute, Singapore
- Singapore National Eye Center, Singapore
| | | | - Fei Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Lam Kin Chung, Jet King-Shing Ho Glaucoma Treatment And Research Centre, The Chinese University of Hong Kong, Hong Kong, China
| | - Siamak Yousefi
- Department of Ophthalmology, The University of Tennessee Health Science Center, Memphis, TN
| | - Clement C Y Tham
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Lam Kin Chung, Jet King-Shing Ho Glaucoma Treatment And Research Centre, The Chinese University of Hong Kong, Hong Kong, China
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore
- Singapore National Eye Center, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
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17
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Teo ZL, Lee AY, Campbell P, Chan RVP, Ting DSW. Developments in Artificial Intelligence for Ophthalmology: Federated Learning. Asia Pac J Ophthalmol (Phila) 2022; 11:500-502. [PMID: 36417673 DOI: 10.1097/apo.0000000000000582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 10/04/2022] [Indexed: 11/24/2022] Open
Affiliation(s)
- Zhen Ling Teo
- Singapore National Eye Centre, Singapore
- Singapore Eye Research Institute, Singapore
| | - Aaron Y Lee
- Department of Ophthalmology, US Roger and Angie Karalis Johnson Retina Center, University of Washington, Seattle, WA
| | - Peter Campbell
- Department of Ophthalmology, Oregon Health and Science University, Portland, OR
| | - R V Paul Chan
- Department of Ophthalmology, University of Illinois Chicago, Chicago, IL
| | - Daniel S W Ting
- Singapore National Eye Centre, Singapore
- Singapore Eye Research Institute, Singapore
- Duke-NUS Medical School, Singapore
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18
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Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, Denniston AK, Faes L, Geerts B, Ibrahim M, Liu X, Mateen BA, Mathur P, McCradden MD, Morgan L, Ordish J, Rogers C, Saria S, Ting DSW, Watkinson P, Weber W, Wheatstone P, McCulloch P. Publisher Correction: Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med 2022; 28:2218. [PMID: 35962208 DOI: 10.1038/s41591-022-01951-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Baptiste Vasey
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK. .,Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK. .,Critical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Myura Nagendran
- UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, UK
| | - Bruce Campbell
- University of Exeter Medical School, Exeter, UK.,Royal Devon and Exeter Hospital, Exeter, UK
| | - David A Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK.,British Heart Foundation Data Science Centre, London, UK.,Health Data Research UK, London, UK.,UCL Hospitals Biomedical Research Centre, London, UK
| | - Alastair K Denniston
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.,Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.,Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Livia Faes
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Bart Geerts
- Healthplus.ai-R&D BV, Amsterdam, The Netherlands
| | - Mudathir Ibrahim
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.,Department of Surgery, Maimonides Medical Center, Brooklyn, NY, USA
| | - Xiaoxuan Liu
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.,Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Bilal A Mateen
- Institute of Health Informatics, University College London, London, UK.,The Wellcome Trust, London, UK.,The Alan Turing Institute, London, UK
| | - Piyush Mathur
- Department of General Anesthesiology, Anesthesiology Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Melissa D McCradden
- The Hospital for Sick Children, Toronto ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto ON, Canada
| | | | - Johan Ordish
- Medicines and Healthcare products Regulatory Agency, London, UK
| | | | - Suchi Saria
- Departments of Computer Science, Statistics, and Health Policy, and Division of Informatics, Johns Hopkins University, Baltimore, MD, USA.,Bayesian Health, New York, NY, USA
| | - Daniel S W Ting
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore.,Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Peter Watkinson
- Critical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,NIHR Biomedical Research Centre Oxford, Oxford University Hospitals NHS Trust, Oxford, UK
| | | | | | - Peter McCulloch
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
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19
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Sreejith Kumar AJ, Chong RS, Crowston JG, Chua J, Bujor I, Husain R, Vithana EN, Girard MJA, Ting DSW, Cheng CY, Aung T, Popa-Cherecheanu A, Schmetterer L, Wong D. Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma. JAMA Ophthalmol 2022; 140:974-981. [PMID: 36048435 PMCID: PMC9437828 DOI: 10.1001/jamaophthalmol.2022.3375] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Importance Deep learning (DL) networks require large data sets for training, which can be challenging to collect clinically. Generative models could be used to generate large numbers of synthetic optical coherence tomography (OCT) images to train such DL networks for glaucoma detection. Objective To assess whether generative models can synthesize circumpapillary optic nerve head OCT images of normal and glaucomatous eyes and determine the usability of synthetic images for training DL models for glaucoma detection. Design, Setting, and Participants Progressively growing generative adversarial network models were trained to generate circumpapillary OCT scans. Image gradeability and authenticity were evaluated on a clinical set of 100 real and 100 synthetic images by 2 clinical experts. DL networks for glaucoma detection were trained with real or synthetic images and evaluated on independent internal and external test data sets of 140 and 300 real images, respectively. Main Outcomes and Measures Evaluations of the clinical set between the experts were compared. Glaucoma detection performance of the DL networks was assessed using area under the curve (AUC) analysis. Class activation maps provided visualizations of the regions contributing to the respective classifications. Results A total of 990 normal and 862 glaucomatous eyes were analyzed. Evaluations of the clinical set were similar for gradeability (expert 1: 92.0%; expert 2: 93.0%) and authenticity (expert 1: 51.8%; expert 2: 51.3%). The best-performing DL network trained on synthetic images had AUC scores of 0.97 (95% CI, 0.95-0.99) on the internal test data set and 0.90 (95% CI, 0.87-0.93) on the external test data set, compared with AUCs of 0.96 (95% CI, 0.94-0.99) on the internal test data set and 0.84 (95% CI, 0.80-0.87) on the external test data set for the network trained with real images. An increase in the AUC for the synthetic DL network was observed with the use of larger synthetic data set sizes. Class activation maps showed that the regions of the synthetic images contributing to glaucoma detection were generally similar to that of real images. Conclusions and Relevance DL networks trained with synthetic OCT images for glaucoma detection were comparable with networks trained with real images. These results suggest potential use of generative models in the training of DL networks and as a means of data sharing across institutions without patient information confidentiality issues.
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Affiliation(s)
- Ashish Jith Sreejith Kumar
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,SERI-NTU Advanced Ocular Engineering (STANCE), Singapore, Singapore.,Institute for Infocomm Research, A*STAR, Singapore
| | - Rachel S Chong
- Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Jonathan G Crowston
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Jacqueline Chua
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Inna Bujor
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Rahat Husain
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Eranga N Vithana
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Michaël J A Girard
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore.,Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore.,Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Alina Popa-Cherecheanu
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania.,Emergency University Hospital, Department of Ophthalmology, Bucharest, Romania
| | - Leopold Schmetterer
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore.,SERI-NTU Advanced Ocular Engineering (STANCE), Singapore, Singapore.,Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland.,Department of Ophthalmology and Optometry, Medical University Vienna, Vienna, Austria.,School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore.,Department of Clinical Pharmacology, Medical University Vienna, Vienna, Austria.,Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria
| | - Damon Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,SERI-NTU Advanced Ocular Engineering (STANCE), Singapore, Singapore.,School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore
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20
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Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, Denniston AK, Faes L, Geerts B, Ibrahim M, Liu X, Mateen BA, Mathur P, McCradden MD, Morgan L, Ordish J, Rogers C, Saria S, Ting DSW, Watkinson P, Weber W, Wheatstone P, McCulloch P. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ 2022; 377:e070904. [PMID: 35584845 PMCID: PMC9116198 DOI: 10.1136/bmj-2022-070904] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/26/2022] [Indexed: 01/04/2023]
Affiliation(s)
- Baptiste Vasey
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
- Critical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Myura Nagendran
- UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, UK
| | - Bruce Campbell
- University of Exeter Medical School, Exeter, UK
- Royal Devon and Exeter Hospital, Exeter, UK
| | - David A Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- British Heart Foundation Data Science Centre, London, UK
- Health Data Research UK, London, UK
- UCL Hospitals Biomedical Research Centre, London, UK
| | - Alastair K Denniston
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Livia Faes
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | | | - Mudathir Ibrahim
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Department of Surgery, Maimonides Medical Center, New York, NY, USA
| | - Xiaoxuan Liu
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Bilal A Mateen
- Institute of Health Informatics, University College London, London, UK
- Wellcome Trust, London, UK
- Alan Turing Institute, London, UK
| | - Piyush Mathur
- Department of General Anesthesiology, Anesthesiology Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Melissa D McCradden
- Hospital for Sick Children, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | | | - Johan Ordish
- The Medicines and Healthcare products Regulatory Agency, London, UK
| | | | - Suchi Saria
- Departments of Computer Science, Statistics, and Health Policy, and Division of Informatics, Johns Hopkins University, Baltimore, MD, USA
- Bayesian Health, New York, NY, USA
| | - Daniel S W Ting
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Peter Watkinson
- Critical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- NIHR Biomedical Research Centre Oxford, Oxford University Hospitals NHS Trust, Oxford, UK
| | | | | | - Peter McCulloch
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
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21
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Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, Denniston AK, Faes L, Geerts B, Ibrahim M, Liu X, Mateen BA, Mathur P, McCradden MD, Morgan L, Ordish J, Rogers C, Saria S, Ting DSW, Watkinson P, Weber W, Wheatstone P, McCulloch P. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med 2022; 28:924-933. [PMID: 35585198 DOI: 10.1038/s41591-022-01772-9] [Citation(s) in RCA: 99] [Impact Index Per Article: 49.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 03/03/2022] [Indexed: 12/31/2022]
Abstract
A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system's actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings.
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Affiliation(s)
- Baptiste Vasey
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
- Critical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Myura Nagendran
- UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, UK
| | - Bruce Campbell
- University of Exeter Medical School, Exeter, UK
- Royal Devon and Exeter Hospital, Exeter, UK
| | - David A Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- British Heart Foundation Data Science Centre, London, UK
- Health Data Research UK, London, UK
- UCL Hospitals Biomedical Research Centre, London, UK
| | - Alastair K Denniston
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Livia Faes
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Bart Geerts
- Healthplus.ai-R&D BV, Amsterdam, The Netherlands
| | - Mudathir Ibrahim
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Department of Surgery, Maimonides Medical Center, Brooklyn, NY, USA
| | - Xiaoxuan Liu
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Bilal A Mateen
- Institute of Health Informatics, University College London, London, UK
- The Wellcome Trust, London, UK
- The Alan Turing Institute, London, UK
| | - Piyush Mathur
- Department of General Anesthesiology, Anesthesiology Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Melissa D McCradden
- The Hospital for Sick Children, Toronto ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto ON, Canada
| | | | - Johan Ordish
- Medicines and Healthcare products Regulatory Agency, London, UK
| | | | - Suchi Saria
- Departments of Computer Science, Statistics, and Health Policy, and Division of Informatics, Johns Hopkins University, Baltimore, MD, USA
- Bayesian Health, New York, NY, USA
| | - Daniel S W Ting
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Peter Watkinson
- Critical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- NIHR Biomedical Research Centre Oxford, Oxford University Hospitals NHS Trust, Oxford, UK
| | | | | | - Peter McCulloch
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
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Abstract
ABSTRACT Accessibility to the Internet and computer systems has prompted the gravitation towards digital learning in medicine, including ophthalmology. Using the PubMed database and Google search engine, current initiatives in ophthalmology that serve as alternatives to traditional in-person learning with the purpose of enhancing clinical and surgical training were reviewed. This includes the development of teleeducation modules, construction of libraries of clinical and surgical videos, conduction of didactics via video communication, and the implementation of simulators and intelligent tutoring systems into clinical and surgical training programs. In this age of digital communication, teleophthalmology programs, virtual ophthalmological society meetings, and online examinations have become necessary for conducting clinical work and educational training in ophthalmology, especially in light of recent global events that have prevented large gatherings as well as the rural location of various populations. Looking forward, web-based modules and resources, artificial intelligence-based systems, and telemedicine programs will augment current curricula for ophthalmology trainees.
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Affiliation(s)
- Tala Al-Khaled
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, US
| | - Luis Acaba-Berrocal
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, US
| | - Emily Cole
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, US
| | - Daniel S W Ting
- Singapore Eye Research institute, Singapore National Eye centre, Singapore
- Duke-NUS Medical School, National University Singapore, Singapore
| | - Michael F Chiang
- National Eye Institute, National Institutes of Health, Bethesda, MD, US
| | - R V Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, US
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23
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Lim JS, Hong M, Lam WST, Zhang Z, Teo ZL, Liu Y, Ng WY, Foo LL, Ting DSW. Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology. Curr Opin Ophthalmol 2022; 33:174-187. [PMID: 35266894 DOI: 10.1097/icu.0000000000000846] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW The application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each. RECENT FINDINGS Several key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks. SUMMARY AI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.
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Affiliation(s)
- Jane S Lim
- Singapore National Eye Centre, Singapore Eye Research Institute
| | | | - Walter S T Lam
- Yong Loo Lin School of Medicine, National University of Singapore
| | - Zheting Zhang
- Lee Kong Chian School of Medicine, Nanyang Technological University
| | - Zhen Ling Teo
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Yong Liu
- National University of Singapore, DukeNUS Medical School, Singapore
| | - Wei Yan Ng
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Li Lian Foo
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Daniel S W Ting
- Singapore National Eye Centre, Singapore Eye Research Institute
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24
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Ting DSJ, Deshmukh R, Ting DSW, Ang M. Corneal Disorders. Ophthalmic Epidemiol 2022. [DOI: 10.1201/9781315146737-10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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25
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Li Y, Foo LL, Wong CW, Li J, Hoang QV, Schmetterer L, Ting DSW, Ang M. Pathologic myopia: advances in imaging and the potential role of artificial intelligence. Br J Ophthalmol 2022; 107:600-606. [PMID: 35288438 DOI: 10.1136/bjophthalmol-2021-320926] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 02/17/2022] [Indexed: 11/04/2022]
Abstract
Pathologic myopia is a severe form of myopia that can lead to permanent visual impairment. The recent global increase in the prevalence of myopia has been projected to lead to a higher incidence of pathologic myopia in the future. Thus, imaging myopic eyes to detect early pathological changes, or predict myopia progression to allow for early intervention, has become a key priority. Recent advances in optical coherence tomography (OCT) have contributed to the new grading system for myopic maculopathy and myopic traction maculopathy, which may improve phenotyping and thus, clinical management. Widefield fundus and OCT imaging has improved the detection of posterior staphyloma. Non-invasive OCT angiography has enabled depth-resolved imaging for myopic choroidal neovascularisation. Artificial intelligence (AI) has shown great performance in detecting pathologic myopia and the identification of myopia-associated complications. These advances in imaging with adjunctive AI analysis may lead to improvements in monitoring disease progression or guiding treatments. In this review, we provide an update on the classification of pathologic myopia, how imaging has improved clinical evaluation and management of myopia-associated complications, and the recent development of AI algorithms to aid the detection and classification of pathologic myopia.
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Affiliation(s)
- Yong Li
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore
| | - Li-Lian Foo
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore
| | - Chee Wai Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore
| | - Jonathan Li
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Quan V Hoang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Department of Ophthalmology, Columbia University, New York City, New York, USA
| | - Leopold Schmetterer
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore.,SERI-NTU Advanced Ocular Engineering (STANCE), Singapore.,School of Chemical and Biological Engineering, Nanyang Technological University, Singapore.,Department of Clinical Pharmacology, Medical University Vienna, Vienna, Austria.,Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria.,Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore
| | - Marcus Ang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore .,Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore
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26
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Gunasekeran DV, Zheng F, Lim GYS, Chong CCY, Zhang S, Ng WY, Keel S, Xiang Y, Park KH, Park SJ, Chandra A, Wu L, Campbel JP, Lee AY, Keane PA, Denniston A, Lam DSC, Fung AT, Chan PRV, Sadda SR, Loewenstein A, Grzybowski A, Fong KCS, Wu WC, Bachmann LM, Zhang X, Yam JC, Cheung CY, Pongsachareonnont P, Ruamviboonsuk P, Raman R, Sakamoto T, Habash R, Girard M, Milea D, Ang M, Tan GSW, Schmetterer L, Cheng CY, Lamoureux E, Lin H, van Wijngaarden P, Wong TY, Ting DSW. Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective. Front Med (Lausanne) 2022; 9:875242. [PMID: 36314006 PMCID: PMC9612721 DOI: 10.3389/fmed.2022.875242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 03/29/2022] [Indexed: 11/13/2022] Open
Abstract
Background Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract. Methods This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. Results One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10-12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63-0.83. Conclusion Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.
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Affiliation(s)
- Dinesh V Gunasekeran
- Singapore Eye Research Institute (SERI), Singapore National Eye Center (SNEC), Singapore, Singapore.,School of Medicine, National University of Singapore (NUS), Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Feihui Zheng
- Singapore Eye Research Institute (SERI), Singapore National Eye Center (SNEC), Singapore, Singapore
| | - Gilbert Y S Lim
- Singapore Eye Research Institute (SERI), Singapore National Eye Center (SNEC), Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Crystal C Y Chong
- Singapore Eye Research Institute (SERI), Singapore National Eye Center (SNEC), Singapore, Singapore
| | - Shihao Zhang
- Singapore Eye Research Institute (SERI), Singapore National Eye Center (SNEC), Singapore, Singapore
| | - Wei Yan Ng
- Singapore Eye Research Institute (SERI), Singapore National Eye Center (SNEC), Singapore, Singapore
| | - Stuart Keel
- Department of Ophthalmology, University of Melbourne, Melbourne, VIC, Australia
| | - Yifan Xiang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center (ZOC), Sun Yat-sen University, Guangzhou, China
| | - Ki Ho Park
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul, South Korea
| | - Sang Jun Park
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul, South Korea.,Department of Ophthalmology, Seoul National University Bundang Hospital, Seongnam-si, South Korea
| | - Aman Chandra
- Department of Ophthalmology, Southend University Hospital, Southend-on-Sea, United Kingdom
| | - Lihteh Wu
- Asociados de Macula, Vitreo y Retina de Costa Rica, San José, Costa Rica
| | - J Peter Campbel
- Casey Eye Institute, Oregon Health and Science, Portland, OR, United States
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, United States
| | | | - Alastair Denniston
- Department of Ophthalmology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom.,Institute of Ophthalmology, University College London (UCL), London, United Kingdom
| | - Dennis S C Lam
- International Eye Research Institute of the Chinese University of Hong Kong (Shenzhen), Shenzhen, China.,C-MER International Eye Research Center of the Chinese University of Hong Kong (Shenzhen), Shenzhen, China
| | - Adrian T Fung
- Specialty of Clinical Ophthalmology and Eye Health, Faculty of Medicine and Health, Westmead Clinical School, The University of Sydney, Sydney, NSW, Australia.,Department of Ophthalmology, Faculty of Medicine, Health and Human Sciences, Macquarie University Hospital, Sydney, NSW, Australia
| | - Paul R V Chan
- Department of Ophthalmology, University of Illinois College of Medicine, Chicago, IL, United States
| | - SriniVas R Sadda
- Department of Ophthalmology, Doheny Eye Institute, Los Angeles, CA, United States
| | - Anat Loewenstein
- Department of Ophthalmology, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Andrzej Grzybowski
- Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland.,Institute for Research in Ophthalmology, Ponzan, Poland
| | | | - Wei-Chi Wu
- Department of Ophthalmology, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | | | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center (ZOC), Sun Yat-sen University, Guangzhou, China
| | - Jason C Yam
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong (CUHK), Hong Kong, Hong Kong SAR, China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong (CUHK), Hong Kong, Hong Kong SAR, China
| | - Pear Pongsachareonnont
- Vitreoretinal Research Unit, Department of Ophthalmology, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Paisan Ruamviboonsuk
- Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
| | - Rajiv Raman
- Vitreo-Retinal Department, Sankara Nethralaya, Chennai, India
| | - Taiji Sakamoto
- Department of Ophthalmology, Kagoshima University, Kagoshima, Japan
| | - Ranya Habash
- Bascom Palmar Eye Institute, Miami, FL, United States
| | - Michael Girard
- Singapore Eye Research Institute (SERI), Singapore National Eye Center (SNEC), Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Dan Milea
- Singapore Eye Research Institute (SERI), Singapore National Eye Center (SNEC), Singapore, Singapore.,Copenhagen University Hospital, Copenhagen, Denmark
| | - Marcus Ang
- Singapore Eye Research Institute (SERI), Singapore National Eye Center (SNEC), Singapore, Singapore.,School of Medicine, National University of Singapore (NUS), Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Gavin S W Tan
- Singapore Eye Research Institute (SERI), Singapore National Eye Center (SNEC), Singapore, Singapore.,School of Medicine, National University of Singapore (NUS), Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Leopold Schmetterer
- Singapore Eye Research Institute (SERI), Singapore National Eye Center (SNEC), Singapore, Singapore.,School of Medicine, National University of Singapore (NUS), Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute (SERI), Singapore National Eye Center (SNEC), Singapore, Singapore.,School of Medicine, National University of Singapore (NUS), Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Ecosse Lamoureux
- Singapore Eye Research Institute (SERI), Singapore National Eye Center (SNEC), Singapore, Singapore.,School of Medicine, National University of Singapore (NUS), Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center (ZOC), Sun Yat-sen University, Guangzhou, China
| | | | - Tien Y Wong
- Singapore Eye Research Institute (SERI), Singapore National Eye Center (SNEC), Singapore, Singapore.,School of Medicine, National University of Singapore (NUS), Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore.,Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Daniel S W Ting
- Singapore Eye Research Institute (SERI), Singapore National Eye Center (SNEC), Singapore, Singapore.,School of Medicine, National University of Singapore (NUS), Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
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27
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Aung YYM, Wong DCS, Ting DSW. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br Med Bull 2021; 139:4-15. [PMID: 34405854 DOI: 10.1093/bmb/ldab016] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/15/2021] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Artificial intelligence (AI) and machine learning (ML) are rapidly evolving fields in various sectors, including healthcare. This article reviews AI's present applications in healthcare, including its benefits, limitations and future scope. SOURCES OF DATA A review of the English literature was conducted with search terms 'AI' or 'ML' or 'deep learning' and 'healthcare' or 'medicine' using PubMED and Google Scholar from 2000-2021. AREAS OF AGREEMENT AI could transform physician workflow and patient care through its applications, from assisting physicians and replacing administrative tasks to augmenting medical knowledge. AREAS OF CONTROVERSY From challenges training ML systems to unclear accountability, AI's implementation is difficult and incremental at best. Physicians also lack understanding of what AI implementation could represent. GROWING POINTS AI can ultimately prove beneficial in healthcare, but requires meticulous governance similar to the governance of physician conduct. AREAS TIMELY FOR DEVELOPING RESEARCH Regulatory guidelines are needed on how to safely implement and assess AI technology, alongside further research into the specific capabilities and limitations of its medical use.
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Affiliation(s)
- Yuri Y M Aung
- Imperial College School of Medicine, Imperial College London, SW7 2AZ, UK
| | - David C S Wong
- University of Cambridge, School of Clinical Medicine, CB2 0SP, UK
| | - Daniel S W Ting
- Duke-NUS Medical School, Singapore National Eye Centre, 168751, Singapore
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28
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Liu TYA, Wei J, Zhu H, Subramanian PS, Myung D, Yi PH, Hui FK, Unberath M, Ting DSW, Miller NR. Detection of Optic Disc Abnormalities in Color Fundus Photographs Using Deep Learning. J Neuroophthalmol 2021; 41:368-374. [PMID: 34415271 PMCID: PMC10637344 DOI: 10.1097/wno.0000000000001358] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND To date, deep learning-based detection of optic disc abnormalities in color fundus photographs has mostly been limited to the field of glaucoma. However, many life-threatening systemic and neurological conditions can manifest as optic disc abnormalities. In this study, we aimed to extend the application of deep learning (DL) in optic disc analyses to detect a spectrum of nonglaucomatous optic neuropathies. METHODS Using transfer learning, we trained a ResNet-152 deep convolutional neural network (DCNN) to distinguish between normal and abnormal optic discs in color fundus photographs (CFPs). Our training data set included 944 deidentified CFPs (abnormal 364; normal 580). Our testing data set included 151 deidentified CFPs (abnormal 71; normal 80). Both the training and testing data sets contained a wide range of optic disc abnormalities, including but not limited to ischemic optic neuropathy, atrophy, compressive optic neuropathy, hereditary optic neuropathy, hypoplasia, papilledema, and toxic optic neuropathy. The standard measures of performance (sensitivity, specificity, and area under the curve of the receiver operating characteristic curve (AUC-ROC)) were used for evaluation. RESULTS During the 10-fold cross-validation test, our DCNN for distinguishing between normal and abnormal optic discs achieved the following mean performance: AUC-ROC 0.99 (95 CI: 0.98-0.99), sensitivity 94% (95 CI: 91%-97%), and specificity 96% (95 CI: 93%-99%). When evaluated against the external testing data set, our model achieved the following mean performance: AUC-ROC 0.87, sensitivity 90%, and specificity 69%. CONCLUSION In summary, we have developed a deep learning algorithm that is capable of detecting a spectrum of optic disc abnormalities in color fundus photographs, with a focus on neuro-ophthalmological etiologies. As the next step, we plan to validate our algorithm prospectively as a focused screening tool in the emergency department, which if successful could be beneficial because current practice pattern and training predict a shortage of neuro-ophthalmologists and ophthalmologists in general in the near future.
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Affiliation(s)
- T Y Alvin Liu
- Department of Ophthalmology (TYAL, NRM), Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland; Department of Biomedical Engineering (JW), Johns Hopkins University, Baltimore, Maryland; Malone Center for Engineering in Healthcare (HZ, MU), Johns Hopkins University, Baltimore, Maryland; Department of Radiology (PHY, FKH), Johns Hopkins University, Baltimore, Maryland; Singapore Eye Research Institute (DSWT), Singapore National Eye Center, Duke-NUS Medical School, National University of Singapore, Singapore ; Department of Ophthalmology (PSS), University of Colorado School of Medicine, Aurora, Colorado; and Department of Ophthalmology (DM), Byers Eye Institute, Stanford University, Palo Alto, California
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Tang F, Wang X, Ran AR, Chan CKM, Ho M, Yip W, Young AL, Lok J, Szeto S, Chan J, Yip F, Wong R, Tang Z, Yang D, Ng DS, Chen LJ, Brelén M, Chu V, Li K, Lai THT, Tan GS, Ting DSW, Huang H, Chen H, Ma JH, Tang S, Leng T, Kakavand S, Mannil SS, Chang RT, Liew G, Gopinath B, Lai TYY, Pang CP, Scanlon PH, Wong TY, Tham CC, Chen H, Heng PA, Cheung CY. A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis. Diabetes Care 2021; 44:2078-2088. [PMID: 34315698 PMCID: PMC8740924 DOI: 10.2337/dc20-3064] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 05/29/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Diabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep learning (DL) system for classifying DME using images from three common commercially available optical coherence tomography (OCT) devices. RESEARCH DESIGN AND METHODS We trained and validated two versions of a multitask convolution neural network (CNN) to classify DME (center-involved DME [CI-DME], non-CI-DME, or absence of DME) using three-dimensional (3D) volume scans and 2D B-scans, respectively. For both 3D and 2D CNNs, we used the residual network (ResNet) as the backbone. For the 3D CNN, we used a 3D version of ResNet-34 with the last fully connected layer removed as the feature extraction module. A total of 73,746 OCT images were used for training and primary validation. External testing was performed using 26,981 images across seven independent data sets from Singapore, Hong Kong, the U.S., China, and Australia. RESULTS In classifying the presence or absence of DME, the DL system achieved area under the receiver operating characteristic curves (AUROCs) of 0.937 (95% CI 0.920-0.954), 0.958 (0.930-0.977), and 0.965 (0.948-0.977) for the primary data set obtained from CIRRUS, SPECTRALIS, and Triton OCTs, respectively, in addition to AUROCs >0.906 for the external data sets. For further classification of the CI-DME and non-CI-DME subgroups, the AUROCs were 0.968 (0.940-0.995), 0.951 (0.898-0.982), and 0.975 (0.947-0.991) for the primary data set and >0.894 for the external data sets. CONCLUSIONS We demonstrated excellent performance with a DL system for the automated classification of DME, highlighting its potential as a promising second-line screening tool for patients with DM, which may potentially create a more effective triaging mechanism to eye clinics.
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Affiliation(s)
- Fangyao Tang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR
| | - Xi Wang
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR
| | - An-Ran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR
| | | | - Mary Ho
- Department of Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR.,Alice Ho Miu Ling Nethersole Hospital, Hong Kong SAR
| | - Wilson Yip
- Department of Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR.,Alice Ho Miu Ling Nethersole Hospital, Hong Kong SAR
| | - Alvin L Young
- Department of Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR.,Alice Ho Miu Ling Nethersole Hospital, Hong Kong SAR
| | - Jerry Lok
- Hong Kong Eye Hospital, Hong Kong SAR
| | | | | | - Fanny Yip
- Hong Kong Eye Hospital, Hong Kong SAR
| | | | - Ziqi Tang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR
| | - Dawei Yang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR
| | - Danny S Ng
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR.,Hong Kong Eye Hospital, Hong Kong SAR
| | - Li Jia Chen
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR.,Department of Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR
| | - Marten Brelén
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR
| | - Victor Chu
- United Christian Hospital, Hong Kong SAR
| | - Kenneth Li
- United Christian Hospital, Hong Kong SAR
| | | | - Gavin S Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Haifan Huang
- Joint Shantou International Eye Center, Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong, China
| | - Haoyu Chen
- Joint Shantou International Eye Center, Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong, China
| | - Jacey Hongjie Ma
- Aier School of Ophthalmology, Central South University, Changsha, Hunan, China
| | - Shibo Tang
- Aier School of Ophthalmology, Central South University, Changsha, Hunan, China
| | - Theodore Leng
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, CA
| | - Schahrouz Kakavand
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, CA
| | - Suria S Mannil
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, CA
| | - Robert T Chang
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, CA
| | - Gerald Liew
- Department of Ophthalmology, Westmead Institute for Medical Research, University of Sydney, Sydney, NSW, Australia
| | - Bamini Gopinath
- Department of Ophthalmology, Westmead Institute for Medical Research, University of Sydney, Sydney, NSW, Australia.,Macquarie University Hearing, Department of Linguistics, Macquarie University, Sydney, New South Wales, Australia
| | - Timothy Y Y Lai
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR
| | - Chi Pui Pang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR
| | - Peter H Scanlon
- Gloucestershire Retinal Research Group, Gloucestershire Hospitals NHS Foundation Trust, Gloucester, U.K
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Clement C Tham
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR.,Hong Kong Eye Hospital, Hong Kong SAR.,Department of Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR
| | - Hao Chen
- Department of Computer Science and Engineering, The Hong Kong University of Sciences and Technology, Hong Kong SAR
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR
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30
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Lin D, Xiong J, Liu C, Zhao L, Li Z, Yu S, Wu X, Ge Z, Hu X, Wang B, Fu M, Zhao X, Wang X, Zhu Y, Chen C, Li T, Li Y, Wei W, Zhao M, Li J, Xu F, Ding L, Tan G, Xiang Y, Hu Y, Zhang P, Han Y, Li JPO, Wei L, Zhu P, Liu Y, Chen W, Ting DSW, Wong TY, Chen Y, Lin H. Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study. Lancet Digit Health 2021; 3:e486-e495. [PMID: 34325853 DOI: 10.1016/s2589-7500(21)00086-8] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 04/21/2021] [Accepted: 05/07/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND Medical artificial intelligence (AI) has entered the clinical implementation phase, although real-world performance of deep-learning systems (DLSs) for screening fundus disease remains unsatisfactory. Our study aimed to train a clinically applicable DLS for fundus diseases using data derived from the real world, and externally test the model using fundus photographs collected prospectively from the settings in which the model would most likely be adopted. METHODS In this national real-world evidence study, we trained a DLS, the Comprehensive AI Retinal Expert (CARE) system, to identify the 14 most common retinal abnormalities using 207 228 colour fundus photographs derived from 16 clinical settings with different disease distributions. CARE was internally validated using 21 867 photographs and externally tested using 18 136 photographs prospectively collected from 35 real-world settings across China where CARE might be adopted, including eight tertiary hospitals, six community hospitals, and 21 physical examination centres. The performance of CARE was further compared with that of 16 ophthalmologists and tested using datasets with non-Chinese ethnicities and previously unused camera types. This study was registered with ClinicalTrials.gov, NCT04213430, and is currently closed. FINDINGS The area under the receiver operating characteristic curve (AUC) in the internal validation set was 0·955 (SD 0·046). AUC values in the external test set were 0·965 (0·035) in tertiary hospitals, 0·983 (0·031) in community hospitals, and 0·953 (0·042) in physical examination centres. The performance of CARE was similar to that of ophthalmologists. Large variations in sensitivity were observed among the ophthalmologists in different regions and with varying experience. The system retained strong identification performance when tested using the non-Chinese dataset (AUC 0·960, 95% CI 0·957-0·964 in referable diabetic retinopathy). INTERPRETATION Our DLS (CARE) showed satisfactory performance for screening multiple retinal abnormalities in real-world settings using prospectively collected fundus photographs, and so could allow the system to be implemented and adopted for clinical care. FUNDING This study was funded by the National Key R&D Programme of China, the Science and Technology Planning Projects of Guangdong Province, the National Natural Science Foundation of China, the Natural Science Foundation of Guangdong Province, and the Fundamental Research Funds for the Central Universities. TRANSLATION For the Chinese translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Duoru Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jianhao Xiong
- Beijing Eaglevision Technology Development, Beijing, China
| | - Congxin Liu
- Beijing Eaglevision Technology Development, Beijing, China
| | - Lanqin Zhao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zhongwen Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Shanshan Yu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xiaohang Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zongyuan Ge
- Department of Electrical and Computer Systems Engineering, Faculty of Engineering, Monash University, Melbourne, VIC, Australia
| | - Xinyue Hu
- Beijing Eaglevision Technology Development, Beijing, China
| | - Bin Wang
- Beijing Eaglevision Technology Development, Beijing, China
| | - Meng Fu
- Beijing Eaglevision Technology Development, Beijing, China
| | - Xin Zhao
- Beijing Eaglevision Technology Development, Beijing, China
| | - Xin Wang
- Centre for Precision Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yi Zhu
- Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Chuan Chen
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Tao Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yonghao Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Wenbin Wei
- Beijing Tongren Eye Centre, Beijing Key Laboratory of Intraocular Tumour Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Mingwei Zhao
- Department of Ophthalmology, Ophthalmology and Optometry Centre, Peking University People's Hospital, Beijing, China
| | - Jianqiao Li
- Department of Ophthalmology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Fan Xu
- Department of Ophthalmology, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Lin Ding
- Department of Ophthalmology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Shanxi, China
| | - Gang Tan
- Department of Ophthalmology, University of South China, Hengyang, Hunan, China
| | - Yi Xiang
- Department of Ophthalmology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yongcheng Hu
- Bayannur Paralympic Eye Hospital, Bayannur, Inner Mongolia, China
| | - Ping Zhang
- Bayannur Paralympic Eye Hospital, Bayannur, Inner Mongolia, China
| | - Yu Han
- Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China
| | | | - Lai Wei
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Pengzhi Zhu
- Guangdong Medical Devices Quality Surveillance and Test Institute, Guangzhou, Guangdong, China
| | - Yizhi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Weirong Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Daniel S W Ting
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Tien Y Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Yuzhong Chen
- Beijing Eaglevision Technology Development, Beijing, China.
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China; Centre for Precision Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
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31
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Ting DSW, Wong TY, Park KH, Cheung CY, Tham CC, Lam DSC. Ocular Imaging Standardization for Artificial Intelligence Applications in Ophthalmology: the Joint Position Statement and Recommendations From the Asia-Pacific Academy of Ophthalmology and the Asia-Pacific Ocular Imaging Society. Asia Pac J Ophthalmol (Phila) 2021; 10:348-349. [PMID: 34415245 DOI: 10.1097/apo.0000000000000421] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Affiliation(s)
- Daniel S W Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
- Duke-NUS Medical School, National University Singapore, Singapore
| | - Tien Y Wong
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
- Duke-NUS Medical School, National University Singapore, Singapore
| | | | - Carol Y Cheung
- The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Clement C Tham
- The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Dennis S C Lam
- C-MER International Eye Care Group Limited, Hong Kong SAR, China
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32
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Rampat R, Deshmukh R, Chen X, Ting DSW, Said DG, Dua HS, Ting DSJ. Artificial Intelligence in Cornea, Refractive Surgery, and Cataract: Basic Principles, Clinical Applications, and Future Directions. Asia Pac J Ophthalmol (Phila) 2021; 10:268-281. [PMID: 34224467 PMCID: PMC7611495 DOI: 10.1097/apo.0000000000000394] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
ABSTRACT Corneal diseases, uncorrected refractive errors, and cataract represent the major causes of blindness globally. The number of refractive surgeries, either cornea- or lens-based, is also on the rise as the demand for perfect vision continues to increase. With the recent advancement and potential promises of artificial intelligence (AI) technologies demonstrated in the realm of ophthalmology, particularly retinal diseases and glaucoma, AI researchers and clinicians are now channeling their focus toward the less explored ophthalmic areas related to the anterior segment of the eye. Conditions that rely on anterior segment imaging modalities, including slit-lamp photography, anterior segment optical coherence tomography, corneal tomography, in vivo confocal microscopy and/or optical biometers, are the most commonly explored areas. These include infectious keratitis, keratoconus, corneal grafts, ocular surface pathologies, preoperative screening before refractive surgery, intraocular lens calculation, and automated refraction, among others. In this review, we aimed to provide a comprehensive update on the utilization of AI in anterior segment diseases, with particular emphasis on the recent advancement in the past few years. In addition, we demystify some of the basic principles and terminologies related to AI, particularly machine learning and deep learning, to help improve the understanding, research and clinical implementation of these AI technologies among the ophthalmologists and vision scientists. As we march toward the era of digital health, guidelines such as CONSORT-AI, SPIRIT-AI, and STARD-AI will play crucial roles in guiding and standardizing the conduct and reporting of AI-related trials, ultimately promoting their potential for clinical translation.
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Affiliation(s)
| | - Rashmi Deshmukh
- Department of Ophthalmology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Xin Chen
- School of Computer Science, University of Nottingham, Nottingham, UK
| | - Daniel S. W. Ting
- Duke-NUS Medical School, National University of Singapore, Singapore
- Singapore National Eye Centre / Singapore Eye Research Institute, Singapore
| | - Dalia G. Said
- Academic Ophthalmology, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Ophthalmology, Queen’s Medical Centre, Nottingham, UK
| | - Harminder S. Dua
- Academic Ophthalmology, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Ophthalmology, Queen’s Medical Centre, Nottingham, UK
| | - Darren S. J. Ting
- Singapore National Eye Centre / Singapore Eye Research Institute, Singapore
- Academic Ophthalmology, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Ophthalmology, Queen’s Medical Centre, Nottingham, UK
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33
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Ng WY, Tan TE, Xiao Z, Movva PVH, Foo FSS, Yun D, Chen W, Wong TY, Lin HT, Ting DSW. Blockchain Technology for Ophthalmology: Coming of Age? Asia Pac J Ophthalmol (Phila) 2021; 10:343-347. [PMID: 34415244 DOI: 10.1097/apo.0000000000000399] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Affiliation(s)
- Wei Yan Ng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-National University of Singapore Medical School, Singapore
| | - Tien-En Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-National University of Singapore Medical School, Singapore
| | - Zhe Xiao
- Institute of High Performance Computing, Agency for Science, Technology and Research (A∗STAR), Singapore
| | - Prasanth V H Movva
- Certis Commercial and Industrial Security Corporation Security Private Limited, Singapore
| | - Fuji S S Foo
- Certis Commercial and Industrial Security Corporation Security Private Limited, Singapore
| | - Dongyuan Yun
- Zhongshan Ophthalmic Center, Sun Yat-Sen University, People's Republic of China
| | - Wenben Chen
- Zhongshan Ophthalmic Center, Sun Yat-Sen University, People's Republic of China
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-National University of Singapore Medical School, Singapore
| | - Hao Tian Lin
- Zhongshan Ophthalmic Center, Sun Yat-Sen University, People's Republic of China
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-National University of Singapore Medical School, Singapore
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Valikodath NG, Cole E, Ting DSW, Campbell JP, Pasquale LR, Chiang MF, Chan RVP. Impact of Artificial Intelligence on Medical Education in Ophthalmology. Transl Vis Sci Technol 2021; 10:14. [PMID: 34125146 PMCID: PMC8212436 DOI: 10.1167/tvst.10.7.14] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Clinical care in ophthalmology is rapidly evolving as artificial intelligence (AI) algorithms are being developed. The medical community and national and federal regulatory bodies are recognizing the importance of adapting to AI. However, there is a gap in physicians’ understanding of AI and its implications regarding its potential use in clinical care, and there are limited resources and established programs focused on AI and medical education in ophthalmology. Physicians are essential in the application of AI in a clinical context. An AI curriculum in ophthalmology can help provide physicians with a fund of knowledge and skills to integrate AI into their practice. In this paper, we provide general recommendations for an AI curriculum for medical students, residents, and fellows in ophthalmology.
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Affiliation(s)
- Nita G Valikodath
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, USA
| | - Emily Cole
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, USA
| | - Daniel S W Ting
- Singapore National Eye Center, Duke-NUS Medical School, Singapore
| | - J Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA
| | - Louis R Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA
| | - Michael F Chiang
- National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - R V Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, USA
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35
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Tey KY, Wong QY, Dan YS, Tsai ASH, Ting DSW, Ang M, Cheung GCM, Lee SY, Wong TY, Hoang QV, Wong CW. Association of Aberrant Posterior Vitreous Detachment and Pathologic Tractional Forces With Myopic Macular Degeneration. Invest Ophthalmol Vis Sci 2021; 62:7. [PMID: 34096974 PMCID: PMC8185394 DOI: 10.1167/iovs.62.7.7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Purpose The purpose of this study was to assess whether the tractional elements of pathologic myopia (PM; e.g. myopic traction maculopathy [MTM], posterior staphyloma [PS], and aberrant posterior vitreous detachment [PVD]) are associated with myopic macular degeneration (MMD) independent of age and axial length, among highly myopic (HM) eyes. Methods One hundred twenty-nine individuals with 239 HM eyes from the Myopic and Pathologic Eyes in Singapore (MyoPES) cohort underwent ocular biometry, fundus photography, swept-source optical coherence tomography, and ocular B-scan ultrasound. Images were analyzed for PVD grade, and presence of MTM, PS, and MMD. The χ² test was done to determine the difference in prevalence of MMD between eyes with and without PVD, PS, and MTM. Multivariate probit regression analyses were performed to ascertain the relationship between the potential predictors (PVD, PS, and MTM) and outcome variable (MMD), after accounting for possible confounders (e.g. age and axial length). Marginal effects were reported. Results Controlling for potential confounders, eyes with MTM have a 29.92 percentage point higher likelihood of having MMD (P = 0.003), and eyes with PS have a 25.72 percentage point higher likelihood of having MMD (P = 0.002). The likelihood of MMD increases by 10.61 percentage points per 1 mm increase in axial length (P < 0.001). Subanalysis revealed that eyes with incomplete PVD have a 22.54 percentage point higher likelihood of having MMD than eyes with early PVD (P = 0.04). Conclusions Our study demonstrated an association between tractional (MTM, PS, and persistently incomplete PVD) and degenerative elements of PM independent of age and axial length. These data provide further insights into the pathogenesis of MMD.
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Affiliation(s)
- Kai Yuan Tey
- Singapore Eye Research Institute, Singapore.,Tasmanian School of Medicine, Tasmania, Australia
| | | | | | - Andrew S H Tsai
- Singapore National Eye Centre, Duke-NUS Medical School, Singapore
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore.,Singapore National Eye Centre, Duke-NUS Medical School, Singapore
| | - Marcus Ang
- Singapore Eye Research Institute, Singapore.,Singapore National Eye Centre, Duke-NUS Medical School, Singapore
| | - Gemmy Chiu Ming Cheung
- Singapore Eye Research Institute, Singapore.,Singapore National Eye Centre, Duke-NUS Medical School, Singapore
| | - Shu Yen Lee
- Singapore National Eye Centre, Duke-NUS Medical School, Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore.,Singapore National Eye Centre, Duke-NUS Medical School, Singapore
| | - Quan V Hoang
- Singapore Eye Research Institute, Singapore.,Singapore National Eye Centre, Duke-NUS Medical School, Singapore.,Department of Ophthalmology, Columbia University College of Physicians and Surgeons, New York, NY, United States
| | - Chee Wai Wong
- Singapore Eye Research Institute, Singapore.,Singapore National Eye Centre, Duke-NUS Medical School, Singapore
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Valikodath NG, Al-Khaled T, Cole E, Ting DSW, Tu EY, Campbell JP, Chiang MF, Hallak JA, Chan RVP. Evaluation of pediatric ophthalmologists' perspectives of artificial intelligence in ophthalmology. J AAPOS 2021; 25:164.e1-164.e5. [PMID: 34087473 PMCID: PMC8328946 DOI: 10.1016/j.jaapos.2021.01.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 01/12/2021] [Accepted: 01/13/2021] [Indexed: 12/31/2022]
Abstract
PURPOSE To survey pediatric ophthalmologists on their perspectives of artificial intelligence (AI) in ophthalmology. METHODS This is a subgroup analysis of a study previously reported. In March 2019, members of the American Association for Pediatric Ophthalmology and Strabismus (AAPOS) were recruited via the online AAPOS discussion board to voluntarily complete a Web-based survey consisting of 15 items. Survey items assessed the extent participants "agreed" or "disagreed" with statements on the perceived benefits and concerns of AI in ophthalmology. Responses were analyzed using descriptive statistics. RESULTS A total of 80 pediatric ophthalmologists who are members of AAPOS completed the survey. The mean number of years since graduating residency was 21 years (range, 0-46). Overall, 91% (73/80) reported understanding the concept of AI, 70% (56/80) believed AI will improve the practice of ophthalmology, 68% (54/80) reported willingness to incorporate AI into their clinical practice, 65% (52/80) did not believe AI will replace physicians, and 71% (57/80) believed AI should be incorporated into medical school and residency curricula. However, 15% (12/80) were concerned that AI will replace physicians, 26% (21/80) believed AI will harm the patient-physician relationship, and 46% (37/80) reported concern over the diagnostic accuracy of AI. CONCLUSIONS Most pediatric ophthalmologists in this survey viewed the role of AI in ophthalmology positively.
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Affiliation(s)
- Nita G Valikodath
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Tala Al-Khaled
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Emily Cole
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Daniel S W Ting
- Singapore National Eye Center, Duke-NUS Medical School Singapore, Singapore
| | - Elmer Y Tu
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - J Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Michael F Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Joelle A Hallak
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - R V Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois.
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Li JPO, Liu H, Ting DSJ, Jeon S, Chan RVP, Kim JE, Sim DA, Thomas PBM, Lin H, Chen Y, Sakomoto T, Loewenstein A, Lam DSC, Pasquale LR, Wong TY, Lam LA, Ting DSW. Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective. Prog Retin Eye Res 2021; 82:100900. [PMID: 32898686 PMCID: PMC7474840 DOI: 10.1016/j.preteyeres.2020.100900] [Citation(s) in RCA: 189] [Impact Index Per Article: 63.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 08/25/2020] [Accepted: 08/31/2020] [Indexed: 12/29/2022]
Abstract
The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital innovations include artificial intelligence (AI), 5th generation (5G) telecommunication networks and the Internet of Things (IoT), creating an inter-dependent ecosystem offering opportunities to develop new models of eye care addressing the challenges of COVID-19 and beyond. Ophthalmology has thrived in some of these areas partly due to its many image-based investigations. Tele-health and AI provide synchronous solutions to challenges facing ophthalmologists and healthcare providers worldwide. This article reviews how countries across the world have utilised these digital innovations to tackle diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract and other anterior segment disorders. The review summarises the digital strategies that countries are developing and discusses technologies that may increasingly enter the clinical workflow and processes of ophthalmologists. Furthermore as countries around the world have initiated a series of escalating containment and mitigation measures during the COVID-19 pandemic, the delivery of eye care services globally has been significantly impacted. As ophthalmic services adapt and form a "new normal", the rapid adoption of some of telehealth and digital innovation during the pandemic is also discussed. Finally, challenges for validation and clinical implementation are considered, as well as recommendations on future directions.
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Affiliation(s)
- Ji-Peng Olivia Li
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Hanruo Liu
- Beijing Tongren Hospital; Capital Medical University; Beijing Institute of Ophthalmology; Beijing, China
| | - Darren S J Ting
- Academic Ophthalmology, University of Nottingham, United Kingdom
| | - Sohee Jeon
- Keye Eye Center, Seoul, Republic of Korea
| | | | - Judy E Kim
- Medical College of Wisconsin, Milwaukee, WI, USA
| | - Dawn A Sim
- NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Peter B M Thomas
- NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Haotian Lin
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Guangzhou, China
| | - Youxin Chen
- Peking Union Medical College Hospital, Beijing, China
| | - Taiji Sakomoto
- Department of Ophthalmology, Kagoshima University Graduate School of Medical and Dental Sciences, Japan
| | | | - Dennis S C Lam
- C-MER Dennis Lam Eye Center, C-Mer International Eye Care Group Limited, Hong Kong, Hong Kong; International Eye Research Institute of the Chinese University of Hong Kong (Shenzhen), Shenzhen, China
| | - Louis R Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Tien Y Wong
- Singapore National Eye Center, Duke-NUS Medical School Singapore, Singapore
| | - Linda A Lam
- USC Roski Eye Institute, University of Southern California (USC) Keck School of Medicine, Los Angeles, CA, USA
| | - Daniel S W Ting
- Singapore National Eye Center, Duke-NUS Medical School Singapore, Singapore.
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Rim TH, Lee CJ, Tham YC, Cheung N, Yu M, Lee G, Kim Y, Ting DSW, Chong CCY, Choi YS, Yoo TK, Ryu IH, Baik SJ, Kim YA, Kim SK, Lee SH, Lee BK, Kang SM, Wong EYM, Kim HC, Kim SS, Park S, Cheng CY, Wong TY. Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs. Lancet Digit Health 2021; 3:e306-e316. [PMID: 33890578 DOI: 10.1016/s2589-7500(21)00043-1] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 02/17/2021] [Accepted: 03/02/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Coronary artery calcium (CAC) score is a clinically validated marker of cardiovascular disease risk. We developed and validated a novel cardiovascular risk stratification system based on deep-learning-predicted CAC from retinal photographs. METHODS We used 216 152 retinal photographs from five datasets from South Korea, Singapore, and the UK to train and validate the algorithms. First, using one dataset from a South Korean health-screening centre, we trained a deep-learning algorithm to predict the probability of the presence of CAC (ie, deep-learning retinal CAC score, RetiCAC). We stratified RetiCAC scores into tertiles and used Cox proportional hazards models to evaluate the ability of RetiCAC to predict cardiovascular events based on external test sets from South Korea, Singapore, and the UK Biobank. We evaluated the incremental values of RetiCAC when added to the Pooled Cohort Equation (PCE) for participants in the UK Biobank. FINDINGS RetiCAC outperformed all single clinical parameter models in predicting the presence of CAC (area under the receiver operating characteristic curve of 0·742, 95% CI 0·732-0·753). Among the 527 participants in the South Korean clinical cohort, 33 (6·3%) had cardiovascular events during the 5-year follow-up. When compared with the current CAC risk stratification (0, >0-100, and >100), the three-strata RetiCAC showed comparable prognostic performance with a concordance index of 0·71. In the Singapore population-based cohort (n=8551), 310 (3·6%) participants had fatal cardiovascular events over 10 years, and the three-strata RetiCAC was significantly associated with increased risk of fatal cardiovascular events (hazard ratio [HR] trend 1·33, 95% CI 1·04-1·71). In the UK Biobank (n=47 679), 337 (0·7%) participants had fatal cardiovascular events over 10 years. When added to the PCE, the three-strata RetiCAC improved cardiovascular risk stratification in the intermediate-risk group (HR trend 1·28, 95% CI 1·07-1·54) and borderline-risk group (1·62, 1·04-2·54), and the continuous net reclassification index was 0·261 (95% CI 0·124-0·364). INTERPRETATION A deep learning and retinal photograph-derived CAC score is comparable to CT scan-measured CAC in predicting cardiovascular events, and improves on current risk stratification approaches for cardiovascular disease events. These data suggest retinal photograph-based deep learning has the potential to be used as an alternative measure of CAC, especially in low-resource settings. FUNDING Yonsei University College of Medicine; Ministry of Health and Welfare, Korea Institute for Advancement of Technology, South Korea; Agency for Science, Technology, and Research; and National Medical Research Council, Singapore.
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Affiliation(s)
- Tyler Hyungtaek Rim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore; Department of Ophthalmology, Institute of Vision Research, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Chan Joo Lee
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Ning Cheung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Marco Yu
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | | | | | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | | | - Yoon Seong Choi
- Radiological Sciences Academic Clinical Programme, Duke-NUS Medical School, Singapore; Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Tae Keun Yoo
- Department of Ophthalmology, Institute of Vision Research, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | | | - Su Jung Baik
- Healthcare Research Team, Health Promotion Center, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Young Ah Kim
- Division of Healthcare Big Data, Yonsei University College of Medicine, Seoul, South Korea
| | - Sung Kyu Kim
- Philip Medical Center, Bundang, Seongnam, South Korea
| | - Sang-Hak Lee
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea; Integrated Research Center for Cerebrovascular and Cardiovascular Diseases, Yonsei University College of Medicine, Seoul, South Korea
| | - Byoung Kwon Lee
- Division of Cardiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Seok-Min Kang
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Edmund Yick Mun Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Hyeon Chang Kim
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea; Integrated Research Center for Cerebrovascular and Cardiovascular Diseases, Yonsei University College of Medicine, Seoul, South Korea
| | - Sung Soo Kim
- Department of Ophthalmology, Institute of Vision Research, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
| | - Sungha Park
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea; Integrated Research Center for Cerebrovascular and Cardiovascular Diseases, Yonsei University College of Medicine, Seoul, South Korea.
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore.
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
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Aggarwal R, Sounderajah V, Martin G, Ting DSW, Karthikesalingam A, King D, Ashrafian H, Darzi A. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Digit Med 2021; 4:65. [PMID: 33828217 PMCID: PMC8027892 DOI: 10.1038/s41746-021-00438-z] [Citation(s) in RCA: 202] [Impact Index Per Article: 67.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 02/25/2021] [Indexed: 12/19/2022] Open
Abstract
Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted in Medline and EMBASE up to January 2020. We identified 11,921 studies, of which 503 were included in the systematic review. Eighty-two studies in ophthalmology, 82 in breast disease and 115 in respiratory disease were included for meta-analysis. Two hundred twenty-four studies in other specialities were included for qualitative review. Peer-reviewed studies that reported on the diagnostic accuracy of DL algorithms to identify pathology using medical imaging were included. Primary outcomes were measures of diagnostic accuracy, study design and reporting standards in the literature. Estimates were pooled using random-effects meta-analysis. In ophthalmology, AUC's ranged between 0.933 and 1 for diagnosing diabetic retinopathy, age-related macular degeneration and glaucoma on retinal fundus photographs and optical coherence tomography. In respiratory imaging, AUC's ranged between 0.864 and 0.937 for diagnosing lung nodules or lung cancer on chest X-ray or CT scan. For breast imaging, AUC's ranged between 0.868 and 0.909 for diagnosing breast cancer on mammogram, ultrasound, MRI and digital breast tomosynthesis. Heterogeneity was high between studies and extensive variation in methodology, terminology and outcome measures was noted. This can lead to an overestimation of the diagnostic accuracy of DL algorithms on medical imaging. There is an immediate need for the development of artificial intelligence-specific EQUATOR guidelines, particularly STARD, in order to provide guidance around key issues in this field.
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Affiliation(s)
- Ravi Aggarwal
- Institute of Global Health Innovation, Imperial College London, London, UK
| | | | - Guy Martin
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | | | - Dominic King
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Hutan Ashrafian
- Institute of Global Health Innovation, Imperial College London, London, UK.
| | - Ara Darzi
- Institute of Global Health Innovation, Imperial College London, London, UK
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40
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Gunasekeran DV, Tham YC, Ting DSW, Tan GSW, Wong TY. Digital health during COVID-19: lessons from operationalising new models of care in ophthalmology. Lancet Digit Health 2021; 3:e124-e134. [PMID: 33509383 DOI: 10.1016/s2589-7500(20)30287-9] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 11/11/2020] [Accepted: 11/18/2020] [Indexed: 12/13/2022]
Abstract
The COVID-19 pandemic has resulted in massive disruptions within health care, both directly as a result of the infectious disease outbreak, and indirectly because of public health measures to mitigate against transmission. This disruption has caused rapid dynamic fluctuations in demand, capacity, and even contextual aspects of health care. Therefore, the traditional face-to-face patient-physician care model has had to be re-examined in many countries, with digital technology and new models of care being rapidly deployed to meet the various challenges of the pandemic. This Viewpoint highlights new models in ophthalmology that have adapted to incorporate digital health solutions such as telehealth, artificial intelligence decision support for triaging and clinical care, and home monitoring. These models can be operationalised for different clinical applications based on the technology, clinical need, demand from patients, and manpower availability, ranging from out-of-hospital models including the hub-and-spoke pre-hospital model, to front-line models such as the inflow funnel model and monitoring models such as the so-called lighthouse model for provider-led monitoring. Lessons learnt from operationalising these models for ophthalmology in the context of COVID-19 are discussed, along with their relevance for other specialty domains.
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Affiliation(s)
- Dinesh V Gunasekeran
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Duke-NUS Medical School, Singapore
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Duke-NUS Medical School, Singapore
| | - Gavin S W Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Duke-NUS Medical School, Singapore
| | - Tien Y Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Duke-NUS Medical School, Singapore.
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41
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Campbell JP, Lee AY, Abràmoff M, Keane PA, Ting DSW, Lum F, Chiang MF. Reporting Guidelines for Artificial Intelligence in Medical Research. Ophthalmology 2020; 127:1596-1599. [PMID: 32920029 PMCID: PMC7875521 DOI: 10.1016/j.ophtha.2020.09.009] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 09/04/2020] [Accepted: 09/08/2020] [Indexed: 11/16/2022] Open
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Fenwick EK, Man REK, Gan ATL, Aravindhan A, Tey CS, Soon HJT, Ting DSW, Yeo SIY, Lee SY, Tan G, Wong TY, Lamoureux EL. Validation of a New Diabetic Retinopathy Knowledge and Attitudes Questionnaire in People with Diabetic Retinopathy and Diabetic Macular Edema. Transl Vis Sci Technol 2020; 9:32. [PMID: 33062395 PMCID: PMC7533728 DOI: 10.1167/tvst.9.10.32] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 08/21/2020] [Indexed: 12/31/2022] Open
Abstract
Purpose A validated questionnaire assessing diabetic retinopathy (DR)- and diabetic macular edema (DME)-related knowledge (K) and attitudes (A) is lacking. We developed and validated the Diabetic Retinopathy Knowledge and Attitudes (DRKA) questionnaire and explored the association between K and A and the self-reported difficulty accessing DR-related information (hereafter referred to as Access). Methods In this mixed-methods study, eight focus groups with 36 people with DR or DME (mean age, 60.1 ± 8.0 years; 53% male) were conducted to develop content (phase 1). In phase 2, we conducted 10 cognitive interviews to refine item phrasing. In phase 3, we administered 28-item K and nine-item A pilot questionnaires to 200 purposively recruited DR/DME patients (mean age, 59.0 ± 10.6 years; 59% male). The psychometric properties of DRKA were assessed using Rasch and classical methods. The association between K and A and DR-related Access was assessed using univariable linear regression of mean K/A scores against Access. Results Following Rasch-guided amendments, the final 22-item K and nine-item A scales demonstrated adequate psychometric properties, although precision remained borderline. The scales displayed excellent discriminant validity, with K/A scores increasing as education level increased. Compared to those with low scores, those with high K/A scores were more likely to report better access to DR-related information, with K scores of 0.99 ± 0.86 for no difficulty; 0.79 ± 1.05 for a little difficulty; and 0.24 ± 0.85 for moderate or worse difficulty (P < 0.001). Conclusions The psychometrically robust 31-item DRKA questionnaire can measure DR- and DME-related knowledge and attitudes. Translational Relevance The DRKA questionnaire may be useful for interventions to improve DR-related knowledge and attitudes and, in turn, optimize health behaviors and health literacy.
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Affiliation(s)
- Eva K Fenwick
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Duke-NUS Medical School, Singapore
| | - Ryan E K Man
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Duke-NUS Medical School, Singapore
| | - Alfred T L Gan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Amudha Aravindhan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Ching Siong Tey
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | | | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Duke-NUS Medical School, Singapore
| | - San I Y Yeo
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Duke-NUS Medical School, Singapore
| | - Shu Yen Lee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Gavin Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Tien Y Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Ecosse L Lamoureux
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
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43
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Li F, Song D, Chen H, Xiong J, Li X, Zhong H, Tang G, Fan S, Lam DSC, Pan W, Zheng Y, Li Y, Qu G, He J, Wang Z, Jin L, Zhou R, Song Y, Sun Y, Cheng W, Yang C, Fan Y, Li Y, Zhang H, Yuan Y, Xu Y, Xiong Y, Jin L, Lv A, Niu L, Liu Y, Li S, Zhang J, Zangwill LM, Frangi AF, Aung T, Cheng CY, Qiao Y, Zhang X, Ting DSW. Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection. NPJ Digit Med 2020; 3:123. [PMID: 33043147 PMCID: PMC7508974 DOI: 10.1038/s41746-020-00329-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 08/31/2020] [Indexed: 12/02/2022] Open
Abstract
By 2040, ~100 million people will have glaucoma. To date, there are a lack of high-efficiency glaucoma diagnostic tools based on visual fields (VFs). Herein, we develop and evaluate the performance of 'iGlaucoma', a smartphone application-based deep learning system (DLS) in detecting glaucomatous VF changes. A total of 1,614,808 data points of 10,784 VFs (5542 patients) from seven centers in China were included in this study, divided over two phases. In Phase I, 1,581,060 data points from 10,135 VFs of 5105 patients were included to train (8424 VFs), validate (598 VFs) and test (3 independent test sets-200, 406, 507 samples) the diagnostic performance of the DLS. In Phase II, using the same DLS, iGlaucoma cloud-based application further tested on 33,748 data points from 649 VFs of 437 patients from three glaucoma clinics. With reference to three experienced expert glaucomatologists, the diagnostic performance (area under curve [AUC], sensitivity and specificity) of the DLS and six ophthalmologists were evaluated in detecting glaucoma. In Phase I, the DLS outperformed all six ophthalmologists in the three test sets (AUC of 0.834-0.877, with a sensitivity of 0.831-0.922 and a specificity of 0.676-0.709). In Phase II, iGlaucoma had 0.99 accuracy in recognizing different patterns in pattern deviation probability plots region, with corresponding AUC, sensitivity and specificity of 0.966 (0.953-0.979), 0.954 (0.930-0.977), and 0.873 (0.838-0.908), respectively. The 'iGlaucoma' is a clinically effective glaucoma diagnostic tool to detect glaucoma from humphrey VFs, although the target population will need to be carefully identified with glaucoma expertise input.
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Affiliation(s)
- Fei Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Diping Song
- ShenZhen Key Lab of Computer Vision and Pattern Recognition, Shenzhen Institutes of Advanced Technology, The Chinese Academy of Sciences, Shenzhen, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing, People’s Republic of China
| | - Han Chen
- ShenZhen Key Lab of Computer Vision and Pattern Recognition, Shenzhen Institutes of Advanced Technology, The Chinese Academy of Sciences, Shenzhen, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing, People’s Republic of China
| | - Jian Xiong
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Xingyi Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Hua Zhong
- Department of Ophthalmology, The First Affiliated Hospital of Kunming Medical University, Kunming, People’s Republic of China
| | - Guangxian Tang
- The First Hospital of Shijiazhuang City, Shijiazhuang, People’s Republic of China
| | - Sujie Fan
- Handan City Eye Hospital, Handan, People’s Republic of China
| | - Dennis S. C. Lam
- C-MER (Shenzhen) Dennis Lam Eye Hospital, International Eye Research Institute of The Chinese University of Hong Kong (Shenzhen), Shenzhen, People’s Republic of China
| | - Weihua Pan
- The Eye Hospital, WMU at Hangzhou, Hangzhou, People’s Republic of China
| | - Yajuan Zheng
- Department of Ophthalmology, The Second Hospital of Jilin University, Changchun, People’s Republic of China
| | - Ying Li
- ShenZhen Key Lab of Computer Vision and Pattern Recognition, Shenzhen Institutes of Advanced Technology, The Chinese Academy of Sciences, Shenzhen, People’s Republic of China
| | - Guoxiang Qu
- ShenZhen Key Lab of Computer Vision and Pattern Recognition, Shenzhen Institutes of Advanced Technology, The Chinese Academy of Sciences, Shenzhen, People’s Republic of China
| | - Junjun He
- ShenZhen Key Lab of Computer Vision and Pattern Recognition, Shenzhen Institutes of Advanced Technology, The Chinese Academy of Sciences, Shenzhen, People’s Republic of China
| | - Zhe Wang
- SenseTime Group Limited, Hong Kong, People’s Republic of China
| | - Ling Jin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Rouxi Zhou
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Yunhe Song
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Yi Sun
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Weijing Cheng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Chunman Yang
- Department of Ophthalmology, The Second Affiliated Hospital of Guizhou Medical University, Kaili, People’s Republic of China
| | - Yazhi Fan
- Department of Ophthalmology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, People’s Republic of China
| | - Yingjie Li
- Department of Ophthalmology, The Third Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China
| | - Hengli Zhang
- The First Hospital of Shijiazhuang City, Shijiazhuang, People’s Republic of China
| | - Ye Yuan
- C-MER (Shenzhen) Dennis Lam Eye Hospital, International Eye Research Institute of The Chinese University of Hong Kong (Shenzhen), Shenzhen, People’s Republic of China
| | - Yang Xu
- Department of Ophthalmology, The First Affiliated Hospital of Kunming Medical University, Kunming, People’s Republic of China
| | - Yunfan Xiong
- Department of Ophthalmology, The First Affiliated Hospital of Kunming Medical University, Kunming, People’s Republic of China
| | - Lingfei Jin
- The Eye Hospital, WMU at Hangzhou, Hangzhou, People’s Republic of China
| | - Aiguo Lv
- Handan City Eye Hospital, Handan, People’s Republic of China
| | - Lingzhi Niu
- Department of Ophthalmology, The Second Hospital of Jilin University, Changchun, People’s Republic of China
| | - Yuhong Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Shaoli Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Jiani Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Linda M. Zangwill
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, CA United States
| | - Alejandro F. Frangi
- CISTIB Center for Computational Imaging and Simulation Technologies in Biomedicine, Schools of Computing and Medicine, University of Leeds, Leeds, UK
| | - Tin Aung
- Singapore Eye Research Institute and Singapore National Eye Centre, Singapore, Singapore
| | - Ching-yu Cheng
- Singapore Eye Research Institute and Singapore National Eye Centre, Singapore, Singapore
| | - Yu Qiao
- ShenZhen Key Lab of Computer Vision and Pattern Recognition, Shenzhen Institutes of Advanced Technology, The Chinese Academy of Sciences, Shenzhen, People’s Republic of China
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Daniel S. W. Ting
- Singapore Eye Research Institute and Singapore National Eye Centre, Singapore, Singapore
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FRCOphth AK, Shantha JG, Olivia Li JP, Faia LJ, Hartley C, Kuthyar S, Albini TA, Wu H, Chodosh J, Ting DSW, Yeh S. SARS-CoV-2 and the Eye: Implications for the Retina Specialist from Human Coronavirus Outbreaks and Animal Models. ACTA ACUST UNITED AC 2020; 4:411-419. [PMID: 33665540 PMCID: PMC7928265 DOI: 10.1177/2474126420939723] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Purpose The current SARS-CoV-2 pandemic has escalated rapidly since December 2019. Understanding the ophthalmic manifestations in patients and animal models of the novel coronavirus may have implications for disease surveillance. Recognition of the potential for viral transmission through the tear film has ramification for protection of patients, physicians, and the public. Methods Information from relevant published journal articles was surveyed using a computerized PubMed search and public health websites. We summarize current knowledge of ophthalmic manifestations of SARS-CoV-2 infection in patients and animal models, risk mitigation measures for patients and their providers, and implications for retina specialists. Results SARS-CoV-2 is efficiently transmitted among humans, and while the clinical course is mild in the majority of infected patients, severe complications including pneumonia, acute respiratory distress syndrome, and death can ensue, most often in elderly patients and individuals with co-morbidities. Conjunctivitis occurs in a small minority of patients with COVID-19 and SARS-CoV-2 RNA has been identified primarily in association with conjunctivitis. Uveitis has been observed in animal models of coronavirus infection and cotton-wool spots have been reported recently. Conclusion SARS-CoV-2 and other coronaviruses have been rarely associated with conjunctivitis. The identification of SARS-CoV and SARS-CoV-2 RNA in the tear film of patients and its highly efficient transmission via respiratory aerosols supports eye protection, mask and gloves as part of infection prevention and control recommendations for retina providers. Disease surveillance during the COVID-19 pandemic outbreak may also include ongoing evaluation for uveitis and retinal disease given prior findings observed in animal models and a recent report of retinal manifestations.
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Affiliation(s)
| | | | - Ji-Peng Olivia Li
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Lisa J Faia
- Associated Retinal Consultants, Royal Oak, MI
| | - Caleb Hartley
- Emory University Rollins School of Public Health, Atlanta, GA
| | - Sanjana Kuthyar
- Emory Eye Center, Emory University School of Medicine, Atlanta, GA
| | - Thomas A Albini
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine
| | - Henry Wu
- Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, GA
| | - James Chodosh
- Massachusetts Eye and Ear, Harvard Medical School, Boston, MA
| | - Daniel S W Ting
- Singapore National Eye Center, Duke-NUS Medical School, Singapore
| | - Steven Yeh
- Emory Eye Center, Emory University School of Medicine, Atlanta, GA.,Emory Global Health Institute, Emory University, Atlanta, GA
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45
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Campbell CG, Ting DSW, Keane PA, Foster PJ. The potential application of artificial intelligence for diagnosis and management of glaucoma in adults. Br Med Bull 2020; 134:21-33. [PMID: 32518944 DOI: 10.1093/bmb/ldaa012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 04/02/2020] [Accepted: 04/02/2020] [Indexed: 12/26/2022]
Abstract
BACKGROUND Glaucoma is the most frequent cause of irreversible blindness worldwide. There is no cure, but early detection and treatment can slow the progression and prevent loss of vision. It has been suggested that artificial intelligence (AI) has potential application for detection and management of glaucoma. SOURCES OF DATA This literature review is based on articles published in peer-reviewed journals. AREAS OF AGREEMENT There have been significant advances in both AI and imaging techniques that are able to identify the early signs of glaucomatous damage. Machine and deep learning algorithms show capabilities equivalent to human experts, if not superior. AREAS OF CONTROVERSY Concerns that the increased reliance on AI may lead to deskilling of clinicians. GROWING POINTS AI has potential to be used in virtual review clinics, telemedicine and as a training tool for junior doctors. Unsupervised AI techniques offer the potential of uncovering currently unrecognized patterns of disease. If this promise is fulfilled, AI may then be of use in challenging cases or where a second opinion is desirable. AREAS TIMELY FOR DEVELOPING RESEARCH There is a need to determine the external validity of deep learning algorithms and to better understand how the 'black box' paradigm reaches results.
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Affiliation(s)
- Cara G Campbell
- UCL Institute of Ophthalmology, Faculty of Brain Science, University College London, 11-43 Bath Street, London EC1V 9EL, UK
| | - Daniel S W Ting
- Medical Retina Service, Moorfields Eye Hospital NHS Foundation Trust, 162 City Road, London EC1V 2PD, UK
| | - Pearse A Keane
- UCL Institute of Ophthalmology, Faculty of Brain Science, University College London, 11-43 Bath Street, London EC1V 9EL, UK
- Medical Retina Service, Moorfields Eye Hospital NHS Foundation Trust, 162 City Road, London EC1V 2PD, UK
- National Institute for Health Research Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust NHS Foundation Trust, 2/12 Wolfson Building and UCL Institute of Ophthalmology, 11-43 Bath Street, London EC1V 9EL, UK
| | - Paul J Foster
- UCL Institute of Ophthalmology, Faculty of Brain Science, University College London, 11-43 Bath Street, London EC1V 9EL, UK
- Medical Retina Service, Moorfields Eye Hospital NHS Foundation Trust, 162 City Road, London EC1V 2PD, UK
- National Institute for Health Research Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust NHS Foundation Trust, 2/12 Wolfson Building and UCL Institute of Ophthalmology, 11-43 Bath Street, London EC1V 9EL, UK
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46
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Chua J, Sim R, Tan B, Wong D, Yao X, Liu X, Ting DSW, Schmidl D, Ang M, Garhöfer G, Schmetterer L. Optical Coherence Tomography Angiography in Diabetes and Diabetic Retinopathy. J Clin Med 2020; 9:E1723. [PMID: 32503234 PMCID: PMC7357089 DOI: 10.3390/jcm9061723] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 05/24/2020] [Accepted: 06/02/2020] [Indexed: 12/21/2022] Open
Abstract
Diabetic retinopathy (DR) is a common complication of diabetes mellitus that disrupts the retinal microvasculature and is a leading cause of vision loss globally. Recently, optical coherence tomography angiography (OCTA) has been developed to image the retinal microvasculature, by generating 3-dimensional images based on the motion contrast of circulating blood cells. OCTA offers numerous benefits over traditional fluorescein angiography in visualizing the retinal vasculature in that it is non-invasive and safer; while its depth-resolved ability makes it possible to visualize the finer capillaries of the retinal capillary plexuses and choriocapillaris. High-quality OCTA images have also enabled the visualization of features associated with DR, including microaneurysms and neovascularization and the quantification of alterations in retinal capillary and choriocapillaris, thereby suggesting a promising role for OCTA as an objective technology for accurate DR classification. Of interest is the potential of OCTA to examine the effect of DR on individual retinal layers, and to detect DR even before it is clinically detectable on fundus examination. We will focus the review on the clinical applicability of OCTA derived quantitative metrics that appear to be clinically relevant to the diagnosis, classification, and management of patients with diabetes or DR. Future studies with longitudinal design of multiethnic multicenter populations, as well as the inclusion of pertinent systemic information that may affect vascular changes, will improve our understanding on the benefit of OCTA biomarkers in the detection and progression of DR.
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Affiliation(s)
- Jacqueline Chua
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore; (J.C.); (R.S.); (B.T.); (D.W.); (X.Y.); (X.L.); (D.S.W.T.); (M.A.)
- Academic Clinical Program, Duke-NUS Medical School, Singapore 169857, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE), Singapore 639798, Singapore
| | - Ralene Sim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore; (J.C.); (R.S.); (B.T.); (D.W.); (X.Y.); (X.L.); (D.S.W.T.); (M.A.)
| | - Bingyao Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore; (J.C.); (R.S.); (B.T.); (D.W.); (X.Y.); (X.L.); (D.S.W.T.); (M.A.)
- SERI-NTU Advanced Ocular Engineering (STANCE), Singapore 639798, Singapore
- Institute of Health Technologies, Nanyang Technological University, Singapore 639798, Singapore
| | - Damon Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore; (J.C.); (R.S.); (B.T.); (D.W.); (X.Y.); (X.L.); (D.S.W.T.); (M.A.)
- SERI-NTU Advanced Ocular Engineering (STANCE), Singapore 639798, Singapore
- Institute of Health Technologies, Nanyang Technological University, Singapore 639798, Singapore
| | - Xinwen Yao
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore; (J.C.); (R.S.); (B.T.); (D.W.); (X.Y.); (X.L.); (D.S.W.T.); (M.A.)
- SERI-NTU Advanced Ocular Engineering (STANCE), Singapore 639798, Singapore
- Institute of Health Technologies, Nanyang Technological University, Singapore 639798, Singapore
| | - Xinyu Liu
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore; (J.C.); (R.S.); (B.T.); (D.W.); (X.Y.); (X.L.); (D.S.W.T.); (M.A.)
- SERI-NTU Advanced Ocular Engineering (STANCE), Singapore 639798, Singapore
| | - Daniel S. W. Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore; (J.C.); (R.S.); (B.T.); (D.W.); (X.Y.); (X.L.); (D.S.W.T.); (M.A.)
- Academic Clinical Program, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Doreen Schmidl
- Department of Clinical Pharmacology, Medical University of Vienna, 1090 Vienna, Austria; (D.S.); (G.G.)
| | - Marcus Ang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore; (J.C.); (R.S.); (B.T.); (D.W.); (X.Y.); (X.L.); (D.S.W.T.); (M.A.)
- Academic Clinical Program, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Gerhard Garhöfer
- Department of Clinical Pharmacology, Medical University of Vienna, 1090 Vienna, Austria; (D.S.); (G.G.)
| | - Leopold Schmetterer
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore; (J.C.); (R.S.); (B.T.); (D.W.); (X.Y.); (X.L.); (D.S.W.T.); (M.A.)
- Academic Clinical Program, Duke-NUS Medical School, Singapore 169857, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE), Singapore 639798, Singapore
- Institute of Health Technologies, Nanyang Technological University, Singapore 639798, Singapore
- Department of Clinical Pharmacology, Medical University of Vienna, 1090 Vienna, Austria; (D.S.); (G.G.)
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria
- Institute of Molecular and Clinical Ophthalmology, CH-4031 Basel, Switzerland
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Sabanayagam C, Xu D, Ting DSW, Nusinovici S, Banu R, Hamzah H, Lim C, Tham YC, Cheung CY, Tai ES, Wang YX, Jonas JB, Cheng CY, Lee ML, Hsu W, Wong TY. A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations. Lancet Digit Health 2020; 2:e295-e302. [PMID: 33328123 DOI: 10.1016/s2589-7500(20)30063-7] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 02/19/2020] [Accepted: 03/05/2020] [Indexed: 01/08/2023]
Abstract
BACKGROUND Screening for chronic kidney disease is a challenge in community and primary care settings, even in high-income countries. We developed an artificial intelligence deep learning algorithm (DLA) to detect chronic kidney disease from retinal images, which could add to existing chronic kidney disease screening strategies. METHODS We used data from three population-based, multiethnic, cross-sectional studies in Singapore and China. The Singapore Epidemiology of Eye Diseases study (SEED, patients aged ≥40 years) was used to develop (5188 patients) and validate (1297 patients) the DLA. External testing was done on two independent datasets: the Singapore Prospective Study Program (SP2, 3735 patients aged ≥25 years) and the Beijing Eye Study (BES, 1538 patients aged ≥40 years). Chronic kidney disease was defined as estimated glomerular filtration rate less than 60 mL/min per 1·73m2. Three models were trained: 1) image DLA; 2) risk factors (RF) including age, sex, ethnicity, diabetes, and hypertension; and 3) hybrid DLA combining image and RF. Model performances were evaluated using the area under the receiver operating characteristic curve (AUC). FINDINGS In the SEED validation dataset, the AUC was 0·911 for image DLA (95% CI 0·886 -0·936), 0·916 for RF (0·891-0·941), and 0·938 for hybrid DLA (0·917-0·959). Corresponding estimates in the SP2 testing dataset were 0·733 for image DLA (95% CI 0·696-0·770), 0·829 for RF (0·797-0·861), and 0·810 for hybrid DLA (0·776-0·844); and in the BES testing dataset estimates were 0·835 for image DLA (0·767-0·903), 0·887 for RF (0·828-0·946), and 0·858 for hybrid DLA (0·794-0·922). AUC estimates were similar in subgroups of people with diabetes (image DLA 0·889 [95% CI 0·850-0·928], RF 0·899 [0·862-0·936], hybrid 0·925 [0·893-0·957]) and hypertension (image DLA 0·889 [95% CI 0·860-0·918], RF 0·889 [0·860-0·918], hybrid 0·918 [0·893-0·943]). INTERPRETATION A retinal image DLA shows good performance for estimating chronic kidney disease, underlying the feasibility of using retinal photography as an adjunctive or opportunistic screening tool for chronic kidney disease in community populations. FUNDING National Medical Research Council, Singapore.
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Affiliation(s)
- Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Dejiang Xu
- School of Computing, National University of Singapore, Singapore
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Simon Nusinovici
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Riswana Banu
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Haslina Hamzah
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | | | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong
| | - E Shyong Tai
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Sciences Key Laboratory, Beijing, China
| | - Jost B Jonas
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Sciences Key Laboratory, Beijing, China; Department of Ophthalmology, Medical Faculty Mannheim of the Ruprecht-Karls-University Heidelberg, Mannheim, Germany
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Mong Li Lee
- School of Computing, National University of Singapore, Singapore
| | - Wynne Hsu
- School of Computing, National University of Singapore, Singapore
| | - Tien Y Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore.
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48
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Olivia Li JP, Shantha J, Wong TY, Wong EY, Mehta J, Lin H, Lin X, Strouthidis NG, Park KH, Fung AT, McLeod SD, Busin M, Parke DW, Holland GN, Chodosh J, Yeh S, Ting DSW. Preparedness among Ophthalmologists: During and Beyond the COVID-19 Pandemic. Ophthalmology 2020; 127:569-572. [PMID: 32327128 PMCID: PMC7167498 DOI: 10.1016/j.ophtha.2020.03.037] [Citation(s) in RCA: 112] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 03/27/2020] [Indexed: 01/02/2023] Open
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49
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Xie Y, Nguyen QD, Hamzah H, Lim G, Bellemo V, Gunasekeran DV, Yip MYT, Qi Lee X, Hsu W, Li Lee M, Tan CS, Tym Wong H, Lamoureux EL, Tan GSW, Wong TY, Finkelstein EA, Ting DSW. Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. Lancet Digit Health 2020; 2:e240-e249. [PMID: 33328056 DOI: 10.1016/s2589-7500(20)30060-1] [Citation(s) in RCA: 115] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 02/18/2020] [Accepted: 02/21/2020] [Indexed: 12/15/2022]
Abstract
BACKGROUND Deep learning is a novel machine learning technique that has been shown to be as effective as human graders in detecting diabetic retinopathy from fundus photographs. We used a cost-minimisation analysis to evaluate the potential savings of two deep learning approaches as compared with the current human assessment: a semi-automated deep learning model as a triage filter before secondary human assessment; and a fully automated deep learning model without human assessment. METHODS In this economic analysis modelling study, using 39 006 consecutive patients with diabetes in a national diabetic retinopathy screening programme in Singapore in 2015, we used a decision tree model and TreeAge Pro to compare the actual cost of screening this cohort with human graders against the simulated cost for semi-automated and fully automated screening models. Model parameters included diabetic retinopathy prevalence rates, diabetic retinopathy screening costs under each screening model, cost of medical consultation, and diagnostic performance (ie, sensitivity and specificity). The primary outcome was total cost for each screening model. Deterministic sensitivity analyses were done to gauge the sensitivity of the results to key model assumptions. FINDINGS From the health system perspective, the semi-automated screening model was the least expensive of the three models, at US$62 per patient per year. The fully automated model was $66 per patient per year, and the human assessment model was $77 per patient per year. The savings to the Singapore health system associated with switching to the semi-automated model are estimated to be $489 000, which is roughly 20% of the current annual screening cost. By 2050, Singapore is projected to have 1 million people with diabetes; at this time, the estimated annual savings would be $15 million. INTERPRETATION This study provides a strong economic rationale for using deep learning systems as an assistive tool to screen for diabetic retinopathy. FUNDING Ministry of Health, Singapore.
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Affiliation(s)
- Yuchen Xie
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Quang D Nguyen
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Haslina Hamzah
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Gilbert Lim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; School of Computing, National University of Singapore, Singapore
| | - Valentina Bellemo
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | | | | | - Xin Qi Lee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Wynne Hsu
- School of Computing, National University of Singapore, Singapore
| | - Mong Li Lee
- School of Computing, National University of Singapore, Singapore
| | - Colin S Tan
- Tan Tock Seng Hospital, National Healthcare Group, Singapore
| | - Hon Tym Wong
- Tan Tock Seng Hospital, National Healthcare Group, Singapore
| | - Ecosse L Lamoureux
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Tan Tock Seng Hospital, National Healthcare Group, Singapore
| | - Gavin S W Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Tan Tock Seng Hospital, National Healthcare Group, Singapore
| | - Tien Y Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Tan Tock Seng Hospital, National Healthcare Group, Singapore
| | | | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Tan Tock Seng Hospital, National Healthcare Group, Singapore; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yet-Sen University, Guangzhou, China.
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50
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Lim G, Bellemo V, Xie Y, Lee XQ, Yip MYT, Ting DSW. Different fundus imaging modalities and technical factors in AI screening for diabetic retinopathy: a review. Eye Vis (Lond) 2020; 7:21. [PMID: 32313813 PMCID: PMC7155252 DOI: 10.1186/s40662-020-00182-7] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 03/10/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Effective screening is a desirable method for the early detection and successful treatment for diabetic retinopathy, and fundus photography is currently the dominant medium for retinal imaging due to its convenience and accessibility. Manual screening using fundus photographs has however involved considerable costs for patients, clinicians and national health systems, which has limited its application particularly in less-developed countries. The advent of artificial intelligence, and in particular deep learning techniques, has however raised the possibility of widespread automated screening. MAIN TEXT In this review, we first briefly survey major published advances in retinal analysis using artificial intelligence. We take care to separately describe standard multiple-field fundus photography, and the newer modalities of ultra-wide field photography and smartphone-based photography. Finally, we consider several machine learning concepts that have been particularly relevant to the domain and illustrate their usage with extant works. CONCLUSIONS In the ophthalmology field, it was demonstrated that deep learning tools for diabetic retinopathy show clinically acceptable diagnostic performance when using colour retinal fundus images. Artificial intelligence models are among the most promising solutions to tackle the burden of diabetic retinopathy management in a comprehensive manner. However, future research is crucial to assess the potential clinical deployment, evaluate the cost-effectiveness of different DL systems in clinical practice and improve clinical acceptance.
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Affiliation(s)
- Gilbert Lim
- School of Computing, National University of Singapore, Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Valentina Bellemo
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, 11 Third Hospital Road Avenue, Singapore, 168751 Singapore
| | - Yuchen Xie
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Xin Q. Lee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Michelle Y. T. Yip
- Duke-NUS Medical School, National University of Singapore, 11 Third Hospital Road Avenue, Singapore, 168751 Singapore
| | - Daniel S. W. Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, 11 Third Hospital Road Avenue, Singapore, 168751 Singapore
- Vitreo-Retinal Service, Singapore National Eye Center, 11 Third Hospital Road Avenue, Singapore, 168751 Singapore
- Artificial Intelligence in Ophthalmology, Singapore Eye Research Institute, 11 Third Hospital Road Avenue, Singapore, 168751 Singapore
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