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Sidhu Z, Mansoori T. Artificial intelligence in glaucoma detection using color fundus photographs. Indian J Ophthalmol 2024; 72:408-411. [PMID: 38099383 PMCID: PMC11001223 DOI: 10.4103/ijo.ijo_613_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 08/14/2023] [Accepted: 09/25/2023] [Indexed: 12/19/2023] Open
Abstract
PURPOSE To explore the potential of artificial intelligence (AI) for glaucoma detection using deep learning algorithm and evaluate its accuracy for image classification of glaucomatous optic neuropathy (GON) from color fundus photographs. METHODS A total of 1375 color fundus photographs, 735 normal optic nerve head and 640 GON, were uploaded on the AI software for training, validation, and testing using deep learning model, which is based on Residual Network (Res Net) 50V2. For initial training and validation, 400 fundus images (200 normal and 200 GON) were uploaded and for the final training and testing 975 (535 normal and 440 GON) were uploaded later. Accuracy, sensitivity, and specificity were used to evaluate the image classification performance of the algorithm. Also, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio were calculated. RESULTS The model used in the study showed an image classification accuracy of 81.3%, sensitivity of 83%, and specificity of 80% for the detection of GON. The false-negative grading was 17% and false-positive grading was 20% for the image classification of GON. Coexistence of glaucoma in patients with high myopia, early glaucoma in a small disc, and software misclassification of GON were the reasons for false-negative results. Physiological large cupping in a large disc, myopic or titled disc, and software misclassification of normal optic disc were the reasons for false-positive results. CONCLUSION The model employed in this study achieved a good accuracy, and hence has a good potential in detection of GON using color fundus photographs.
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Affiliation(s)
- Zubin Sidhu
- Department of Science, Basis Scottsdale School, Scottsdale, Arizona, USA
| | - Tarannum Mansoori
- Department of Glaucoma, Anand Eye Institute, Hyderabad, Telangana, India
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Alotaibi SS, Rehman A, Hasnain M. Revolutionizing ocular cancer management: a narrative review on exploring the potential role of ChatGPT. Front Public Health 2023; 11:1338215. [PMID: 38192545 PMCID: PMC10773849 DOI: 10.3389/fpubh.2023.1338215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024] Open
Abstract
This paper pioneers the exploration of ocular cancer, and its management with the help of Artificial Intelligence (AI) technology. Existing literature presents a significant increase in new eye cancer cases in 2023, experiencing a higher incidence rate. Extensive research was conducted using online databases such as PubMed, ACM Digital Library, ScienceDirect, and Springer. To conduct this review, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines are used. Of the collected 62 studies, only 20 documents met the inclusion criteria. The review study identifies seven ocular cancer types. Important challenges associated with ocular cancer are highlighted, including limited awareness about eye cancer, restricted healthcare access, financial barriers, and insufficient infrastructure support. Financial barriers is one of the widely examined ocular cancer challenges in the literature. The potential role and limitations of ChatGPT are discussed, emphasizing its usefulness in providing general information to physicians, noting its inability to deliver up-to-date information. The paper concludes by presenting the potential future applications of ChatGPT to advance research on ocular cancer globally.
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Affiliation(s)
- Saud S. Alotaibi
- Information Systems Department, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Amna Rehman
- Department of Computer Science, Lahore Leads University, Lahore, Pakistan
| | - Muhammad Hasnain
- Department of Computer Science, Lahore Leads University, Lahore, Pakistan
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Li T, Stein J, Nallasamy N. Evaluation of the Nallasamy formula: a stacking ensemble machine learning method for refraction prediction in cataract surgery. Br J Ophthalmol 2023; 107:1066-1071. [PMID: 35379599 PMCID: PMC9530066 DOI: 10.1136/bjophthalmol-2021-320599] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 03/03/2022] [Indexed: 11/04/2022]
Abstract
AIMS To develop a new intraocular lens power selection method with improved accuracy for general cataract patients receiving Alcon SN60WF lenses. METHODS AND ANALYSIS A total of 5016 patients (6893 eyes) who underwent cataract surgery at University of Michigan's Kellogg Eye Center and received the Alcon SN60WF lens were included in the study. A machine learning-based method was developed using a training dataset of 4013 patients (5890 eyes), and evaluated on a testing dataset of 1003 patients (1003 eyes). The performance of our method was compared with that of Barrett Universal II, Emmetropia Verifying Optical (EVO), Haigis, Hoffer Q, Holladay 1, PearlDGS and SRK/T. RESULTS Mean absolute error (MAE) of the Nallasamy formula in the testing dataset was 0.312 Dioptres and the median absolute error (MedAE) was 0.242 D. Performance of existing methods were as follows: Barrett Universal II MAE=0.328 D, MedAE=0.256 D; EVO MAE=0.322 D, MedAE=0.251 D; Haigis MAE=0.363 D, MedAE=0.289 D; Hoffer Q MAE=0.404 D, MedAE=0.331 D; Holladay 1 MAE=0.371 D, MedAE=0.298 D; PearlDGS MAE=0.329 D, MedAE=0.258 D; SRK/T MAE=0.376 D, MedAE=0.300 D. The Nallasamy formula performed significantly better than seven existing methods based on the paired Wilcoxon test with Bonferroni correction (p<0.05). CONCLUSIONS The Nallasamy formula (available at https://lenscalc.com/) outperformed the seven other formulas studied on overall MAE, MedAE, and percentage of eyes within 0.5 D of prediction. Clinical significance may be primarily at the population level.
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Affiliation(s)
- Tingyang Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Joshua Stein
- Kellogg Eye Center, Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA
- Center for Eye Policy and Innovation, University of Michigan, Ann Arbor, Michigan, USA
- Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Nambi Nallasamy
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
- Kellogg Eye Center, Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA
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Scanzera AC, Beversluis C, Potharazu AV, Bai P, Leifer A, Cole E, Du DY, Musick H, Chan RVP. Planning an artificial intelligence diabetic retinopathy screening program: a human-centered design approach. Front Med (Lausanne) 2023; 10:1198228. [PMID: 37484841 PMCID: PMC10361413 DOI: 10.3389/fmed.2023.1198228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/16/2023] [Indexed: 07/25/2023] Open
Abstract
Diabetic retinopathy (DR) is a leading cause of vision loss in the United States and throughout the world. With early detection and treatment, sight-threatening sequelae from DR can be prevented. Although artificial intelligence (AI) based DR screening programs have been proven to be effective in identifying patients at high risk of vision loss, adoption of AI in clinical practice has been slow. We adapted the United Kingdom Design Council's Double-Diamond model to design a strategy for care delivery which integrates an AI-based screening program for DR into a primary care setting. Methods from human-centered design were used to develop a strategy for implementation informed by context-specific barriers and facilitators. The purpose of this community case study is to present findings from this work in progress, including a system of protocols, educational documents and workflows created using key stakeholder input.
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Affiliation(s)
- Angelica C. Scanzera
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, IL, United States
| | - Cameron Beversluis
- Institute for Healthcare Delivery Design, Office of Population Health Sciences, University of Illinois Chicago, Chicago, IL, United States
| | - Archit V. Potharazu
- Institute for Healthcare Delivery Design, Office of Population Health Sciences, University of Illinois Chicago, Chicago, IL, United States
| | - Patricia Bai
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, IL, United States
| | - Ariel Leifer
- Department of Family and Community Medicine, University of Illinois at Chicago, Chicago, IL, United States
| | - Emily Cole
- W.K. Kellogg Eye Center, University of Michigan, Ann Arbor, MI, United States
| | - David Yuzhou Du
- Segal Design Institute, Northwestern University, Evanston, IL, United States
| | - Hugh Musick
- Institute for Healthcare Delivery Design, Office of Population Health Sciences, University of Illinois Chicago, Chicago, IL, United States
| | - R. V. Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, IL, United States
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Wongchaisuwat P, Thamphithak R, Jitpukdee P, Wongchaisuwat N. Application of Deep Learning for Automated Detection of Polypoidal Choroidal Vasculopathy in Spectral Domain Optical Coherence Tomography. Transl Vis Sci Technol 2022; 11:16. [PMID: 36219163 PMCID: PMC9580222 DOI: 10.1167/tvst.11.10.16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Objective To develop an automated polypoidal choroidal vasculopathy (PCV) screening model to distinguish PCV from wet age-related macular degeneration (wet AMD). Methods A retrospective review of spectral domain optical coherence tomography (SD-OCT) images was undertaken. The included SD-OCT images were classified into two distinct categories (PCV or wet AMD) prior to the development of the PCV screening model. The automated detection of PCV using the developed model was compared with the results of gold-standard fundus fluorescein angiography and indocyanine green (FFA + ICG) angiography. A framework of SHapley Additive exPlanations was used to interpret the results from the model. Results A total of 2334 SD-OCT images were enrolled for training purposes, and an additional 1171 SD-OCT images were used for external validation. The ResNet attention model yielded superior performance with average area under the curve values of 0.8 and 0.81 for the training and external validation data sets, respectively. The sensitivity/specificity calculated at a patient level was 100%/60% and 85%/71% for the training and external validation data sets, respectively. Conclusions A conventional FFA + ICG investigation to differentiate PCV from wet AMD requires intense health care resources and adversely affects patients. A deep learning algorithm is proposed to automatically distinguish PCV from wet AMD. The developed algorithm exhibited promising performance for further development into an alternative PCV screening tool. Enhancement of the model's performance with additional data is needed prior to implementation of this diagnostic tool in real-world clinical practice. The invisibility of disease signs within SD-OCT images is the main limitation of the proposed model. Translational Relevance Basic research of deep learning algorithms was applied to differentiate PCV from wet AMD based on OCT images, benefiting a diagnosis process and minimizing a risk of ICG angiogram.
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Affiliation(s)
- Papis Wongchaisuwat
- Department of Industrial Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand
| | - Ranida Thamphithak
- Department of Ophthalmology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Peerakarn Jitpukdee
- Department of Industrial Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand
| | - Nida Wongchaisuwat
- Department of Ophthalmology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
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Scanzera AC, Shorter E, Kinnaird C, Valikodath N, Al-Khaled T, Cole E, Kravets S, Hallak JA, McMahon T, Chan RVP. Optometrist's perspectives of Artificial Intelligence in eye care. JOURNAL OF OPTOMETRY 2022; 15 Suppl 1:S91-S97. [PMID: 36137899 PMCID: PMC9732481 DOI: 10.1016/j.optom.2022.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 04/02/2022] [Accepted: 06/17/2022] [Indexed: 06/16/2023]
Abstract
PURPOSE The application of artificial intelligence (AI) in diagnosing and managing ocular disease has gained popularity as research highlights the utilization of AI to improve personalized medicine and healthcare outcomes. The objective of this study is to describe current optometric perspectives of AI in eye care. METHODS Members of the American Academy of Optometry were sent an electronic invitation to complete a 17-item survey. Survey items assessed perceived advantages and concerns regarding AI using a 5-point Likert scale ranging from "strongly agree" to "strongly disagree." RESULTS A total of 400 optometrists completed the survey. The mean number of years since optometry school completion was 25 ± 15.1. Most respondents reported familiarity with AI (66.8%). Though half of optometrists had concerns about the diagnostic accuracy of AI (53.0%), most believed it would improve the practice of optometry (72.0%). Optometrists reported their willingness to incorporate AI into practice increased from 53.3% before the COVID-19 pandemic to 65.5% after onset of the pandemic (p<0.001). CONCLUSION In this study, optometrists are optimistic about the use of AI in eye care, and willingness to incorporate AI in clinical practice also increased after the onset of the COVID-19 pandemic.
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Affiliation(s)
- Angelica C Scanzera
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States.
| | - Ellen Shorter
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States
| | - Charles Kinnaird
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States
| | - Nita Valikodath
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States
| | - Tala Al-Khaled
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States
| | - Emily Cole
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States
| | - Sasha Kravets
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States
| | - Joelle A Hallak
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States
| | - Timothy McMahon
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States
| | - R V Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1855 W. Taylor Street, Chicago, IL 60612, United States
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Al-Khaled T, Acaba-Berrocal L, Cole E, Ting DSW, Chiang MF, Chan RVP. Digital Education in Ophthalmology. Asia Pac J Ophthalmol (Phila) 2022; 11:267-272. [PMID: 34966034 PMCID: PMC9240107 DOI: 10.1097/apo.0000000000000484] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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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Ramesh PV, Subramaniam T, Ray P, Devadas AK, Ramesh SV, Ansar SM, Ramesh MK, Rajasekaran R, Parthasarathi S. Utilizing human intelligence in artificial intelligence for detecting glaucomatous fundus images using human-in-the-loop machine learning. Indian J Ophthalmol 2022; 70:1131-1138. [PMID: 35325999 PMCID: PMC9240493 DOI: 10.4103/ijo.ijo_2583_21] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Purpose: For diagnosing glaucomatous damage, we have employed a novel convolutional neural network (CNN) from TrueColor confocal fundus images to conquer the black box dilemma in artificial intelligence (AI). This neural network with CNN architecture with human-in-the-loop (HITL) data annotation helps not only in diagnosing glaucoma but also in predicting and locating detailed signs in the glaucomatous fundus, such as splinter hemorrhages, glaucomatous optic atrophy, vertical glaucomatous cupping, peripapillary atrophy, and retinal nerve fiber layer (RNFL) defect. Methods: The training was done on a well-curated private dataset of 1,400 high-resolution confocal fundus images, out of which 1,120 images (80%) were used exclusively for training and 280 images (20%) were used exclusively for testing. A custom trained You Only Look Once version 5 (YOLOv5)-based object detection methodology was used to identify the underlying conditions precisely. Twenty-six predefined medical conditions were annotated by a team of humans (comprising two glaucoma specialists and two optometrists) by using the Microsoft Visual Object Tagging Tool (VoTT) tool. The 280 testing images were split into three groups (90,100, and 90 images) for three test runs done once every 15 days. Results: Test results showed consistent increments in the accuracy, from 94.44% to 98.89%, in predicting the glaucoma diagnosis along with the detailed signs of the glaucomatous fundus Conclusion: Utilizing human intelligence in AI for detecting glaucomatous fundus images by using HITL machine learning has never been reported in the literature before. This AI model not only has good sensitivity and specificity in accurate glaucoma predictions but is also an explainable AI, thus overcoming the black box dilemma.
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Affiliation(s)
- Prasanna Venkatesh Ramesh
- Medical Officer, Department of Glaucoma and Research, Mahathma Eye Hospital Private Limited, Trichy, India
| | | | - Prajnya Ray
- Consultant Optometrist, Department of Optometry and Visual Science, Mahathma Eye Hospital Private Limited, Trichy, India
| | - Aji Kunnath Devadas
- Consultant Optometrist, Department of Optometry and Visual Science, Mahathma Eye Hospital Private Limited, Trichy, India
| | - Shruthy Vaishali Ramesh
- Medical Officer, Department of Cataract and Refractive Surgery, Mahathma Eye Hospital Private Limited, Trichy, India
| | | | - Meena Kumari Ramesh
- Head of the Department of Cataract and Refractive Surgery, Mahathma Eye Hospital Private Limited, Trichy, India
| | - Ramesh Rajasekaran
- Chief Medical Officer, Mahathma Eye Hospital Private Limited, Trichy, India
| | - Sathyan Parthasarathi
- Director, Sathyan Eye Care Hospital and Coimbatore Glaucoma Foundation, Coimbatore, Tamil Nadu, India
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Akkara JD, Kuriakose A. Commentary: Water, water everywhere; alters eye when you drink. Indian J Ophthalmol 2022; 70:1230-1231. [PMID: 35326022 PMCID: PMC9240575 DOI: 10.4103/ijo.ijo_3041_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Affiliation(s)
- John D Akkara
- Department of Ophthalmology, Sri Ramachandra Institute of Higher Education and Research, Chennai, Tamil Nadu; Department of Glaucoma, Westend Eye Hospital, Cochin, Kerala, India
| | - Anju Kuriakose
- Department of Retina, Aravind Eye Hospital, Chennai, Tamil Nadu, India
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Juaristi L, Irigoyen C, Chapartegui J, Guibelalde A, Mar J. Assessing the Utility and Patient Satisfaction of Virtual Retina Clinics During COVID-19 Pandemic. Clin Ophthalmol 2022; 16:311-321. [PMID: 35173410 PMCID: PMC8841596 DOI: 10.2147/opth.s349939] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 01/13/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose To explore whether the virtual retina clinic (VRC) has been a useful and safe platform for monitoring retinal diseases during the COVID-19 pandemic and assessing patient satisfaction. Methods A prospective observational study was conducted for patients with stable retinal diseases in Donostia University Hospital’s Ophthalmology Service during the pandemic. All patients were assessed in the VRC with optical coherence tomography of the macula and widefield retinography, plus visual field tests in hydroxychloroquine retinopathy screenings. The VRC´s effectiveness was evaluated with repeat blind assessments and patient satisfaction with an adapted SERVQUAL scale. Results The most common diseases were diabetic retinopathy (30.3%) and age-related macular degeneration (21.8%). Most patients (74%) were eligible to continue in the VRC, 19.3% were referred to face-to-face (F2F) appointments and 6.6% were discharged. Patients underwent repeat blind assessments in F2F appointments to monitor VRC performance in 23.7% of the cases. The sensitivity to detect disease progression was 100%. The specificity was 80.1%. The VRC took half the time. The patient overall satisfaction rating was 9.8/10. Conclusion The VRC, as an additional platform, supports F2F appointments. Almost three-quarters of patients could continue being safely seen in the VRC. The virtual approach decreases SARS-CoV-2 exposure. Patient satisfaction is very good. Translational Relevance The VRC enables us to attend patients safely with decreased SARS-CoV-2 exposure.
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Affiliation(s)
- Leire Juaristi
- Department of Ophthalmology, Donostia Unibertsitate Ospitalea - Hospital Universitario Donostia (HUD), Donostia San-Sebastian, Gipuzkoa, Spain
- Biodonostia Health Research Institute, Donostia-San Sebastián, Gipuzkoa, Spain
| | - Cristina Irigoyen
- Department of Ophthalmology, Donostia Unibertsitate Ospitalea - Hospital Universitario Donostia (HUD), Donostia San-Sebastian, Gipuzkoa, Spain
- Biodonostia Health Research Institute, Donostia-San Sebastián, Gipuzkoa, Spain
- Department of Ophthalmology, University of the Basque Country, Basque Country, Spain
- Correspondence: Cristina Irigoyen, Email
| | - Jaione Chapartegui
- Department of Ophthalmology, Donostia Unibertsitate Ospitalea - Hospital Universitario Donostia (HUD), Donostia San-Sebastian, Gipuzkoa, Spain
- Biodonostia Health Research Institute, Donostia-San Sebastián, Gipuzkoa, Spain
| | - Ane Guibelalde
- Department of Ophthalmology, Donostia Unibertsitate Ospitalea - Hospital Universitario Donostia (HUD), Donostia San-Sebastian, Gipuzkoa, Spain
- Department of Ophthalmology, University of the Basque Country, Basque Country, Spain
| | - Javier Mar
- Biodonostia Health Research Institute, Donostia-San Sebastián, Gipuzkoa, Spain
- Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Gipuzkoa, Spain
- Kronikgune Institute for Health Service Research, Barakaldo, Bizkaia, Spain
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Akkara JD, Kuriakose A. Commentary: Intravitreal injection of formalin as a life hack for ophthalmic wet lab training. Indian J Ophthalmol 2021; 69:3755-3756. [PMID: 34827039 PMCID: PMC8837281 DOI: 10.4103/ijo.ijo_1682_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- John D Akkara
- Department of Ophthalmology, Sri Ramachandra Institute of Higher Education and Research, Chennai, Tamil Nadu; Department of Glaucoma, Westend Eye Hospital, Cochin, Kerala, India
| | - Anju Kuriakose
- Department of Retina, Aravind Eye Hospital, Chennai, Tamil Nadu, India
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Gupta S, Schneider MJ, Vardhan SA, Ravilla T. Use of predictive models to identify patients who are likely to benefit from refraction at a follow-up visit after cataract surgery. Indian J Ophthalmol 2021; 69:2695-2701. [PMID: 34571618 PMCID: PMC8597443 DOI: 10.4103/ijo.ijo_661_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 06/08/2021] [Accepted: 06/13/2021] [Indexed: 11/15/2022] Open
Abstract
PURPOSE To develop predictive models to identify cataract surgery patients who are more likely to benefit from refraction at a four-week postoperative exam. METHODS In this retrospective study, we used data of all 86,776 cataract surgeries performed in 2015 at a large tertiary-care eye hospital in India. The outcome variable was a binary indicator of whether the difference between corrected distance visual acuity and uncorrected visual acuity at the four-week postoperative exam was at least two lines on the Snellen chart. We examined the following statistical models: logistic regression, decision tree, pruned decision tree, random forest, weighted k-nearest neighbor, and a neural network. Predictor variables included in each model were patient sex and age, source eye (left or right), preoperative visual acuity, first-day postoperative visual acuity, intraoperative and immediate postoperative complications, and combined surgeries. We compared the predictive performance of models and assessed their clinical impact in test samples. RESULTS All models demonstrated predictive accuracy better than chance based on area under the receiver operating characteristic curve. In a targeting exercise with a fixed intervention budget, we found that gains from predictive models in identifying patients who would benefit from refraction ranged from 7.8% (increase from 1500 to 1617 patients) to 74% (increase from 250 to 435 patients). CONCLUSION The use of predictive statistical models to identify patients who are likely to benefit from refraction at follow-up can improve the economic efficiency of interventions. Simpler models like logistic regression perform almost as well as more complex machine-learning models, but are easier to implement.
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Affiliation(s)
- Sachin Gupta
- SC Johnson College of Business, Cornell University, Ithaca, NY, USA
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Ajitha S, Akkara JD, Judy MV. Identification of glaucoma from fundus images using deep learning techniques. Indian J Ophthalmol 2021; 69:2702-2709. [PMID: 34571619 PMCID: PMC8597466 DOI: 10.4103/ijo.ijo_92_21] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Purpose Glaucoma is one of the preeminent causes of incurable visual disability and blindness across the world due to elevated intraocular pressure within the eyes. Accurate and timely diagnosis is essential for preventing visual disability. Manual detection of glaucoma is a challenging task that needs expertise and years of experience. Methods In this paper, we suggest a powerful and accurate algorithm using a convolutional neural network (CNN) for the automatic diagnosis of glaucoma. In this work, 1113 fundus images consisting of 660 normal and 453 glaucomatous images from four databases have been used for the diagnosis of glaucoma. A 13-layer CNN is potently trained from this dataset to mine vital features, and these features are classified into either glaucomatous or normal class during testing. The proposed algorithm is implemented in Google Colab, which made the task straightforward without spending hours installing the environment and supporting libraries. To evaluate the effectiveness of our algorithm, the dataset is divided into 70% for training, 20% for validation, and the remaining 10% utilized for testing. The training images are augmented to 12012 fundus images. Results Our model with SoftMax classifier achieved an accuracy of 93.86%, sensitivity of 85.42%, specificity of 100%, and precision of 100%. In contrast, the model with the SVM classifier achieved accuracy, sensitivity, specificity, and precision of 95.61, 89.58, 100, and 100%, respectively. Conclusion These results demonstrate the ability of the deep learning model to identify glaucoma from fundus images and suggest that the proposed system can help ophthalmologists in a fast, accurate, and reliable diagnosis of glaucoma.
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Affiliation(s)
- S Ajitha
- Department of Computer Applications, Cochin University of Science and Technology, Kochi, Kerala, India
| | - John D Akkara
- Ophthalmology Department, Sri Ramachandra Institute of Higher Education and Research, Chennai, Tamil Nadu, India
| | - M V Judy
- Department of Computer Applications, Cochin University of Science and Technology, Kochi, Kerala, India
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Akkara JD, Kuriakose A. Commentary: Is it time for teleophthalmology, virtual glaucoma clinics and uberization of eye care? Indian J Ophthalmol 2021; 69:719-720. [PMID: 33595508 PMCID: PMC7942080 DOI: 10.4103/ijo.ijo_3823_20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Affiliation(s)
- John Davis Akkara
- Consultant Ophthalmologist, Department of Ophthalmology, Sri Ramachandra Institute of Higher Education and Research, Cochin, Kerala, India
- Department of Glaucoma, Westend Eye Hospital, Cochin, Kerala, India
| | - Anju Kuriakose
- Sri Ramachandra Institute of Higher Education and Research, Chennai, Tamil Nadu, India
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Akkara J, Prasher P, Singh B, Vig V. Smartphone microscope in eye clinic to visualize fungus and Demodex. KERALA JOURNAL OF OPHTHALMOLOGY 2021. [DOI: 10.4103/kjo.kjo_125_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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Agarwal D, Kumar A. Commentary: Artificial intelligence in ophthalmology: Potential challenges and way ahead. Indian J Ophthalmol 2020; 68:1347-1348. [PMID: 32587161 PMCID: PMC7574123 DOI: 10.4103/ijo.ijo_737_20] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Affiliation(s)
- Divya Agarwal
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | - Atul Kumar
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
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Akkara JD, Kuriakose A. Commentary: Artificial intelligence for everything: Can we trust it? Indian J Ophthalmol 2020; 68:1346-1347. [PMID: 32587160 PMCID: PMC7574113 DOI: 10.4103/ijo.ijo_216_20] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Affiliation(s)
- John Davis Akkara
- Department of Ophthalmology, Little Flower Hospital and Research Centre; Angamaly and Glaucoma Department, Westend Eye Hospital, Cochin, Kerala, India
| | - Anju Kuriakose
- Department of Ophthalmology, Jubilee Mission Medical College, Thrissur, Kerala, India
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Akkara JD, Kuriakose A. Commentary: Gamifying teleconsultation during COVID-19 lockdown. Indian J Ophthalmol 2020; 68:1013-1014. [PMID: 32461417 PMCID: PMC7508151 DOI: 10.4103/ijo.ijo_1495_20] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Affiliation(s)
- John D Akkara
- Department of Ophthalmology, Little Flower Hospital and Research Centre, Angamaly, Cochin, Kerala, India
- Cataract and Glaucoma Services, Westend Eye Hospital, Cochin, Kerala, India
| | - Anju Kuriakose
- Department of Retina, Aravind Eye Hospital, Chennai, Tamil Nadu, India
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Ruamviboonsuk P, Cheung CY, Zhang X, Raman R, Park SJ, Ting DSW. Artificial Intelligence in Ophthalmology: Evolutions in Asia. Asia Pac J Ophthalmol (Phila) 2020; 9:78-84. [PMID: 32349114 DOI: 10.1097/01.apo.0000656980.41190.bf] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Artificial intelligence (AI) has been studied in ophthalmology since availability of digital information in ophthalmic care. The significant turning point was availability of commercial digital color fundus photography in the late 1990s, which caused digital screening for diabetic retinopathy (DR) to take off. Automated Retinal Disease Assessment software was then developed using machine learning to detect abnormal lesions in fundus to screen DR. The use of this version of AI had not been generalized because the specificity at 45% was not high enough, although the sensitivity reached 90%. The recent breakthrough in machine learning is the invent of deep learning, which accelerates its performance to be on par with experts. The first 2 breakthrough studies on deep learning for screening DR were conducted in Asia. The first represented collaboration of datasets between Asia and the United States for algorithms development, whereas the second represented algorithms developed in Asia but validated in different populations across the world. Both found accuracy for detecting referable DR of >95%. Diversity and variety are unique strengths of Asia for AI studies. There are many more studies of AI ongoing in Asia not only as prospective deployments in DR but in glaucoma, age-related macular degeneration, cataract, and systemic disease, such as Alzheimer's disease. Some Asian countries have laid out plans for digital health care system using AI as one of the puzzle pieces for solving blindness. More studies on AI and digital health are expected to come from Asia in this new decade.
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Affiliation(s)
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong
| | - Xiulan Zhang
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, India
| | - Sang Jun Park
- Duke-NUS Medical School Consultant, Vitreo-retinal Department, Singapore National Eye Center, Singapore
| | - Daniel Shu Wei Ting
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea
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Akkara JD, Kuriakose A. Commentary: Teleophthalmology and electronic medical records: Weighing the pros and cons of unavoidable progress. Indian J Ophthalmol 2020; 68:367-368. [PMID: 31957729 PMCID: PMC7003609 DOI: 10.4103/ijo.ijo_2082_19] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- John Davis Akkara
- Department of Ophthalmology, Little Flower Hospital and Research Centre, Angamaly, Kerala, India
- Glaucoma Department, Westend Eye Hospital, Cochin, Kerala, India
| | - Anju Kuriakose
- Department of Ophthalmology, Jubilee Mission Medical College, Thrissur, Kerala, India
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