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Savoy FM, Rao DP, Toh JK, Ong B, Sivaraman A, Sharma A, Das T. Empowering Portable Age-Related Macular Degeneration Screening: Evaluation of a Deep Learning Algorithm for a Smartphone Fundus Camera. BMJ Open 2024; 14:e081398. [PMID: 39237272 DOI: 10.1136/bmjopen-2023-081398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/07/2024] Open
Abstract
OBJECTIVES Despite global research on early detection of age-related macular degeneration (AMD), not enough is being done for large-scale screening. Automated analysis of retinal images captured via smartphone presents a potential solution; however, to our knowledge, such an artificial intelligence (AI) system has not been evaluated. The study aimed to assess the performance of an AI algorithm in detecting referable AMD on images captured on a portable fundus camera. DESIGN, SETTING A retrospective image database from the Age-Related Eye Disease Study (AREDS) and target device was used. PARTICIPANTS The algorithm was trained on two distinct data sets with macula-centric images: initially on 108,251 images (55% referable AMD) from AREDS and then fine-tuned on 1108 images (33% referable AMD) captured on Asian eyes using the target device. The model was designed to indicate the presence of referable AMD (intermediate and advanced AMD). Following the first training step, the test set consisted of 909 images (49% referable AMD). For the fine-tuning step, the test set consisted of 238 (34% referable AMD) images. The reference standard for the AREDS data set was fundus image grading by the central reading centre, and for the target device, it was consensus image grading by specialists. OUTCOME MEASURES Area under receiver operating curve (AUC), sensitivity and specificity of algorithm. RESULTS Before fine-tuning, the deep learning (DL) algorithm exhibited a test set (from AREDS) sensitivity of 93.48% (95% CI: 90.8% to 95.6%), specificity of 82.33% (95% CI: 78.6% to 85.7%) and AUC of 0.965 (95% CI:0.95 to 0.98). After fine-tuning, the DL algorithm displayed a test set (from the target device) sensitivity of 91.25% (95% CI: 82.8% to 96.4%), specificity of 84.18% (95% CI: 77.5% to 89.5%) and AUC 0.947 (95% CI: 0.911 to 0.982). CONCLUSION The DL algorithm shows promising results in detecting referable AMD from a portable smartphone-based imaging system. This approach can potentially bring effective and affordable AMD screening to underserved areas.
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Affiliation(s)
| | | | - Jun Kai Toh
- Medios Technologies, Remidio Innovative Solutions, Singapore
| | - Bryan Ong
- Medios Technologies, Remidio Innovative Solutions, Singapore
| | - Anand Sivaraman
- Remidio Innovative Solutions Pvt Ltd, Bangalore, Karnataka, India
| | - Ashish Sharma
- Lotus Eye Care Hospital and Institute, Coimbatore, Tamil Nadu, India
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Martin E, Cook AG, Frost SM, Turner AW, Chen FK, McAllister IL, Nolde JM, Schlaich MP. Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs. Eye (Lond) 2024; 38:2581-2588. [PMID: 38734746 DOI: 10.1038/s41433-024-03085-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 04/03/2024] [Accepted: 04/11/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND/OBJECTIVES Artificial intelligence can assist with ocular image analysis for screening and diagnosis, but it is not yet capable of autonomous full-spectrum screening. Hypothetically, false-positive results may have unrealized screening potential arising from signals persisting despite training and/or ambiguous signals such as from biomarker overlap or high comorbidity. The study aimed to explore the potential to detect clinically useful incidental ocular biomarkers by screening fundus photographs of hypertensive adults using diabetic deep learning algorithms. SUBJECTS/METHODS Patients referred for treatment-resistant hypertension were imaged at a hospital unit in Perth, Australia, between 2016 and 2022. The same 45° colour fundus photograph selected for each of the 433 participants imaged was processed by three deep learning algorithms. Two expert retinal specialists graded all false-positive results for diabetic retinopathy in non-diabetic participants. RESULTS Of the 29 non-diabetic participants misclassified as positive for diabetic retinopathy, 28 (97%) had clinically useful retinal biomarkers. The models designed to screen for fewer diseases captured more incidental disease. All three algorithms showed a positive correlation between severity of hypertensive retinopathy and misclassified diabetic retinopathy. CONCLUSIONS The results suggest that diabetic deep learning models may be responsive to hypertensive and other clinically useful retinal biomarkers within an at-risk, hypertensive cohort. Observing that models trained for fewer diseases captured more incidental pathology increases confidence in signalling hypotheses aligned with using self-supervised learning to develop autonomous comprehensive screening. Meanwhile, non-referable and false-positive outputs of other deep learning screening models could be explored for immediate clinical use in other populations.
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Affiliation(s)
- Eve Martin
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Kensington, WA, Australia.
- School of Population and Global Health, The University of Western Australia, Crawley, Australia.
- Dobney Hypertension Centre - Royal Perth Hospital Unit, Medical School, The University of Western Australia, Perth, Australia.
- Australian e-Health Research Centre, Floreat, WA, Australia.
| | - Angus G Cook
- School of Population and Global Health, The University of Western Australia, Crawley, Australia
| | - Shaun M Frost
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Kensington, WA, Australia
- Australian e-Health Research Centre, Floreat, WA, Australia
| | - Angus W Turner
- Lions Eye Institute, Nedlands, WA, Australia
- Centre for Ophthalmology and Visual Science, The University of Western Australia, Perth, Australia
| | - Fred K Chen
- Lions Eye Institute, Nedlands, WA, Australia
- Centre for Ophthalmology and Visual Science, The University of Western Australia, Perth, Australia
- Centre for Eye Research Australia, The Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, The University of Melbourne, East Melbourne, VIC, Australia
- Ophthalmology Department, Royal Perth Hospital, Perth, Australia
| | - Ian L McAllister
- Lions Eye Institute, Nedlands, WA, Australia
- Centre for Ophthalmology and Visual Science, The University of Western Australia, Perth, Australia
| | - Janis M Nolde
- Dobney Hypertension Centre - Royal Perth Hospital Unit, Medical School, The University of Western Australia, Perth, Australia
- Departments of Cardiology and Nephrology, Royal Perth Hospital, Perth, Australia
| | - Markus P Schlaich
- Dobney Hypertension Centre - Royal Perth Hospital Unit, Medical School, The University of Western Australia, Perth, Australia
- Departments of Cardiology and Nephrology, Royal Perth Hospital, Perth, Australia
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Driban M, Yan A, Selvam A, Ong J, Vupparaboina KK, Chhablani J. Artificial intelligence in chorioretinal pathology through fundoscopy: a comprehensive review. Int J Retina Vitreous 2024; 10:36. [PMID: 38654344 PMCID: PMC11036694 DOI: 10.1186/s40942-024-00554-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Applications for artificial intelligence (AI) in ophthalmology are continually evolving. Fundoscopy is one of the oldest ocular imaging techniques but remains a mainstay in posterior segment imaging due to its prevalence, ease of use, and ongoing technological advancement. AI has been leveraged for fundoscopy to accomplish core tasks including segmentation, classification, and prediction. MAIN BODY In this article we provide a review of AI in fundoscopy applied to representative chorioretinal pathologies, including diabetic retinopathy and age-related macular degeneration, among others. We conclude with a discussion of future directions and current limitations. SHORT CONCLUSION As AI evolves, it will become increasingly essential for the modern ophthalmologist to understand its applications and limitations to improve patient outcomes and continue to innovate.
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Affiliation(s)
- Matthew Driban
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Audrey Yan
- Department of Medicine, West Virginia School of Osteopathic Medicine, Lewisburg, WV, USA
| | - Amrish Selvam
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Joshua Ong
- Michigan Medicine, University of Michigan, Ann Arbor, USA
| | | | - Jay Chhablani
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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Choi JY, Ryu IH, Kim JK, Lee IS, Yoo TK. Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography. BMC Med Inform Decis Mak 2024; 24:25. [PMID: 38273286 PMCID: PMC10811871 DOI: 10.1186/s12911-024-02431-4] [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: 07/29/2023] [Accepted: 01/17/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND The epiretinal membrane (ERM) is a common retinal disorder characterized by abnormal fibrocellular tissue at the vitreomacular interface. Most patients with ERM are asymptomatic at early stages. Therefore, screening for ERM will become increasingly important. Despite the high prevalence of ERM, few deep learning studies have investigated ERM detection in the color fundus photography (CFP) domain. In this study, we built a generative model to enhance ERM detection performance in the CFP. METHODS This deep learning study retrospectively collected 302 ERM and 1,250 healthy CFP data points from a healthcare center. The generative model using StyleGAN2 was trained using single-center data. EfficientNetB0 with StyleGAN2-based augmentation was validated using independent internal single-center data and external datasets. We randomly assigned healthcare center data to the development (80%) and internal validation (20%) datasets. Data from two publicly accessible sources were used as external validation datasets. RESULTS StyleGAN2 facilitated realistic CFP synthesis with the characteristic cellophane reflex features of the ERM. The proposed method with StyleGAN2-based augmentation outperformed the typical transfer learning without a generative adversarial network. The proposed model achieved an area under the receiver operating characteristic (AUC) curve of 0.926 for internal validation. AUCs of 0.951 and 0.914 were obtained for the two external validation datasets. Compared with the deep learning model without augmentation, StyleGAN2-based augmentation improved the detection performance and contributed to the focus on the location of the ERM. CONCLUSIONS We proposed an ERM detection model by synthesizing realistic CFP images with the pathological features of ERM through generative deep learning. We believe that our deep learning framework will help achieve a more accurate detection of ERM in a limited data setting.
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Affiliation(s)
- Joon Yul Choi
- Department of Biomedical Engineering, Yonsei University, Wonju, South Korea
| | - Ik Hee Ryu
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
- Research and development department, VISUWORKS, Seoul, South Korea
| | - Jin Kuk Kim
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
- Research and development department, VISUWORKS, Seoul, South Korea
| | - In Sik Lee
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
| | - Tae Keun Yoo
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea.
- Research and development department, VISUWORKS, Seoul, South Korea.
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Zhang J, Zou H. Insights into artificial intelligence in myopia management: from a data perspective. Graefes Arch Clin Exp Ophthalmol 2024; 262:3-17. [PMID: 37231280 PMCID: PMC10212230 DOI: 10.1007/s00417-023-06101-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 03/23/2023] [Accepted: 05/06/2023] [Indexed: 05/27/2023] Open
Abstract
Given the high incidence and prevalence of myopia, the current healthcare system is struggling to handle the task of myopia management, which is worsened by home quarantine during the ongoing COVID-19 pandemic. The utilization of artificial intelligence (AI) in ophthalmology is thriving, yet not enough in myopia. AI can serve as a solution for the myopia pandemic, with application potential in early identification, risk stratification, progression prediction, and timely intervention. The datasets used for developing AI models are the foundation and determine the upper limit of performance. Data generated from clinical practice in managing myopia can be categorized into clinical data and imaging data, and different AI methods can be used for analysis. In this review, we comprehensively review the current application status of AI in myopia with an emphasis on data modalities used for developing AI models. We propose that establishing large public datasets with high quality, enhancing the model's capability of handling multimodal input, and exploring novel data modalities could be of great significance for the further application of AI for myopia.
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Affiliation(s)
- Juzhao Zhang
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haidong Zou
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Shanghai Eye Diseases Prevention & Treatment Center, Shanghai Eye Hospital, Shanghai, China.
- National Clinical Research Center for Eye Diseases, Shanghai, China.
- Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.
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Komatsu K, Sano K, Fukai K, Nakagawa R, Nakagawa T, Tatemichi M, Nakano T. Associated factors of diabetic retinopathy by artificial intelligence evaluation of fundus images in Japan. Sci Rep 2023; 13:19742. [PMID: 37957353 PMCID: PMC10643360 DOI: 10.1038/s41598-023-47270-x] [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: 07/21/2023] [Accepted: 11/11/2023] [Indexed: 11/15/2023] Open
Abstract
This cross-sectional study aimed to investigate the promoting and inhibitory factors of diabetic retinopathy (DR) according to diabetes mellitus (DM) stage using standardized evaluation of fundus images by artificial intelligence (AI). A total of 30,167 participants underwent blood and fundus examinations at a health screening facility in Japan (2015-2016). Fundus photographs were screened by the AI software, RetCAD and DR scores (DRSs) were quantified. The presence of DR was determined by setting two cut-off values prioritizing sensitivity or specificity. DM was defined as four stages (no DM: DM0; advanced DM: DM3) based on treatment history and hemoglobin A1c (HbA1c) levels. Associated factors of DR were identified using logistic regression analysis. For cutoff values, multivariate analysis revealed age, sex, systolic blood pressure (SBP), smoking, urinary protein, and HbA1c level as positively associated with the risk of DR among all DM stages. In addition to glycemic control, SBP and Fibrosis-4 index might act as promoting factors for DR at all or an earlier DM stage. T-Bil, cholinesterase, and T-cho level might be protective factors at an advanced DM stage.
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Affiliation(s)
- Koji Komatsu
- Department of Ophthalmology, The Jikei University School of Medicine, 3-25-8 Nishi-Shimbashi, Minato-Ku, Tokyo, 105-8461, Japan.
| | - Kei Sano
- Department of Ophthalmology, The Jikei University School of Medicine, 3-25-8 Nishi-Shimbashi, Minato-Ku, Tokyo, 105-8461, Japan
- Department of Preventive Medicine, School of Medicine, Tokai University, Kanagawa, Japan
| | - Kota Fukai
- Department of Preventive Medicine, School of Medicine, Tokai University, Kanagawa, Japan
| | | | | | - Masayuki Tatemichi
- Department of Preventive Medicine, School of Medicine, Tokai University, Kanagawa, Japan
| | - Tadashi Nakano
- Department of Ophthalmology, The Jikei University School of Medicine, 3-25-8 Nishi-Shimbashi, Minato-Ku, Tokyo, 105-8461, Japan
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Abdulazeem H, Whitelaw S, Schauberger G, Klug SJ. A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data. PLoS One 2023; 18:e0274276. [PMID: 37682909 PMCID: PMC10491005 DOI: 10.1371/journal.pone.0274276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 08/29/2023] [Indexed: 09/10/2023] Open
Abstract
With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models' development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer's disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.
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Affiliation(s)
- Hebatullah Abdulazeem
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
| | - Sera Whitelaw
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
| | - Gunther Schauberger
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
| | - Stefanie J. Klug
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
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Zhelev Z, Peters J, Rogers M, Allen M, Kijauskaite G, Seedat F, Wilkinson E, Hyde C. Test accuracy of artificial intelligence-based grading of fundus images in diabetic retinopathy screening: A systematic review. J Med Screen 2023; 30:97-112. [PMID: 36617971 PMCID: PMC10399100 DOI: 10.1177/09691413221144382] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/14/2022] [Accepted: 11/18/2022] [Indexed: 01/10/2023]
Abstract
OBJECTIVES To systematically review the accuracy of artificial intelligence (AI)-based systems for grading of fundus images in diabetic retinopathy (DR) screening. METHODS We searched MEDLINE, EMBASE, the Cochrane Library and the ClinicalTrials.gov from 1st January 2000 to 27th August 2021. Accuracy studies published in English were included if they met the pre-specified inclusion criteria. Selection of studies for inclusion, data extraction and quality assessment were conducted by one author with a second reviewer independently screening and checking 20% of titles. Results were analysed narratively. RESULTS Forty-three studies evaluating 15 deep learning (DL) and 4 machine learning (ML) systems were included. Nine systems were evaluated in a single study each. Most studies were judged to be at high or unclear risk of bias in at least one QUADAS-2 domain. Sensitivity for referable DR and higher grades was ≥85% while specificity varied and was <80% for all ML systems and in 6/31 studies evaluating DL systems. Studies reported high accuracy for detection of ungradable images, but the latter were analysed and reported inconsistently. Seven studies reported that AI was more sensitive but less specific than human graders. CONCLUSIONS AI-based systems are more sensitive than human graders and could be safe to use in clinical practice but have variable specificity. However, for many systems evidence is limited, at high risk of bias and may not generalise across settings. Therefore, pre-implementation assessment in the target clinical pathway is essential to obtain reliable and applicable accuracy estimates.
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Affiliation(s)
- Zhivko Zhelev
- Exeter Test Group, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Jaime Peters
- Exeter Test Group, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Morwenna Rogers
- NIHR ARC South West Peninsula (PenARC), University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Michael Allen
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | | | | | | | - Christopher Hyde
- Exeter Test Group, University of Exeter Medical School, University of Exeter, Exeter, UK
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Chou YB, Kale AU, Lanzetta P, Aslam T, Barratt J, Danese C, Eldem B, Eter N, Gale R, Korobelnik JF, Kozak I, Li X, Li X, Loewenstein A, Ruamviboonsuk P, Sakamoto T, Ting DS, van Wijngaarden P, Waldstein SM, Wong D, Wu L, Zapata MA, Zarranz-Ventura J. Current status and practical considerations of artificial intelligence use in screening and diagnosing retinal diseases: Vision Academy retinal expert consensus. Curr Opin Ophthalmol 2023; 34:403-413. [PMID: 37326222 PMCID: PMC10399944 DOI: 10.1097/icu.0000000000000979] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
PURPOSE OF REVIEW The application of artificial intelligence (AI) technologies in screening and diagnosing retinal diseases may play an important role in telemedicine and has potential to shape modern healthcare ecosystems, including within ophthalmology. RECENT FINDINGS In this article, we examine the latest publications relevant to AI in retinal disease and discuss the currently available algorithms. We summarize four key requirements underlining the successful application of AI algorithms in real-world practice: processing massive data; practicability of an AI model in ophthalmology; policy compliance and the regulatory environment; and balancing profit and cost when developing and maintaining AI models. SUMMARY The Vision Academy recognizes the advantages and disadvantages of AI-based technologies and gives insightful recommendations for future directions.
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Affiliation(s)
- Yu-Bai Chou
- Department of Ophthalmology, Taipei Veterans General Hospital
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Aditya U. Kale
- Academic Unit of Ophthalmology, Institute of Inflammation & Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Paolo Lanzetta
- Department of Medicine – Ophthalmology, University of Udine
- Istituto Europeo di Microchirurgia Oculare, Udine, Italy
| | - Tariq Aslam
- Division of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, University of Manchester School of Health Sciences, Manchester, UK
| | - Jane Barratt
- International Federation on Ageing, Toronto, Canada
| | - Carla Danese
- Department of Medicine – Ophthalmology, University of Udine
- Department of Ophthalmology, AP-HP Hôpital Lariboisière, Université Paris Cité, Paris, France
| | - Bora Eldem
- Department of Ophthalmology, Hacettepe University, Ankara, Turkey
| | - Nicole Eter
- Department of Ophthalmology, University of Münster Medical Center, Münster, Germany
| | - Richard Gale
- Department of Ophthalmology, York Teaching Hospital NHS Foundation Trust, York, UK
| | - Jean-François Korobelnik
- Service d’ophtalmologie, CHU Bordeaux
- University of Bordeaux, INSERM, BPH, UMR1219, F-33000 Bordeaux, France
| | - Igor Kozak
- Moorfields Eye Hospital Centre, Abu Dhabi, UAE
| | - Xiaorong Li
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin
| | - Xiaoxin Li
- Xiamen Eye Center, Xiamen University, Xiamen, China
| | - Anat Loewenstein
- Division of Ophthalmology, Tel Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Paisan Ruamviboonsuk
- Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
| | - Taiji Sakamoto
- Department of Ophthalmology, Kagoshima University, Kagoshima, Japan
| | - Daniel S.W. Ting
- Singapore National Eye Center, Duke-NUS Medical School, Singapore
| | - Peter van Wijngaarden
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | | | - David Wong
- Unity Health Toronto – St. Michael's Hospital, University of Toronto, Toronto, Canada
| | - Lihteh Wu
- Macula, Vitreous and Retina Associates of Costa Rica, San José, Costa Rica
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Matta S, Lamard M, Conze PH, Le Guilcher A, Lecat C, Carette R, Basset F, Massin P, Rottier JB, Cochener B, Quellec G. Towards population-independent, multi-disease detection in fundus photographs. Sci Rep 2023; 13:11493. [PMID: 37460629 DOI: 10.1038/s41598-023-38610-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 07/11/2023] [Indexed: 07/20/2023] Open
Abstract
Independent validation studies of automatic diabetic retinopathy screening systems have recently shown a drop of screening performance on external data. Beyond diabetic retinopathy, this study investigates the generalizability of deep learning (DL) algorithms for screening various ocular anomalies in fundus photographs, across heterogeneous populations and imaging protocols. The following datasets are considered: OPHDIAT (France, diabetic population), OphtaMaine (France, general population), RIADD (India, general population) and ODIR (China, general population). Two multi-disease DL algorithms were developed: a Single-Dataset (SD) network, trained on the largest dataset (OPHDIAT), and a Multiple-Dataset (MD) network, trained on multiple datasets simultaneously. To assess their generalizability, both algorithms were evaluated whenever training and test data originate from overlapping datasets or from disjoint datasets. The SD network achieved a mean per-disease area under the receiver operating characteristic curve (mAUC) of 0.9571 on OPHDIAT. However, it generalized poorly to the other three datasets (mAUC < 0.9). When all four datasets were involved in training, the MD network significantly outperformed the SD network (p = 0.0058), indicating improved generality. However, in leave-one-dataset-out experiments, performance of the MD network was significantly lower on populations unseen during training than on populations involved in training (p < 0.0001), indicating imperfect generalizability.
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Affiliation(s)
- Sarah Matta
- Université de Bretagne Occidentale, Brest, Bretagne, France.
- INSERM, UMR 1101, Brest, F-29 200, France.
| | - Mathieu Lamard
- Université de Bretagne Occidentale, Brest, Bretagne, France
- INSERM, UMR 1101, Brest, F-29 200, France
| | - Pierre-Henri Conze
- INSERM, UMR 1101, Brest, F-29 200, France
- IMT Atlantique, Brest, F-29200, France
| | | | - Clément Lecat
- Evolucare Technologies, Villers-Bretonneux, F-80800, France
| | | | - Fabien Basset
- Evolucare Technologies, Villers-Bretonneux, F-80800, France
| | - Pascale Massin
- Service d'Ophtalmologie, Hôpital Lariboisière, APHP, Paris, F-75475, France
| | - Jean-Bernard Rottier
- Bâtiment de consultation porte 14 Pôle Santé Sud CMCM, 28 Rue de Guetteloup, Le Mans, F-72100, France
| | - Béatrice Cochener
- Université de Bretagne Occidentale, Brest, Bretagne, France
- INSERM, UMR 1101, Brest, F-29 200, France
- Service d'Ophtalmologie, CHRU Brest, Brest, F-29200, France
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Meredith S, van Grinsven M, Engelberts J, Clarke D, Prior V, Vodrey J, Hammond A, Muhammed R, Kirby P. Performance of an artificial intelligence automated system for diabetic eye screening in a large English population. Diabet Med 2023; 40:e15055. [PMID: 36719266 DOI: 10.1111/dme.15055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/20/2023] [Accepted: 01/30/2023] [Indexed: 02/01/2023]
Abstract
AIMS A diabetic eye screening programme has huge value in reducing avoidable sight loss by identifying diabetic retinopathy at a stage when it can be treated. Artificial intelligence automated systems can be used for diabetic eye screening but are not employed in the national English Diabetic Eye Screening Programme. The aim was to report the performance of a commercially available deep-learning artificial intelligence software in a large English population. METHODS 9817 anonymised image sets from 10,000 consecutive diabetic eye screening episodes were presented to an artificial intelligence software. The sensitivity and specificity of the artificial intelligence system for detecting diabetic retinopathy were determined using the diabetic eye screening programme manual grade according to national protocols as the reference standard. RESULTS For no diabetic retinopathy versus any diabetic retinopathy, the sensitivity of the artificial intelligence grading system was 69.7% and specificity 92.2%. The performance of the artificial intelligence system was superior for no or mild diabetic retinopathy versus significant or referrable diabetic retinopathy with a sensitivity of 95.4% and specificity of 92.0%. No cases were identified in which the artificial intelligence grade had missed significant diabetic retinopathy. CONCLUSION The performance of a commercially available deep-learning artificial intelligence system for identifying diabetic retinopathy in an English national Diabetic Eye Screening Programme is presented. Using the pre-defined settings artificial intelligence performance was highest when identifying diabetic retinopathy which requires an action by the screening programme.
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Affiliation(s)
| | | | | | | | | | - Jo Vodrey
- InHealth Intelligence Ltd, Winsford, UK
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12
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Peeters F, Rommes S, Elen B, Gerrits N, Stalmans I, Jacob J, De Boever P. Artificial Intelligence Software for Diabetic Eye Screening: Diagnostic Performance and Impact of Stratification. J Clin Med 2023; 12:jcm12041408. [PMID: 36835942 PMCID: PMC9967595 DOI: 10.3390/jcm12041408] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 01/31/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
AIM To evaluate the MONA.health artificial intelligence screening software for detecting referable diabetic retinopathy (DR) and diabetic macular edema (DME), including subgroup analysis. METHODS The algorithm's threshold value was fixed at the 90% sensitivity operating point on the receiver operating curve to perform the disease classification. Diagnostic performance was appraised on a private test set and publicly available datasets. Stratification analysis was executed on the private test set considering age, ethnicity, sex, insulin dependency, year of examination, camera type, image quality, and dilatation status. RESULTS The software displayed an area under the curve (AUC) of 97.28% for DR and 98.08% for DME on the private test set. The specificity and sensitivity for combined DR and DME predictions were 94.24 and 90.91%, respectively. The AUC ranged from 96.91 to 97.99% on the publicly available datasets for DR. AUC values were above 95% in all subgroups, with lower predictive values found for individuals above the age of 65 (82.51% sensitivity) and Caucasians (84.03% sensitivity). CONCLUSION We report good overall performance of the MONA.health screening software for DR and DME. The software performance remains stable with no significant deterioration of the deep learning models in any studied strata.
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Affiliation(s)
- Freya Peeters
- Department of Ophthalmology, University Hospitals Leuven, 3000 Leuven, Belgium
- Biomedical Sciences Group, Research Group Ophthalmology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Correspondence:
| | - Stef Rommes
- MONA.health, 3060 Bertem, Belgium
- Flemish Institute for Technological Research (VITO), 2400 Mol, Belgium
| | - Bart Elen
- Flemish Institute for Technological Research (VITO), 2400 Mol, Belgium
| | - Nele Gerrits
- Flemish Institute for Technological Research (VITO), 2400 Mol, Belgium
| | - Ingeborg Stalmans
- Department of Ophthalmology, University Hospitals Leuven, 3000 Leuven, Belgium
- Biomedical Sciences Group, Research Group Ophthalmology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
| | - Julie Jacob
- Department of Ophthalmology, University Hospitals Leuven, 3000 Leuven, Belgium
- Biomedical Sciences Group, Research Group Ophthalmology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
| | - Patrick De Boever
- Flemish Institute for Technological Research (VITO), 2400 Mol, Belgium
- Centre for Environmental Sciences, Hasselt University, Diepenbeek, 3500 Hasselt, Belgium
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13
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Domínguez C, Heras J, Mata E, Pascual V, Royo D, Zapata MÁ. Binary and multi-class automated detection of age-related macular degeneration using convolutional- and transformer-based architectures. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107302. [PMID: 36528999 DOI: 10.1016/j.cmpb.2022.107302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 09/05/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Age-related macular degeneration (AMD) is an eye disease that happens when ageing causes damage to the macula, and it is the leading cause of blindness in developed countries. Screening retinal fundus images allows ophthalmologists to early detect, diagnose and treat this disease; however, the manual interpretation of images is a time-consuming task. In this paper, we aim to study different deep learning methods to diagnose AMD. METHODS We have conducted a thorough study of two families of deep learning models based on convolutional neural networks (CNN) and transformer architectures to automatically diagnose referable/non-referable AMD, and grade AMD severity scales (no AMD, early AMD, intermediate AMD, and advanced AMD). In addition, we have analysed several progressive resizing strategies and ensemble methods for convolutional-based architectures to further improve the performance of the models. RESULTS As a first result, we have shown that transformer-based architectures obtain considerably worse results than convolutional-based architectures for diagnosing AMD. Moreover, we have built a model for diagnosing referable AMD that yielded a mean F1-score (SD) of 92.60% (0.47), a mean AUROC (SD) of 97.53% (0.40), and a mean weighted kappa coefficient (SD) of 85.28% (0.91); and an ensemble of models for grading AMD severity scales with a mean accuracy (SD) of 82.55% (2.92), and a mean weighted kappa coefficient (SD) of 84.76% (2.45). CONCLUSIONS This work shows that working with convolutional based architectures is more suitable than using transformer based models for classifying and grading AMD from retinal fundus images. Furthermore, convolutional models can be improved by means of progressive resizing strategies and ensemble methods.
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Affiliation(s)
- César Domínguez
- Department of Mathematics and Computer Science, University of La Rioja, Spain
| | - Jónathan Heras
- Department of Mathematics and Computer Science, University of La Rioja, Spain.
| | - Eloy Mata
- Department of Mathematics and Computer Science, University of La Rioja, Spain
| | - Vico Pascual
- Department of Mathematics and Computer Science, University of La Rioja, Spain
| | | | - Miguel Ángel Zapata
- UPRetina, Barcelona, Spain; Hospital Vall Hebron, Passeig Roser 126, Sant Cugat del Vallés, 08195 Barcelona, Spain
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Morano J, Hervella ÁS, Rouco J, Novo J, Fernández-Vigo JI, Ortega M. Weakly-supervised detection of AMD-related lesions in color fundus images using explainable deep learning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107296. [PMID: 36481530 DOI: 10.1016/j.cmpb.2022.107296] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 11/16/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVES Age-related macular degeneration (AMD) is a degenerative disorder affecting the macula, a key area of the retina for visual acuity. Nowadays, AMD is the most frequent cause of blindness in developed countries. Although some promising treatments have been proposed that effectively slow down its development, their effectiveness significantly diminishes in the advanced stages. This emphasizes the importance of large-scale screening programs for early detection. Nevertheless, implementing such programs for a disease like AMD is usually unfeasible, since the population at risk is large and the diagnosis is challenging. For the characterization of the disease, clinicians have to identify and localize certain retinal lesions. All this motivates the development of automatic diagnostic methods. In this sense, several works have achieved highly positive results for AMD detection using convolutional neural networks (CNNs). However, none of them incorporates explainability mechanisms linking the diagnosis to its related lesions to help clinicians to better understand the decisions of the models. This is specially relevant, since the absence of such mechanisms limits the application of automatic methods in the clinical practice. In that regard, we propose an explainable deep learning approach for the diagnosis of AMD via the joint identification of its associated retinal lesions. METHODS In our proposal, a CNN with a custom architectural setting is trained end-to-end for the joint identification of AMD and its associated retinal lesions. With the proposed setting, the lesion identification is directly derived from independent lesion activation maps; then, the diagnosis is obtained from the identified lesions. The training is performed end-to-end using image-level labels. Thus, lesion-specific activation maps are learned in a weakly-supervised manner. The provided lesion information is of high clinical interest, as it allows clinicians to assess the developmental stage of the disease. Additionally, the proposed approach allows to explain the diagnosis obtained by the models directly from the identified lesions and their corresponding activation maps. The training data necessary for the approach can be obtained without much extra work on the part of clinicians, since the lesion information is habitually present in medical records. This is an important advantage over other methods, including fully-supervised lesion segmentation methods, which require pixel-level labels whose acquisition is arduous. RESULTS The experiments conducted in 4 different datasets demonstrate that the proposed approach is able to identify AMD and its associated lesions with satisfactory performance. Moreover, the evaluation of the lesion activation maps shows that the models trained using the proposed approach are able to identify the pathological areas within the image and, in most cases, to correctly determine to which lesion they correspond. CONCLUSIONS The proposed approach provides meaningful information-lesion identification and lesion activation maps-that conveniently explains and complements the diagnosis, and is of particular interest to clinicians for the diagnostic process. Moreover, the data needed to train the networks using the proposed approach is commonly easy to obtain, what represents an important advantage in fields with particularly scarce data, such as medical imaging.
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Affiliation(s)
- José Morano
- Centro de Investigación CITIC, Universidade da Coruña, A Coruña, Spain; VARPA Research Group, Instituto de Investigación Biomédica de A Coruńa (INIBIC), Universidade da Coruña, A Coruña, Spain.
| | - Álvaro S Hervella
- Centro de Investigación CITIC, Universidade da Coruña, A Coruña, Spain; VARPA Research Group, Instituto de Investigación Biomédica de A Coruńa (INIBIC), Universidade da Coruña, A Coruña, Spain.
| | - José Rouco
- Centro de Investigación CITIC, Universidade da Coruña, A Coruña, Spain; VARPA Research Group, Instituto de Investigación Biomédica de A Coruńa (INIBIC), Universidade da Coruña, A Coruña, Spain.
| | - Jorge Novo
- Centro de Investigación CITIC, Universidade da Coruña, A Coruña, Spain; VARPA Research Group, Instituto de Investigación Biomédica de A Coruńa (INIBIC), Universidade da Coruña, A Coruña, Spain.
| | - José I Fernández-Vigo
- Department of Ophthalmology, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria (IdISSC), Madrid, Spain; Department of Ophthalmology, Centro Internacional de Oftalmología Avanzada, Madrid, Spain.
| | - Marcos Ortega
- Centro de Investigación CITIC, Universidade da Coruña, A Coruña, Spain; VARPA Research Group, Instituto de Investigación Biomédica de A Coruńa (INIBIC), Universidade da Coruña, A Coruña, Spain.
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Selvachandran G, Quek SG, Paramesran R, Ding W, Son LH. Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods. Artif Intell Rev 2023; 56:915-964. [PMID: 35498558 PMCID: PMC9038999 DOI: 10.1007/s10462-022-10185-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2022] [Indexed: 02/02/2023]
Abstract
The exponential increase in the number of diabetics around the world has led to an equally large increase in the number of diabetic retinopathy (DR) cases which is one of the major complications caused by diabetes. Left unattended, DR worsens the vision and would lead to partial or complete blindness. As the number of diabetics continue to increase exponentially in the coming years, the number of qualified ophthalmologists need to increase in tandem in order to meet the demand for screening of the growing number of diabetic patients. This makes it pertinent to develop ways to automate the detection process of DR. A computer aided diagnosis system has the potential to significantly reduce the burden currently placed on the ophthalmologists. Hence, this review paper is presented with the aim of summarizing, classifying, and analyzing all the recent development on automated DR detection using fundus images from 2015 up to this date. Such work offers an unprecedentedly thorough review of all the recent works on DR, which will potentially increase the understanding of all the recent studies on automated DR detection, particularly on those that deploys machine learning algorithms. Firstly, in this paper, a comprehensive state-of-the-art review of the methods that have been introduced in the detection of DR is presented, with a focus on machine learning models such as convolutional neural networks (CNN) and artificial neural networks (ANN) and various hybrid models. Each AI will then be classified according to its type (e.g. CNN, ANN, SVM), its specific task(s) in performing DR detection. In particular, the models that deploy CNN will be further analyzed and classified according to some important properties of the respective CNN architectures of each model. A total of 150 research articles related to the aforementioned areas that were published in the recent 5 years have been utilized in this review to provide a comprehensive overview of the latest developments in the detection of DR. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-022-10185-6.
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Affiliation(s)
- Ganeshsree Selvachandran
- Department of Actuarial Science and Applied Statistics, Faculty of Business & Management, UCSI University, Jalan Menara Gading, Cheras, 56000 Kuala Lumpur, Malaysia
| | - Shio Gai Quek
- Department of Actuarial Science and Applied Statistics, Faculty of Business & Management, UCSI University, Jalan Menara Gading, Cheras, 56000 Kuala Lumpur, Malaysia
| | - Raveendran Paramesran
- Institute of Computer Science and Digital Innovation, UCSI University, Jalan Menara Gading, Cheras, 56000 Kuala Lumpur, Malaysia
| | - Weiping Ding
- School of Information Science and Technology, Nantong University, Nantong, 226019 People’s Republic of China
| | - Le Hoang Son
- VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam
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16
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Luo Z, Ding X, Hou N, Wan J. A Deep-Learning-Based Collaborative Edge-Cloud Telemedicine System for Retinopathy of Prematurity. SENSORS (BASEL, SWITZERLAND) 2022; 23:276. [PMID: 36616874 PMCID: PMC9824555 DOI: 10.3390/s23010276] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/22/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
Retinopathy of prematurity is an ophthalmic disease with a very high blindness rate. With its increasing incidence year by year, its timely diagnosis and treatment are of great significance. Due to the lack of timely and effective fundus screening for premature infants in remote areas, leading to an aggravation of the disease and even blindness, in this paper, a deep learning-based collaborative edge-cloud telemedicine system is proposed to mitigate this issue. In the proposed system, deep learning algorithms are mainly used for classification of processed images. Our algorithm is based on ResNet101 and uses undersampling and resampling to improve the data imbalance problem in the field of medical image processing. Artificial intelligence algorithms are combined with a collaborative edge-cloud architecture to implement a comprehensive telemedicine system to realize timely screening and diagnosis of retinopathy of prematurity in remote areas with shortages or a complete lack of expert medical staff. Finally, the algorithm is successfully embedded in a mobile terminal device and deployed through the support of a core hospital of Guangdong Province. The results show that we achieved 75% ACC and 60% AUC. This research is of great significance for the development of telemedicine systems and aims to mitigate the lack of medical resources and their uneven distribution in rural areas.
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Affiliation(s)
- Zeliang Luo
- College of Electro-Mechanical Engineering, Zhuhai City Polytechnic, Zhuhai 519090, China
| | - Xiaoxuan Ding
- Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China
| | - Ning Hou
- Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China
| | - Jiafu Wan
- Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China
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17
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Vujosevic S, Limoli C, Luzi L, Nucci P. Digital innovations for retinal care in diabetic retinopathy. Acta Diabetol 2022; 59:1521-1530. [PMID: 35962258 PMCID: PMC9374293 DOI: 10.1007/s00592-022-01941-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 07/04/2022] [Indexed: 12/02/2022]
Abstract
AIM The purpose of this review is to examine the applications of novel digital technology domains for the screening and management of patients with diabetic retinopathy (DR). METHODS A PubMed engine search was performed, using the terms "Telemedicine", "Digital health", "Telehealth", "Telescreening", "Artificial intelligence", "Deep learning", "Smartphone", "Triage", "Screening", "Home-based", "Monitoring", "Ophthalmology", "Diabetes", "Diabetic Retinopathy", "Retinal imaging". Full-text English language studies from January 1, 2010, to February 1, 2022, and reference lists were considered for the conceptual framework of this review. RESULTS Diabetes mellitus and its eye complications, including DR, are particularly well suited to digital technologies, providing an ideal model for telehealth initiatives and real-world applications. The current development in the adoption of telemedicine, artificial intelligence and remote monitoring as an alternative to or in addition to traditional forms of care will be discussed. CONCLUSIONS Advances in digital health have created an ecosystem ripe for telemedicine in the field of DR to thrive. Stakeholders and policymakers should adopt a participatory approach to ensure sustained implementation of these technologies after the COVID-19 pandemic. This article belongs to the Topical Collection "Diabetic Eye Disease", managed by Giuseppe Querques.
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Affiliation(s)
- Stela Vujosevic
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy.
- Eye Clinic, IRCCS MultiMedica, Via San Vittore 12, 20123, Milan, Italy.
| | - Celeste Limoli
- Eye Clinic, IRCCS MultiMedica, Via San Vittore 12, 20123, Milan, Italy
- University of Milan, Milan, Italy
| | - Livio Luzi
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
- Department of Endocrinology, Nutrition and Metabolic Diseases, IRCCS MultiMedica, Milan, Italy
| | - Paolo Nucci
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
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Gojić G, Petrović VB, Dragan D, Gajić DB, Mišković D, Džinić V, Grgić Z, Pantelić J, Oros A. Comparing the Clinical Viability of Automated Fundus Image Segmentation Methods. SENSORS (BASEL, SWITZERLAND) 2022; 22:9101. [PMID: 36501801 PMCID: PMC9735987 DOI: 10.3390/s22239101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/16/2022] [Accepted: 11/17/2022] [Indexed: 06/17/2023]
Abstract
Recent methods for automatic blood vessel segmentation from fundus images have been commonly implemented as convolutional neural networks. While these networks report high values for objective metrics, the clinical viability of recovered segmentation masks remains unexplored. In this paper, we perform a pilot study to assess the clinical viability of automatically generated segmentation masks in the diagnosis of diseases affecting retinal vascularization. Five ophthalmologists with clinical experience were asked to participate in the study. The results demonstrate low classification accuracy, inferring that generated segmentation masks cannot be used as a standalone resource in general clinical practice. The results also hint at possible clinical infeasibility in experimental design. In the follow-up experiment, we evaluate the clinical quality of masks by having ophthalmologists rank generation methods. The ranking is established with high intra-observer consistency, indicating better subjective performance for a subset of tested networks. The study also demonstrates that objective metrics are not correlated with subjective metrics in retinal segmentation tasks for the methods involved, suggesting that objective metrics commonly used in scientific papers to measure the method's performance are not plausible criteria for choosing clinically robust solutions.
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Affiliation(s)
- Gorana Gojić
- The Institute for Artificial Intelligence Research and Development of Serbia, 21102 Novi Sad, Serbia
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia
| | - Veljko B. Petrović
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia
| | - Dinu Dragan
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia
| | - Dušan B. Gajić
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia
| | - Dragiša Mišković
- The Institute for Artificial Intelligence Research and Development of Serbia, 21102 Novi Sad, Serbia
| | | | | | - Jelica Pantelić
- Institute of Eye Diseases, University Clinical Center of Serbia, 11000 Belgrade, Serbia
| | - Ana Oros
- Eye Clinic Džinić, 21107 Novi Sad, Serbia
- Institute of Neonatology, 11000 Belgrade, Serbia
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Font O, Torrents-Barrena J, Royo D, García SB, Zarranz-Ventura J, Bures A, Salinas C, Zapata MÁ. Validation of an autonomous artificial intelligence-based diagnostic system for holistic maculopathy screening in a routine occupational health checkup context. Graefes Arch Clin Exp Ophthalmol 2022; 260:3255-3265. [PMID: 35567610 PMCID: PMC9477940 DOI: 10.1007/s00417-022-05653-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 03/15/2022] [Accepted: 03/31/2022] [Indexed: 02/08/2023] Open
Abstract
PURPOSE This study aims to evaluate the ability of an autonomous artificial intelligence (AI) system for detection of the most common central retinal pathologies in fundus photography. METHODS Retrospective diagnostic test evaluation on a raw dataset of 5918 images (2839 individuals) evaluated with non-mydriatic cameras during routine occupational health checkups. Three camera models were employed: Optomed Aurora (field of view - FOV 50º, 88% of the dataset), ZEISS VISUSCOUT 100 (FOV 40º, 9%), and Optomed SmartScope M5 (FOV 40º, 3%). Image acquisition took 2 min per patient. Ground truth for each image of the dataset was determined by 2 masked retina specialists, and disagreements were resolved by a 3rd retina specialist. The specific pathologies considered for evaluation were "diabetic retinopathy" (DR), "Age-related macular degeneration" (AMD), "glaucomatous optic neuropathy" (GON), and "Nevus." Images with maculopathy signs that did not match the described taxonomy were classified as "Other." RESULTS The combination of algorithms to detect any abnormalities had an area under the curve (AUC) of 0.963 with a sensitivity of 92.9% and a specificity of 86.8%. The algorithms individually obtained are as follows: AMD AUC 0.980 (sensitivity 93.8%; specificity 95.7%), DR AUC 0.950 (sensitivity 81.1%; specificity 94.8%), GON AUC 0.889 (sensitivity 53.6% specificity 95.7%), Nevus AUC 0.931 (sensitivity 86.7%; specificity 90.7%). CONCLUSION Our holistic AI approach reaches high diagnostic accuracy at simultaneous detection of DR, AMD, and Nevus. The integration of pathology-specific algorithms permits higher sensitivities with minimal impact on its specificity. It also reduces the risk of missing incidental findings. Deep learning may facilitate wider screenings of eye diseases.
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Affiliation(s)
- Octavi Font
- Optretina Image Reading Team, Barcelona, Spain
| | - Jordina Torrents-Barrena
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Dídac Royo
- Optretina Image Reading Team, Barcelona, Spain
| | - Sandra Banderas García
- Facultat de Cirurgia i Ciències Morfològiques, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain.
- Ophthalmology Department Hospital Vall d'Hebron, Barcelona, Spain.
| | - Javier Zarranz-Ventura
- Institut Clinic of Ophthalmology (ICOF), Hospital Clinic, Barcelona, Spain
- Institut d'Investigacions Biomediques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
| | - Anniken Bures
- Optretina Image Reading Team, Barcelona, Spain
- Instituto de Microcirugía Ocular (IMO), Barcelona, Spain
| | - Cecilia Salinas
- Optretina Image Reading Team, Barcelona, Spain
- Instituto de Microcirugía Ocular (IMO), Barcelona, Spain
| | - Miguel Ángel Zapata
- Optretina Image Reading Team, Barcelona, Spain
- Ophthalmology Department Hospital Vall d'Hebron, Barcelona, Spain
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Nadeem MW, Goh HG, Hussain M, Liew SY, Andonovic I, Khan MA. Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions. SENSORS (BASEL, SWITZERLAND) 2022; 22:6780. [PMID: 36146130 PMCID: PMC9505428 DOI: 10.3390/s22186780] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/02/2022] [Accepted: 08/08/2022] [Indexed: 05/12/2023]
Abstract
Deep learning (DL) enables the creation of computational models comprising multiple processing layers that learn data representations at multiple levels of abstraction. In the recent past, the use of deep learning has been proliferating, yielding promising results in applications across a growing number of fields, most notably in image processing, medical image analysis, data analysis, and bioinformatics. DL algorithms have also had a significant positive impact through yielding improvements in screening, recognition, segmentation, prediction, and classification applications across different domains of healthcare, such as those concerning the abdomen, cardiac, pathology, and retina. Given the extensive body of recent scientific contributions in this discipline, a comprehensive review of deep learning developments in the domain of diabetic retinopathy (DR) analysis, viz., screening, segmentation, prediction, classification, and validation, is presented here. A critical analysis of the relevant reported techniques is carried out, and the associated advantages and limitations highlighted, culminating in the identification of research gaps and future challenges that help to inform the research community to develop more efficient, robust, and accurate DL models for the various challenges in the monitoring and diagnosis of DR.
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Affiliation(s)
- Muhammad Waqas Nadeem
- Faculty of Information and Communication Technology (FICT), Universiti Tunku Abdul Rahman (UTAR), Kampar 31900, Malaysia
| | - Hock Guan Goh
- Faculty of Information and Communication Technology (FICT), Universiti Tunku Abdul Rahman (UTAR), Kampar 31900, Malaysia
| | - Muzammil Hussain
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore 54000, Pakistan
| | - Soung-Yue Liew
- Faculty of Information and Communication Technology (FICT), Universiti Tunku Abdul Rahman (UTAR), Kampar 31900, Malaysia
| | - Ivan Andonovic
- Department of Electronic and Electrical Engineering, Royal College Building, University of Strathclyde, 204 George St., Glasgow G1 1XW, UK
| | - Muhammad Adnan Khan
- Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam 13557, Korea
- Faculty of Computing, Riphah School of Computing and Innovation, Riphah International University, Lahore Campus, Lahore 54000, Pakistan
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21
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Kiburg KV, Turner A, He M. Telemedicine and delivery of ophthalmic care in rural and remote communities: Drawing from Australian experience. Clin Exp Ophthalmol 2022; 50:793-800. [PMID: 35975938 DOI: 10.1111/ceo.14147] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 08/07/2022] [Accepted: 08/12/2022] [Indexed: 01/07/2023]
Abstract
Rural and remote communities in Australia are characterised by small but widely dispersed populations. This has been proven to be a major hurdle in access to medical care services with screening and treatment goals repeatedly being missed. Telemedicine in ophthalmology provides the opportunity to increase the availability of high quality and timely access to healthcare within. Recent years has also seen the introduction of artificial intelligence (AI) in ophthalmology, particularly in the screening of diseases. AI will hopefully increase the number of appropriate referrals, reduce travel time for patients and ensure timely triage given the low number of qualified optometrists and ophthalmologists. Telemedicine and AI has been introduced in a number of countries and has led to tremendous benefits and advantages when compared to standard practices. This paper summarises current practices in telemedicine and AI and the future of this technology in improving patient care in the field of ophthalmology.
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Affiliation(s)
- Katerina V Kiburg
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.,Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Victoria, Australia
| | - Angus Turner
- Lions Outback Vision, Lions Eye Institute, Nedlands, Western Australia, Australia.,Centre for Ophthalmology and Visual Science, University of Western Australia, Nedlands, Western Australia, Australia
| | - Mingguang He
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.,Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Victoria, Australia
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22
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Charng J, Alam K, Swartz G, Kugelman J, Alonso-Caneiro D, Mackey DA, Chen FK. Deep learning: applications in retinal and optic nerve diseases. Clin Exp Optom 2022:1-10. [PMID: 35999058 DOI: 10.1080/08164622.2022.2111201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022] Open
Abstract
Deep learning (DL) represents a paradigm-shifting, burgeoning field of research with emerging clinical applications in optometry. Unlike traditional programming, which relies on human-set specific rules, DL works by exposing the algorithm to a large amount of annotated data and allowing the software to develop its own set of rules (i.e. learn) by adjusting the parameters inside the model (network) during a training process in order to complete the task on its own. One major limitation of traditional programming is that, with complex tasks, it may require an extensive set of rules to accurately complete the assignment. Additionally, traditional programming can be susceptible to human bias from programmer experience. With the dramatic increase in the amount and the complexity of clinical data, DL has been utilised to automate data analysis and thus to assist clinicians in patient management. This review will present the latest advances in DL, for managing posterior eye diseases as well as DL-based solutions for patients with vision loss.
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Affiliation(s)
- Jason Charng
- Centre of Ophthalmology and Visual Science (incorporating Lions Eye Institute), University of Western Australia, Perth, Australia.,Department of Optometry, School of Allied Health, University of Western Australia, Perth, Australia
| | - Khyber Alam
- Department of Optometry, School of Allied Health, University of Western Australia, Perth, Australia
| | - Gavin Swartz
- Department of Optometry, School of Allied Health, University of Western Australia, Perth, Australia
| | - Jason Kugelman
- School of Optometry and Vision Science, Queensland University of Technology, Brisbane, Australia
| | - David Alonso-Caneiro
- Centre of Ophthalmology and Visual Science (incorporating Lions Eye Institute), University of Western Australia, Perth, Australia.,School of Optometry and Vision Science, Queensland University of Technology, Brisbane, Australia
| | - David A Mackey
- Centre of Ophthalmology and Visual Science (incorporating Lions Eye Institute), University of Western Australia, Perth, Australia.,Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Victoria, Australia.,Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Fred K Chen
- Centre of Ophthalmology and Visual Science (incorporating Lions Eye Institute), University of Western Australia, Perth, Australia.,Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Victoria, Australia.,Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.,Department of Ophthalmology, Royal Perth Hospital, Western Australia, Perth, Australia
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23
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Biswas S, Khan MIA, Hossain MT, Biswas A, Nakai T, Rohdin J. Which Color Channel Is Better for Diagnosing Retinal Diseases Automatically in Color Fundus Photographs? LIFE (BASEL, SWITZERLAND) 2022; 12:life12070973. [PMID: 35888063 PMCID: PMC9321111 DOI: 10.3390/life12070973] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 05/25/2022] [Accepted: 06/01/2022] [Indexed: 11/22/2022]
Abstract
Color fundus photographs are the most common type of image used for automatic diagnosis of retinal diseases and abnormalities. As all color photographs, these images contain information about three primary colors, i.e., red, green, and blue, in three separate color channels. This work aims to understand the impact of each channel in the automatic diagnosis of retinal diseases and abnormalities. To this end, the existing works are surveyed extensively to explore which color channel is used most commonly for automatically detecting four leading causes of blindness and one retinal abnormality along with segmenting three retinal landmarks. From this survey, it is clear that all channels together are typically used for neural network-based systems, whereas for non-neural network-based systems, the green channel is most commonly used. However, from the previous works, no conclusion can be drawn regarding the importance of the different channels. Therefore, systematic experiments are conducted to analyse this. A well-known U-shaped deep neural network (U-Net) is used to investigate which color channel is best for segmenting one retinal abnormality and three retinal landmarks.
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Affiliation(s)
- Sangeeta Biswas
- Faculty of Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.I.A.K.); (M.T.H.)
- Correspondence: or
| | - Md. Iqbal Aziz Khan
- Faculty of Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.I.A.K.); (M.T.H.)
| | - Md. Tanvir Hossain
- Faculty of Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.I.A.K.); (M.T.H.)
| | - Angkan Biswas
- CAPM Company Limited, Bonani, Dhaka 1213, Bangladesh;
| | - Takayoshi Nakai
- Faculty of Engineering, Shizuoka University, Hamamatsu 432-8561, Japan;
| | - Johan Rohdin
- Faculty of Information Technology, Brno University of Technology, 61200 Brno, Czech Republic;
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24
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Dow ER, Keenan TDL, Lad EM, Lee AY, Lee CS, Loewenstein A, Eydelman MB, Chew EY, Keane PA, Lim JI. From Data to Deployment: The Collaborative Community on Ophthalmic Imaging Roadmap for Artificial Intelligence in Age-Related Macular Degeneration. Ophthalmology 2022; 129:e43-e59. [PMID: 35016892 PMCID: PMC9859710 DOI: 10.1016/j.ophtha.2022.01.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/16/2021] [Accepted: 01/04/2022] [Indexed: 01/25/2023] Open
Abstract
OBJECTIVE Health care systems worldwide are challenged to provide adequate care for the 200 million individuals with age-related macular degeneration (AMD). Artificial intelligence (AI) has the potential to make a significant, positive impact on the diagnosis and management of patients with AMD; however, the development of effective AI devices for clinical care faces numerous considerations and challenges, a fact evidenced by a current absence of Food and Drug Administration (FDA)-approved AI devices for AMD. PURPOSE To delineate the state of AI for AMD, including current data, standards, achievements, and challenges. METHODS Members of the Collaborative Community on Ophthalmic Imaging Working Group for AI in AMD attended an inaugural meeting on September 7, 2020, to discuss the topic. Subsequently, they undertook a comprehensive review of the medical literature relevant to the topic. Members engaged in meetings and discussion through December 2021 to synthesize the information and arrive at a consensus. RESULTS Existing infrastructure for robust AI development for AMD includes several large, labeled data sets of color fundus photography and OCT images; however, image data often do not contain the metadata necessary for the development of reliable, valid, and generalizable models. Data sharing for AMD model development is made difficult by restrictions on data privacy and security, although potential solutions are under investigation. Computing resources may be adequate for current applications, but knowledge of machine learning development may be scarce in many clinical ophthalmology settings. Despite these challenges, researchers have produced promising AI models for AMD for screening, diagnosis, prediction, and monitoring. Future goals include defining benchmarks to facilitate regulatory authorization and subsequent clinical setting generalization. CONCLUSIONS Delivering an FDA-authorized, AI-based device for clinical care in AMD involves numerous considerations, including the identification of an appropriate clinical application; acquisition and development of a large, high-quality data set; development of the AI architecture; training and validation of the model; and functional interactions between the model output and clinical end user. The research efforts undertaken to date represent starting points for the medical devices that eventually will benefit providers, health care systems, and patients.
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Affiliation(s)
- Eliot R Dow
- Byers Eye Institute, Stanford University, Palo Alto, California
| | - Tiarnan D L Keenan
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Eleonora M Lad
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington
| | - Cecilia S Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington
| | - Anat Loewenstein
- Division of Ophthalmology, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Malvina B Eydelman
- Office of Health Technology 1, Center of Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland
| | - Emily Y Chew
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
| | - Pearse A Keane
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom.
| | - Jennifer I Lim
- Department of Ophthalmology, University of Illinois at Chicago, Chicago, Illinois.
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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] [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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Matta S, Lamard M, Conze PH, Le Guilcher A, Ricquebourg V, Benyoussef AA, Massin P, Rottier JB, Cochener B, Quellec G. Automatic Screening for Ocular Anomalies Using Fundus Photographs. Optom Vis Sci 2022; 99:281-291. [PMID: 34897234 DOI: 10.1097/opx.0000000000001845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
SIGNIFICANCE Screening for ocular anomalies using fundus photography is key to prevent vision impairment and blindness. With the growing and aging population, automated algorithms that can triage fundus photographs and provide instant referral decisions are relevant to scale-up screening and face the shortage of ophthalmic expertise. PURPOSE This study aimed to develop a deep learning algorithm that detects any ocular anomaly in fundus photographs and to evaluate this algorithm for "normal versus anomalous" eye examination classification in the diabetic and general populations. METHODS The deep learning algorithm was developed and evaluated in two populations: the diabetic and general populations. Our patient cohorts consist of 37,129 diabetic patients from the OPHDIAT diabetic retinopathy screening network in Paris, France, and 7356 general patients from the OphtaMaine private screening network, in Le Mans, France. Each data set was divided into a development subset and a test subset of more than 4000 examinations each. For ophthalmologist/algorithm comparison, a subset of 2014 examinations from the OphtaMaine test subset was labeled by a second ophthalmologist. First, the algorithm was trained on the OPHDIAT development subset. Then, it was fine-tuned on the OphtaMaine development subset. RESULTS On the OPHDIAT test subset, the area under the receiver operating characteristic curve for normal versus anomalous classification was 0.9592. On the OphtaMaine test subset, the area under the receiver operating characteristic curve was 0.8347 before fine-tuning and 0.9108 after fine-tuning. On the ophthalmologist/algorithm comparison subset, the second ophthalmologist achieved a specificity of 0.8648 and a sensitivity of 0.6682. For the same specificity, the fine-tuned algorithm achieved a sensitivity of 0.8248. CONCLUSIONS The proposed algorithm compares favorably with human performance for normal versus anomalous eye examination classification using fundus photography. Artificial intelligence, which previously targeted a few retinal pathologies, can be used to screen for ocular anomalies comprehensively.
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Affiliation(s)
| | | | | | | | | | | | - Pascale Massin
- Ophtalmology Department, Lariboisière Hospital, APHP, Paris, France
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27
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Review of Machine Learning Applications Using Retinal Fundus Images. Diagnostics (Basel) 2022; 12:diagnostics12010134. [PMID: 35054301 PMCID: PMC8774893 DOI: 10.3390/diagnostics12010134] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 01/03/2022] [Accepted: 01/03/2022] [Indexed: 02/04/2023] Open
Abstract
Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed.
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28
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Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice. Prog Retin Eye Res 2021; 90:101034. [PMID: 34902546 DOI: 10.1016/j.preteyeres.2021.101034] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 12/03/2021] [Accepted: 12/06/2021] [Indexed: 01/14/2023]
Abstract
An increasing number of artificial intelligence (AI) systems are being proposed in ophthalmology, motivated by the variety and amount of clinical and imaging data, as well as their potential benefits at the different stages of patient care. Despite achieving close or even superior performance to that of experts, there is a critical gap between development and integration of AI systems in ophthalmic practice. This work focuses on the importance of trustworthy AI to close that gap. We identify the main aspects or challenges that need to be considered along the AI design pipeline so as to generate systems that meet the requirements to be deemed trustworthy, including those concerning accuracy, resiliency, reliability, safety, and accountability. We elaborate on mechanisms and considerations to address those aspects or challenges, and define the roles and responsibilities of the different stakeholders involved in AI for ophthalmic care, i.e., AI developers, reading centers, healthcare providers, healthcare institutions, ophthalmological societies and working groups or committees, patients, regulatory bodies, and payers. Generating trustworthy AI is not a responsibility of a sole stakeholder. There is an impending necessity for a collaborative approach where the different stakeholders are represented along the AI design pipeline, from the definition of the intended use to post-market surveillance after regulatory approval. This work contributes to establish such multi-stakeholder interaction and the main action points to be taken so that the potential benefits of AI reach real-world ophthalmic settings.
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29
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Foo LL, Ng WY, Lim GYS, Tan TE, Ang M, Ting DSW. Artificial intelligence in myopia: current and future trends. Curr Opin Ophthalmol 2021; 32:413-424. [PMID: 34310401 DOI: 10.1097/icu.0000000000000791] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
PURPOSE OF REVIEW Myopia is one of the leading causes of visual impairment, with a projected increase in prevalence globally. One potential approach to address myopia and its complications is early detection and treatment. However, current healthcare systems may not be able to cope with the growing burden. Digital technological solutions such as artificial intelligence (AI) have emerged as a potential adjunct for myopia management. RECENT FINDINGS There are currently four significant domains of AI in myopia, including machine learning (ML), deep learning (DL), genetics and natural language processing (NLP). ML has been demonstrated to be a useful adjunctive for myopia prediction and biometry for cataract surgery in highly myopic individuals. DL techniques, particularly convoluted neural networks, have been applied to various image-related diagnostic and predictive solutions. Applications of AI in genomics and NLP appear to be at a nascent stage. SUMMARY Current AI research is mainly focused on disease classification and prediction in myopia. Through greater collaborative research, we envision AI will play an increasingly critical role in big data analysis by aggregating a greater variety of parameters including genomics and environmental factors. This may enable the development of generalizable adjunctive DL systems that could help realize predictive and individualized precision medicine for myopic patients.
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Affiliation(s)
- Li Lian Foo
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Wei Yan Ng
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | | | - Tien-En Tan
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Marcus Ang
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Daniel Shu Wei Ting
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
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30
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Baget-Bernaldiz M, Pedro RA, Santos-Blanco E, Navarro-Gil R, Valls A, Moreno A, Rashwan HA, Puig D. Testing a Deep Learning Algorithm for Detection of Diabetic Retinopathy in a Spanish Diabetic Population and with MESSIDOR Database. Diagnostics (Basel) 2021; 11:diagnostics11081385. [PMID: 34441319 PMCID: PMC8394605 DOI: 10.3390/diagnostics11081385] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 07/16/2021] [Accepted: 07/28/2021] [Indexed: 12/23/2022] Open
Abstract
Background: The aim of the present study was to test our deep learning algorithm (DLA) by reading the retinographies. Methods: We tested our DLA built on convolutional neural networks in 14,186 retinographies from our population and 1200 images extracted from MESSIDOR. The retinal images were graded both by the DLA and independently by four retina specialists. Results of the DLA were compared according to accuracy (ACC), sensitivity (S), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC), distinguishing between identification of any type of DR (any DR) and referable DR (RDR). Results: The results of testing the DLA for identifying any DR in our population were: ACC = 99.75, S = 97.92, SP = 99.91, PPV = 98.92, NPV = 99.82, and AUC = 0.983. When detecting RDR, the results were: ACC = 99.66, S = 96.7, SP = 99.92, PPV = 99.07, NPV = 99.71, and AUC = 0.988. The results of testing the DLA for identifying any DR with MESSIDOR were: ACC = 94.79, S = 97.32, SP = 94.57, PPV = 60.93, NPV = 99.75, and AUC = 0.959. When detecting RDR, the results were: ACC = 98.78, S = 94.64, SP = 99.14, PPV = 90.54, NPV = 99.53, and AUC = 0.968. Conclusions: Our DLA performed well, both in detecting any DR and in classifying those eyes with RDR in a sample of retinographies of type 2 DM patients in our population and the MESSIDOR database.
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Affiliation(s)
- Marc Baget-Bernaldiz
- Ophthalmology Service, Hospital Universitat Sant Joan, Institut de Investigació Sanitària Pere Virgili [IISPV], Universitat Rovira & Virgili, 43204 Reus, Spain; (M.B.-B.); (E.S.-B.); (R.N.-G.)
| | - Romero-Aroca Pedro
- Ophthalmology Service, Hospital Universitat Sant Joan, Institut de Investigació Sanitària Pere Virgili [IISPV], Universitat Rovira & Virgili, 43204 Reus, Spain; (M.B.-B.); (E.S.-B.); (R.N.-G.)
- Correspondence:
| | - Esther Santos-Blanco
- Ophthalmology Service, Hospital Universitat Sant Joan, Institut de Investigació Sanitària Pere Virgili [IISPV], Universitat Rovira & Virgili, 43204 Reus, Spain; (M.B.-B.); (E.S.-B.); (R.N.-G.)
| | - Raul Navarro-Gil
- Ophthalmology Service, Hospital Universitat Sant Joan, Institut de Investigació Sanitària Pere Virgili [IISPV], Universitat Rovira & Virgili, 43204 Reus, Spain; (M.B.-B.); (E.S.-B.); (R.N.-G.)
| | - Aida Valls
- Department of Computer Engineering and Mathematics, Universitat Rovira & Virgili, 43204 Reus, Spain; (A.V.); (A.M.); (H.A.R.); (D.P.)
| | - Antonio Moreno
- Department of Computer Engineering and Mathematics, Universitat Rovira & Virgili, 43204 Reus, Spain; (A.V.); (A.M.); (H.A.R.); (D.P.)
| | - Hatem A. Rashwan
- Department of Computer Engineering and Mathematics, Universitat Rovira & Virgili, 43204 Reus, Spain; (A.V.); (A.M.); (H.A.R.); (D.P.)
| | - Domenec Puig
- Department of Computer Engineering and Mathematics, Universitat Rovira & Virgili, 43204 Reus, Spain; (A.V.); (A.M.); (H.A.R.); (D.P.)
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31
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Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs. Eye (Lond) 2021; 36:1433-1441. [PMID: 34211137 DOI: 10.1038/s41433-021-01552-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 03/24/2021] [Accepted: 04/13/2021] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVES To present and validate a deep ensemble algorithm to detect diabetic retinopathy (DR) and diabetic macular oedema (DMO) using retinal fundus images. METHODS A total of 8739 retinal fundus images were collected from a retrospective cohort of 3285 patients. For detecting DR and DMO, a multiple improved Inception-v4 ensembling approach was developed. We measured the algorithm's performance and made a comparison with that of human experts on our primary dataset, while its generalization was assessed on the publicly available Messidor-2 dataset. Also, we investigated systematically the impact of the size and number of input images used in training on model's performance, respectively. Further, the time budget of training/inference versus model performance was analyzed. RESULTS On our primary test dataset, the model achieved an 0.992 (95% CI, 0.989-0.995) AUC corresponding to 0.925 (95% CI, 0.916-0.936) sensitivity and 0.961 (95% CI, 0.950-0.972) specificity for referable DR, while the sensitivity and specificity for ophthalmologists ranged from 0.845 to 0.936, and from 0.912 to 0.971, respectively. For referable DMO, our model generated an AUC of 0.994 (95% CI, 0.992-0.996) with a 0.930 (95% CI, 0.919-0.941) sensitivity and 0.971 (95% CI, 0.965-0.978) specificity, whereas ophthalmologists obtained sensitivities ranging between 0.852 and 0.946, and specificities ranging between 0.926 and 0.985. CONCLUSION This study showed that the deep ensemble model exhibited excellent performance in detecting DR and DMO, and had good robustness and generalization, which could potentially help support and expand DR/DMO screening programs.
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32
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Bortsova G, González-Gonzalo C, Wetstein SC, Dubost F, Katramados I, Hogeweg L, Liefers B, van Ginneken B, Pluim JPW, Veta M, Sánchez CI, de Bruijne M. Adversarial attack vulnerability of medical image analysis systems: Unexplored factors. Med Image Anal 2021; 73:102141. [PMID: 34246850 DOI: 10.1016/j.media.2021.102141] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Revised: 06/10/2021] [Accepted: 06/17/2021] [Indexed: 01/01/2023]
Abstract
Adversarial attacks are considered a potentially serious security threat for machine learning systems. Medical image analysis (MedIA) systems have recently been argued to be vulnerable to adversarial attacks due to strong financial incentives and the associated technological infrastructure. In this paper, we study previously unexplored factors affecting adversarial attack vulnerability of deep learning MedIA systems in three medical domains: ophthalmology, radiology, and pathology. We focus on adversarial black-box settings, in which the attacker does not have full access to the target model and usually uses another model, commonly referred to as surrogate model, to craft adversarial examples that are then transferred to the target model. We consider this to be the most realistic scenario for MedIA systems. Firstly, we study the effect of weight initialization (pre-training on ImageNet or random initialization) on the transferability of adversarial attacks from the surrogate model to the target model, i.e., how effective attacks crafted using the surrogate model are on the target model. Secondly, we study the influence of differences in development (training and validation) data between target and surrogate models. We further study the interaction of weight initialization and data differences with differences in model architecture. All experiments were done with a perturbation degree tuned to ensure maximal transferability at minimal visual perceptibility of the attacks. Our experiments show that pre-training may dramatically increase the transferability of adversarial examples, even when the target and surrogate's architectures are different: the larger the performance gain using pre-training, the larger the transferability. Differences in the development data between target and surrogate models considerably decrease the performance of the attack; this decrease is further amplified by difference in the model architecture. We believe these factors should be considered when developing security-critical MedIA systems planned to be deployed in clinical practice. We recommend avoiding using only standard components, such as pre-trained architectures and publicly available datasets, as well as disclosure of design specifications, in addition to using adversarial defense methods. When evaluating the vulnerability of MedIA systems to adversarial attacks, various attack scenarios and target-surrogate differences should be simulated to achieve realistic robustness estimates. The code and all trained models used in our experiments are publicly available.3.
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Affiliation(s)
- Gerda Bortsova
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, The Netherlands.
| | - Cristina González-Gonzalo
- A-Eye Research Group, Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboudumc, Nijmegen, The Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboudumc, Nijmegen, The Netherlands
| | - Suzanne C Wetstein
- Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Florian Dubost
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, The Netherlands
| | | | | | - Bart Liefers
- A-Eye Research Group, Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboudumc, Nijmegen, The Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboudumc, Nijmegen, The Netherlands
| | - Bram van Ginneken
- Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboudumc, Nijmegen, The Netherlands
| | - Josien P W Pluim
- Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Mitko Veta
- Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Clara I Sánchez
- A-Eye Research Group, Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboudumc, Nijmegen, The Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboudumc, Nijmegen, The Netherlands; Department of Ophthalmology Radboudumc, Nijmegen, The Netherlands
| | - Marleen de Bruijne
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, The Netherlands; Department of Computer Science, University of Copenhagen, Denmark
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Dong L, Yang Q, Zhang RH, Wei WB. Artificial intelligence for the detection of age-related macular degeneration in color fundus photographs: A systematic review and meta-analysis. EClinicalMedicine 2021; 35:100875. [PMID: 34027334 PMCID: PMC8129891 DOI: 10.1016/j.eclinm.2021.100875] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 04/14/2021] [Accepted: 04/15/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Age-related macular degeneration (AMD) is one of the leading causes of vision loss in the elderly population. The application of artificial intelligence (AI) provides convenience for the diagnosis of AMD. This systematic review and meta-analysis aimed to quantify the performance of AI in detecting AMD in fundus photographs. METHODS We searched PubMed, Embase, Web of Science and the Cochrane Library before December 31st, 2020 for studies reporting the application of AI in detecting AMD in color fundus photographs. Then, we pooled the data for analysis. PROSPERO registration number: CRD42020197532. FINDINGS 19 studies were finally selected for systematic review and 13 of them were included in the quantitative synthesis. All studies adopted human graders as reference standard. The pooled area under the receiver operating characteristic curve (AUROC) was 0.983 (95% confidence interval (CI):0.979-0.987). The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were 0.88 (95% CI:0.88-0.88), 0.90 (95% CI:0.90-0.91), and 275.27 (95% CI:158.43-478.27), respectively. Threshold analysis was performed and a potential threshold effect was detected among the studies (Spearman correlation coefficient: -0.600, P = 0.030), which was the main cause for the heterogeneity. For studies applying convolutional neural networks in the Age-Related Eye Disease Study database, the pooled AUROC, sensitivity, specificity, and DOR were 0.983 (95% CI:0.978-0.988), 0.88 (95% CI:0.88-0.88), 0.91 (95% CI:0.91-0.91), and 273.14 (95% CI:130.79-570.43), respectively. INTERPRETATION Our data indicated that AI was able to detect AMD in color fundus photographs. The application of AI-based automatic tools is beneficial for the diagnosis of AMD. FUNDING Capital Health Research and Development of Special (2020-1-2052).
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Dong L, Hu XY, Yan YN, Zhang Q, Zhou N, Shao L, Wang YX, Xu J, Lan YJ, Li Y, Xiong JH, Liu CX, Ge ZY, Jonas JB, Wei WB. Deep Learning-Based Estimation of Axial Length and Subfoveal Choroidal Thickness From Color Fundus Photographs. Front Cell Dev Biol 2021; 9:653692. [PMID: 33898450 PMCID: PMC8063031 DOI: 10.3389/fcell.2021.653692] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 03/10/2021] [Indexed: 12/24/2022] Open
Abstract
This study aimed to develop an automated computer-based algorithm to estimate axial length and subfoveal choroidal thickness (SFCT) based on color fundus photographs. In the population-based Beijing Eye Study 2011, we took fundus photographs and measured SFCT by optical coherence tomography (OCT) and axial length by optical low-coherence reflectometry. Using 6394 color fundus images taken from 3468 participants, we trained and evaluated a deep-learning-based algorithm for estimation of axial length and SFCT. The algorithm had a mean absolute error (MAE) for estimating axial length and SFCT of 0.56 mm [95% confidence interval (CI): 0.53,0.61] and 49.20 μm (95% CI: 45.83,52.54), respectively. Estimated values and measured data showed coefficients of determination of r 2 = 0.59 (95% CI: 0.50,0.65) for axial length and r 2 = 0.62 (95% CI: 0.57,0.67) for SFCT. Bland-Altman plots revealed a mean difference in axial length and SFCT of -0.16 mm (95% CI: -1.60,1.27 mm) and of -4.40 μm (95% CI, -131.8,122.9 μm), respectively. For the estimation of axial length, heat map analysis showed that signals predominantly from overall of the macular region, the foveal region, and the extrafoveal region were used in the eyes with an axial length of < 22 mm, 22-26 mm, and > 26 mm, respectively. For the estimation of SFCT, the convolutional neural network (CNN) used mostly the central part of the macular region, the fovea or perifovea, independently of the SFCT. Our study shows that deep-learning-based algorithms may be helpful in estimating axial length and SFCT based on conventional color fundus images. They may be a further step in the semiautomatic assessment of the eye.
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Affiliation(s)
- Li Dong
- Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Laboratory, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Xin Yue Hu
- Beijing Eaglevision Technology Co., Ltd., Beijing, China
| | - Yan Ni Yan
- Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Laboratory, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Qi Zhang
- Beijing Ophthalmology and Visual Science Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Institute of Ophthalmology, Capital Medical University, Beijing, China
| | - Nan Zhou
- Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Laboratory, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Lei Shao
- Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Laboratory, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Ya Xing Wang
- Beijing Ophthalmology and Visual Science Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Institute of Ophthalmology, Capital Medical University, Beijing, China
| | - Jie Xu
- Beijing Ophthalmology and Visual Science Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Institute of Ophthalmology, Capital Medical University, Beijing, China
| | - Yin Jun Lan
- Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Laboratory, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yang Li
- Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Laboratory, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jian Hao Xiong
- Beijing Eaglevision Technology Co., Ltd., Beijing, China
| | - Cong Xin Liu
- Beijing Eaglevision Technology Co., Ltd., Beijing, China
| | - Zong Yuan Ge
- eResearch centre, Monash University, Melbourne, VIC, Australia
- ECSE, Faculty of Engineering, Monash University, Melbourne, VIC, Australia
| | - Jost. B. Jonas
- Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Wen Bin Wei
- Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Laboratory, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
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Gong D, Kras A, Miller JB. Application of Deep Learning for Diagnosing, Classifying, and Treating Age-Related Macular Degeneration. Semin Ophthalmol 2021; 36:198-204. [PMID: 33617390 DOI: 10.1080/08820538.2021.1889617] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Age-related macular degeneration (AMD) affects nearly 200 million people and is the third leading cause of irreversible vision loss worldwide. Deep learning, a branch of artificial intelligence that can learn image recognition based on pre-existing datasets, creates an opportunity for more accurate and efficient diagnosis, classification, and treatment of AMD on both individual and population levels. Current algorithms based on fundus photography and optical coherence tomography imaging have already achieved diagnostic accuracy levels comparable to human graders. This accuracy can be further increased when deep learning algorithms are simultaneously applied to multiple diagnostic imaging modalities. Combined with advances in telemedicine and imaging technology, deep learning can enable large populations of patients to be screened than would otherwise be possible and allow ophthalmologists to focus on seeing those patients who are in need of treatment, thus reducing the number of patients with significant visual impairment from AMD.
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Affiliation(s)
- Dan Gong
- Department of Ophthalmology, Retina Service, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA,USA
| | - Ashley Kras
- Harvard Retinal Imaging Lab, Massachusetts Eye and Ear Infirmary, Boston, MA
| | - John B Miller
- Department of Ophthalmology, Retina Service, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA,USA.,Harvard Retinal Imaging Lab, Massachusetts Eye and Ear Infirmary, Boston, MA
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Qummar S, Khan FG, Shah S, Khan A, Din A, Gao J. Deep Learning Techniques for Diabetic Retinopathy Detection. Curr Med Imaging 2021; 16:1201-1213. [DOI: 10.2174/1573405616666200213114026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 11/26/2019] [Accepted: 12/19/2019] [Indexed: 11/22/2022]
Abstract
Diabetes occurs due to the excess of glucose in the blood that may affect many organs
of the body. Elevated blood sugar in the body causes many problems including Diabetic Retinopathy
(DR). DR occurs due to the mutilation of the blood vessels in the retina. The manual detection
of DR by ophthalmologists is complicated and time-consuming. Therefore, automatic detection is
required, and recently different machine and deep learning techniques have been applied to detect
and classify DR. In this paper, we conducted a study of the various techniques available in the literature
for the identification/classification of DR, the strengths and weaknesses of available datasets
for each method, and provides the future directions. Moreover, we also discussed the different
steps of detection, that are: segmentation of blood vessels in a retina, detection of lesions, and other
abnormalities of DR.
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Affiliation(s)
- Sehrish Qummar
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan
| | - Fiaz Gul Khan
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan
| | - Sajid Shah
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan
| | - Ahmad Khan
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan
| | - Ahmad Din
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan
| | - Jinfeng Gao
- Department of Information Engineering, Huanghuai University, Henan, China
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Straňák Z, Penčák M, Veith M. ARTEFICIAL INTELLIGENCE IN DIABETIC RETINOPATHY SCREENING. A REVIEW. CESKA A SLOVENSKA OFTALMOLOGIE : CASOPIS CESKE OFTALMOLOGICKE SPOLECNOSTI A SLOVENSKE OFTALMOLOGICKE SPOLECNOSTI 2021; 77:224-231. [PMID: 34666491 DOI: 10.31348/2021/6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
OBJECTIVE The aim of this comprehensive paper is to acquaint the readers with evaluation of the retinal images using the arteficial intelligence (AI). Main focus of the paper is diabetic retinophaty (DR) screening. The basic principles of the artificial intelligence and algorithms that are already used in clinical practice or are shortly before approval will be described. METHODOLOGY Describing the basic characteristics and mechanisms of different approaches to the use of AI and subsequently literary minireview clarifying the current state of knowledge in the area. RESULTS Modern systems for screening diabetic retinopathy using deep neural networks achieve a sensitivity and specificity of over 80 % in most published studies. The results of specific studies vary depending on the definition of the gold standard, number of images tested and on the evaluated parameters. CONCLUSION Evaluation of images using AI will speed up and streamline the diagnosis of DR. The use of AI will allow to keep the quality of the eye care at least on the same level despite the raising number of the patients with diabetes.
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Narhari BB, Murlidhar BK, Sayyad AD, Sable GS. Automated diagnosis of diabetic retinopathy enabled by optimized thresholding-based blood vessel segmentation and hybrid classifier. BIO-ALGORITHMS AND MED-SYSTEMS 2020. [DOI: 10.1515/bams-2020-0053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Abstract
Objectives
The focus of this paper is to introduce an automated early Diabetic Retinopathy (DR) detection scheme from colour fundus images through enhanced segmentation and classification strategies by analyzing blood vessels.
Methods
The occurrence of DR is increasing from the past years, impacting the eyes due to a sudden rise in the glucose level of blood. All over the world, half of the people who are under age 70 are severely suffered from diabetes. The patients who are affected by DR will lose their vision during the absence of early recognition of DR and appropriate treatment. To decrease the growth and occurrence of loss of vision, the early detection and timely treatment of DR are desirable. At present, deep learning models have presented better performance using retinal images for DR detection. In this work, the input retinal fundus images are initially subjected to pre-processing that undergoes contrast enhancement by Contrast Limited Adaptive Histogram Equalization (CLAHE) and average filtering. Further, the optimized binary thresholding-based segmentation is done for blood vessel segmentation. For the segmented image, Tri-level Discrete Level Decomposition (Tri-DWT) is performed to decompose it. In the feature extraction phase, Local Binary Pattern (LBP), and Gray-Level Co-occurrence Matrices (GLCMs) are extracted. Next, the classification of images is done through the combination of two algorithms, one is Neural Network (NN), and the other Convolutional Neural Network (CNN). The extracted features are subjected to NN, and the tri-DWT-based segmented image is subjected to CNN. Both the segmentation and classification phases are enhanced by the improved meta-heuristic algorithm called Fitness Rate-based Crow Search Algorithm (FR-CSA), in which few parameters are optimized for attaining maximum detection accuracy.
Results
The proposed DR detection model was implemented in MATLAB 2018a, and the analysis was done using three datasets, HRF, Messidor, and DIARETDB.
Conclusions
The developed FR-CSA algorithm has the best detection accuracy in diagnosing DR.
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Affiliation(s)
- Bansode Balbhim Narhari
- Department of Electronics & Telecommunication Engineering , MIT College of Engineering, Dr. Babasaheb Ambedkar Marathwada University , Aurangabad , India
| | - Bakwad Kamlakar Murlidhar
- Department of Electronics Engineering , Puranmal Lahoti Govt. Polytechnic College, MSBTE, Latur , Mumbai , India
| | - Ajij Dildar Sayyad
- Department of Electronics & Telecommunication Engineering , MIT College of Engineering, Dr. Babasaheb Ambedkar Marathwada University , Aurangabad , India
| | - Ganesh Shahubha Sable
- Department of Electronics & Telecommunication Engineering , MIT College of Engineering, Dr. Babasaheb Ambedkar Marathwada University , Aurangabad , India
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Huemer J, Wagner SK, Sim DA. The Evolution of Diabetic Retinopathy Screening Programmes: A Chronology of Retinal Photography from 35 mm Slides to Artificial Intelligence. Clin Ophthalmol 2020; 14:2021-2035. [PMID: 32764868 PMCID: PMC7381763 DOI: 10.2147/opth.s261629] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 07/01/2020] [Indexed: 12/14/2022] Open
Abstract
As a third of people with diabetes mellitus (DM) will suffer the microvascular complications of diabetic retinopathy (DR) and therapeutic options can effectively prevent visual impairment, systematic screening has substantially reduced disease burden in developed countries. In an effort to tackle the rising incidence of DM, screening programmes have modernized in synchrony with technical and infrastructural advancements. Patient evaluation has shifted from face-to-face ophthalmologist-based review delivered through community grassroots to asynchronous store-and-forward modern telemedicine platforms commissioned on a nationwide scale. First pioneered with primitive 35-mm slide film retinal photography, the last decade has seen an emergence of high resolution and widefield imaging devices, which may reveal extents of DR indiscernible to the clinician but with implications of potential earlier identification. Similar progress has been seen in image analysis approaches - automated image analysis of retinal photographs of DR has evolved from qualitative feature detection to rules-based algorithms to autonomous artificial intelligence-powered classification. Such models have, relatively rapidly, been validated and are now receiving approval from health regulation authorities with deployment into the clinical sphere. In this review, we chart the evolution of global DR screening programmes since their inception highlighting major milestones in healthcare infrastructure, telemedicine approaches and imaging devices that have shaped the robust and effective frameworks recognised today. We also provide an outlook for the future of DR screening in the context of recent technological advancements with respect to their limitations in current times.
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Affiliation(s)
- Josef Huemer
- NIHR Biomedical Research Center at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
- Vienna Institute for Research in Ocular Surgery, A Karl Landsteiner Institute, Hanusch Hospital, Vienna, Austria
| | - Siegfried K Wagner
- NIHR Biomedical Research Center at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Dawn A Sim
- NIHR Biomedical Research Center at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
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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 AND VISION (LONDON, ENGLAND) 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] [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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Cai L, Hinkle JW, Arias D, Gorniak RJ, Lakhani PC, Flanders AE, Kuriyan AE. Applications of Artificial Intelligence for the Diagnosis, Prognosis, and Treatment of Age-related Macular Degeneration. Int Ophthalmol Clin 2020; 60:147-168. [PMID: 33093323 DOI: 10.1097/iio.0000000000000334] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
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