The impact of artificial intelligence on retinal disease management: Vision Academy retinal expert consensus.
Carla Danese, Aditya U Kale, Tariq Aslam, Paolo Lanzetta, Jane Barratt, Yu-Bai Chou, Bora Eldem, Nicole Eter, Richard Gale, Jean-François Korobelnik, Igor Kozak, Xiaorong Li, Xiaoxin Li, Anat Loewenstein, Paisan Ruamviboonsuk, Taiji Sakamoto, Daniel S W Ting, Peter van Wijngaarden, Sebastian M Waldstein, David Wong, Lihteh Wu, Miguel A Zapata, Javier Zarranz-Ventura
Author Information
Carla Danese: Department of Medicine - Ophthalmology, University of Udine, Udine, Italy.
Aditya U Kale: Academic Unit of Ophthalmology, Institute of Inflammation & Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham.
Tariq Aslam: Division of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, University of Manchester School of Health Sciences, Manchester, UK.
Paolo Lanzetta: Department of Medicine - Ophthalmology, University of Udine, Udine, Italy.
Jane Barratt: International Federation on Ageing, Toronto, Canada.
Yu-Bai Chou: Department of Ophthalmology, Taipei Veterans General Hospital.
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.
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.
Sebastian M Waldstein: Department of Ophthalmology, Landesklinikum Mistelbach-Gänserndorf, Mistelbach, Austria.
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.
Miguel A Zapata: Ophthalmology Department, Hospital Vall d'Hebron.
Javier Zarranz-Ventura: Hospital Clínic de Barcelona, University of Barcelona, Barcelona, Spain.
PURPOSE OF REVIEW: The aim of this review is to define the "state-of-the-art" in artificial intelligence (AI)-enabled devices that support the management of retinal conditions and to provide Vision Academy recommendations on the topic. RECENT FINDINGS: Most of the AI models described in the literature have not been approved for disease management purposes by regulatory authorities. These new technologies are promising as they may be able to provide personalized treatments as well as a personalized risk score for various retinal diseases. However, several issues still need to be addressed, such as the lack of a common regulatory pathway and a lack of clarity regarding the applicability of AI-enabled medical devices in different populations. SUMMARY: It is likely that current clinical practice will need to change following the application of AI-enabled medical devices. These devices are likely to have an impact on the management of retinal disease. However, a consensus needs to be reached to ensure they are safe and effective for the overall population.
References
International Medical Device Regulators Forum. Machine learning-enabled medical devices—a subset of artificial intelligence-enabled medical devices: key terms and definitions. 2021. Available at: https://www.imdrf.org/sites/default/files/2021-10/Machine%20Learning-enabled%20Medical%20Devices%20-%20A%20subset%20of%20Artificial%20Intelligence-enabled%20Medical%20Devices%20-%20Key%20Terms%20and%20Definitions.pdf [Accessed January 2023].
World Health Organization. WHO guideline: recommendations on digital interventions for health system strengthening. 2019. Available at: https://apps.who.int/iris/bitstream/handle/10665/311941/9789241550505-eng.pdf?sequence=31&isAllowed=y [Accessed January 2023].
Ting DSW, Lee AY, Wong TY. An ophthalmologist's guide to deciphering studies in artificial intelligence. Ophthalmology 2019; 126:14751479.
Chou YB, Kale AU, Lanzetta P, et al. on behalf of the Vision Academy Retinal AI Workstream. Current status and practical considerations of artificial intelligence use in screening and diagnosing retinal diseases: Vision Academy retinal expert consensus. Curr Opin Ophthalmol 2023; 34:000000.
Retmarker, a METEDA company. Retmarker DR Biomarker. 2023. Available at: https://www.retmarker.com/biomarker/ [Accessed March 2023].
Notal Vision. Notal Home OCT. 2023. Available at: https://notalvision.com/technology/home-oct [Accessed March 2023].
Notal Vision. Notal OCT Analyzer. 2023. Available at: https://notalvision.com/technology/notal-oct-analyzer [Accessed March 2023].
RetinAI. RetinAI Discovery ® . 2022. Available at: https://www.retinai.com/products/discovery [Accessed March 2023].
VUNO Inc. VUNO Med-Fundus AI. 2023. Available at: https://www.vuno.co/en/fundus [Accessed March 2023].
iHealthScreen. iPredict. 2020. Available at: https://ihealthscreen.org/ [Accessed March 2023].
RetInSight. RetInSight Fluid Monitor. 2023. Available at: https://retinsight.com/product/ [Accessed March 2023].
European Commission. EUDAMED database. 2023. Available at: https://ec.europa.eu/tools/eudamed/#/screen/home [Accessed January 2023].
US Food and Drug Administration. Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices. 2022. Available at: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices [Accessed January 2023].
US Food and Drug Administration. 510(k) Premarket Notification. 2023. Available at: https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm [Accessed January 2023].
Montesel A, Gigon A, Mosinska A, et al. Automated foveal location detection on spectral-domain optical coherence tomography in geographic atrophy patients. Graefes Arch Clin Exp Ophthalmol 2022; 260:22612270.
Derradji Y, Mosinska A, Apostolopoulos S, et al. Fully-automated atrophy segmentation in dry age-related macular degeneration in optical coherence tomography. Sci Rep 2021; 11:21893.
Mantel I, Mosinska A, Bergin C, et al. Automated quantification of pathological fluids in neovascular age-related macular degeneration, and its repeatability using deep learning. Transl Vis Sci Technol 2021; 10:17.
Apostolopoulos S, Salas J, Ordóñez JLP, et al. Automatically enhanced OCT scans of the retina: a proof of concept study. Sci Rep 2020; 10:7819.
Son J, Shin JY, Kim HD, et al. Development and validation of deep learning models for screening multiple abnormal findings in retinal fundus images. Ophthalmology 2020; 127:8594.
Martin-Pinardel R, Izquierdo-Serra J, De Zanet S, et al. Artificial intelligence-based fluid quantification and associated visual outcomes in a real-world, multicentre neovascular age-related macular degeneration national database. Br J Ophthalmol 2023.
Holz FG, Abreu-Gonzalez R, Bandello F, et al. Does real-time artificial intelligence-based visual pathology enhancement of three-dimensional optical coherence tomography scans optimise treatment decision in patients with nAMD? Rationale and design of the RAZORBILL study. Br J Ophthalmol 2023; 107:96101.
Bhuiyan A, Govindaiah A, Alauddin S, et al. Combined automated screening for age-related macular degeneration and diabetic retinopathy in primary care settings. Ann Eye Sci 2021; 6:12.
Tufail A, Kapetanakis VV, Salas-Vega S, et al. An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness. Health Technol Assess 2016; 20:172.
Gerendas BS, Sadeghipour A, Michl M, et al. Validation of an automated fluid algorithm on real-world data of neovascular age-related macular degeneration over five years. Retina 2022; 42:16731682.
Liu Y, Holekamp NM, Heier JS. Prospective, longitudinal study: daily self-imaging with home OCT for neovascular age-related macular degeneration. Ophthalmol Retina 2022; 6:575585.
Kim JE, Tomkins-Netzer O, Elman MJ, et al. Evaluation of a self-imaging SD-OCT system designed for remote home monitoring. BMC Ophthalmol 2022; 22:261.
Chakravarthy U, Goldenberg D, Young G, et al. Automated identification of lesion activity in neovascular age-related macular degeneration. Ophthalmology 2016; 123:17311736.
Keenan TDL, Goldstein M, Goldenberg D, et al. Prospective, longitudinal pilot study: daily self-imaging with patient-operated home OCT in neovascular age-related macular degeneration. Ophthalmol Sci 2021; 1:100034.
Wittenborn JS, Clemons T, Regillo C, et al. Economic evaluation of a home-based age-related macular degeneration monitoring system. JAMA Ophthalmol 2017; 135:452459.
Chew EY, Clemons TE, Harrington M, et al. Effectiveness of different monitoring modalities in the detection of neovascular age-related macular degeneration: the HOME study, report number 3. Retina 2016; 36:15421547.
De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 2018; 24:13421350.
Seeböck P, Waldstein SM, Klimscha S, et al. Unsupervised identification of disease marker candidates in retinal OCT imaging data. IEEE Trans Med Imaging 2019; 38:10371047.
von der Emde L, Pfau M, Holz FG, et al. AI-based structure-function correlation in age-related macular degeneration. Eye 2021; 35:21102118.
Seeböck P, Vogl W-D, Waldstein SM, et al. Linking function and structure with ReSensNet: predicting retinal sensitivity from OCT using deep learning. Ophthalmol Retina 2022; 6:501511.
Loo J, Clemons TE, Chew EY, et al. Beyond performance metrics: automatic deep learning retinal OCT analysis reproduces clinical trial outcome. Ophthalmology 2020; 127:793801.
Kihara Y, Heeren TFC, Lee CS, et al. Estimating retinal sensitivity using optical coherence tomography with deep-learning algorithms in macular telangiectasia type 2. JAMA Netw Open 2019; 2:e188029.
Crincoli E, Savastano MC, Savastano A, et al. New artificial intelligence analysis for prediction of long-term visual improvement after epiretinal membrane surgery. Retina 2023; 43:173181.
Kim SH, Ahn H, Yang S, et al. Deep learning-based prediction of outcomes following noncomplicated epiretinal membrane surgery. Retina 2022; 42:14651471.
Vogl W-D, Riedl S, Mai J, et al. Predicting topographic disease progression and treatment response of pegcetacoplan in geographic atrophy quantified by deep learning. Ophthalmol Retina 2023; 7:413.
Riedl S, Vogl W-D, Mai J, et al. The effect of pegcetacoplan treatment on photoreceptor maintenance in geographic atrophy monitored by artificial intelligence-based OCT analysis. Ophthalmol Retina 2022; 6:10091018.
Schmidt-Erfurth U, Waldstein SM, Klimscha S, et al. Prediction of individual disease conversion in early AMD using artificial intelligence. Invest Ophthalmol Vis Sci 2018; 59:31993208.
de Sisternes L, Simon N, Tibshirani R, et al. Quantitative SD-OCT imaging biomarkers as indicators of age-related macular degeneration progression. Invest Ophthalmol Vis Sci 2014; 55:70937103.
Schmidt-Erfurth U, Bogunovic H, Sadeghipour A, et al. Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration. Ophthalmol Retina 2018; 2:2430.
Bogunović H, Montuoro A, Baratsits M, et al. Machine learning of the progression of intermediate age-related macular degeneration based on OCT imaging. Invest Ophthalmol Vis Sci 2017; 58:BIO141BIO150.
Gutfleisch M, Ester O, Aydin S, et al. Clinically applicable deep learning-based decision aids for treatment of neovascular AMD. Graefes Arch Clin Exp Ophthalmol 2022; 260:22172230.
Jee D, Yoon JH, Ra H, et al. Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning. Sci Rep 2022; 12:9335.
Pawloff M, Bogunovic H, Gruber A, et al. Systematic correlation of central subfield thickness with retinal fluid volumes quantified by deep learning in the major exudative macular diseases. Retina 2022; 42:831841.
Ting DSW, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol 2019; 103:167175.
Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis. Lancet Digit Health 2021; 3:e195e203.
Wu E, Wu K, Daneshjou R, et al. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat Med 2021; 27:582584.
Ting DSW, Peng L, Varadarajan AV, et al. Deep learning in ophthalmology: the technical and clinical considerations. Prog Retin Eye Res 2019; 72:100759.
Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health 2021; 3:e745e750.
International Medical Device Regulators Forum. International Medical Device Regulators Forum | Home. 2023. Available at: https://www.imdrf.org/international-medical-device-regulators-forum-imdrf [Accessed March 2023].
Medicines & Healthcare products Regulatory Agency. MHRA Products | Home. 2023. Available at: https://products.mhra.gov.uk/ [Accessed March 2023].
Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316:24022410.
Ibrahim H, Liu X, Zariffa N, et al. Health data poverty: an assailable barrier to equitable digital health care. Lancet Digit Health 2021; 3:e260e265.
Phan S, Satoh S, Yoda Y, et al. Evaluation of deep convolutional neural networks for glaucoma detection. Jpn J Ophthalmol 2019; 63:276283.
Sarao V, Veritti D, Lanzetta P. Automated diabetic retinopathy detection with two different retinal imaging devices using artificial intelligence: a comparison study. Graefes Arch Clin Exp Ophthalmol 2020; 258:26472654.
Li J-PO, Liu H, Ting DSJ, et al. Digital technology, tele-medicine and artificial intelligence in ophthalmology: a global perspective. Prog Retin Eye Res 2021; 82:100900.
Choi JY, Yoo TK, Seo JG, et al. Multi-categorical deep learning neural network to classify retinal images: a pilot study employing small database. PLoS One 2017; 12:e0187336.
Vickers AJ, van Calster B, Steyerberg EW. A simple, step-by-step guide to interpreting decision curve analysis. Diag Prog Res 2019; 3:18.
