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Borrelli E, Serafino S, Ricardi F, Coletto A, Neri G, Olivieri C, Ulla L, Foti C, Marolo P, Toro MD, Bandello F, Reibaldi M. Deep Learning in Neovascular Age-Related Macular Degeneration. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:990. [PMID: 38929607 PMCID: PMC11205843 DOI: 10.3390/medicina60060990] [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: 03/17/2024] [Revised: 05/29/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024]
Abstract
Background and objectives: Age-related macular degeneration (AMD) is a complex and multifactorial condition that can lead to permanent vision loss once it progresses to the neovascular exudative stage. This review aims to summarize the use of deep learning in neovascular AMD. Materials and Methods: Pubmed search. Results: Deep learning has demonstrated effectiveness in analyzing structural OCT images in patients with neovascular AMD. This review outlines the role of deep learning in identifying and measuring biomarkers linked to an elevated risk of transitioning to the neovascular form of AMD. Additionally, deep learning techniques can quantify critical OCT features associated with neovascular AMD, which have prognostic implications for these patients. Incorporating deep learning into the assessment of neovascular AMD eyes holds promise for enhancing clinical management strategies for affected individuals. Conclusion: Several studies have demonstrated effectiveness of deep learning in assessing neovascular AMD patients and this has a promising role in the assessment of these patients.
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Affiliation(s)
- Enrico Borrelli
- Division of Ophthalmology, Department of Surgical Sciences, University of Turin, Via Verdi, 8, 10124 Turin, Italy; (S.S.); (F.R.); (A.C.); (G.N.); (C.O.); (L.U.); (C.F.); (M.R.)
- Department of Ophthalmology, “City of Health and Science” Hospital, 10126 Turin, Italy
| | - Sonia Serafino
- Division of Ophthalmology, Department of Surgical Sciences, University of Turin, Via Verdi, 8, 10124 Turin, Italy; (S.S.); (F.R.); (A.C.); (G.N.); (C.O.); (L.U.); (C.F.); (M.R.)
- Department of Ophthalmology, “City of Health and Science” Hospital, 10126 Turin, Italy
| | - Federico Ricardi
- Division of Ophthalmology, Department of Surgical Sciences, University of Turin, Via Verdi, 8, 10124 Turin, Italy; (S.S.); (F.R.); (A.C.); (G.N.); (C.O.); (L.U.); (C.F.); (M.R.)
- Department of Ophthalmology, “City of Health and Science” Hospital, 10126 Turin, Italy
| | - Andrea Coletto
- Division of Ophthalmology, Department of Surgical Sciences, University of Turin, Via Verdi, 8, 10124 Turin, Italy; (S.S.); (F.R.); (A.C.); (G.N.); (C.O.); (L.U.); (C.F.); (M.R.)
- Department of Ophthalmology, “City of Health and Science” Hospital, 10126 Turin, Italy
| | - Giovanni Neri
- Division of Ophthalmology, Department of Surgical Sciences, University of Turin, Via Verdi, 8, 10124 Turin, Italy; (S.S.); (F.R.); (A.C.); (G.N.); (C.O.); (L.U.); (C.F.); (M.R.)
- Department of Ophthalmology, “City of Health and Science” Hospital, 10126 Turin, Italy
| | - Chiara Olivieri
- Division of Ophthalmology, Department of Surgical Sciences, University of Turin, Via Verdi, 8, 10124 Turin, Italy; (S.S.); (F.R.); (A.C.); (G.N.); (C.O.); (L.U.); (C.F.); (M.R.)
- Department of Ophthalmology, “City of Health and Science” Hospital, 10126 Turin, Italy
| | - Lorena Ulla
- Division of Ophthalmology, Department of Surgical Sciences, University of Turin, Via Verdi, 8, 10124 Turin, Italy; (S.S.); (F.R.); (A.C.); (G.N.); (C.O.); (L.U.); (C.F.); (M.R.)
- Department of Ophthalmology, “City of Health and Science” Hospital, 10126 Turin, Italy
| | - Claudio Foti
- Division of Ophthalmology, Department of Surgical Sciences, University of Turin, Via Verdi, 8, 10124 Turin, Italy; (S.S.); (F.R.); (A.C.); (G.N.); (C.O.); (L.U.); (C.F.); (M.R.)
- Department of Ophthalmology, “City of Health and Science” Hospital, 10126 Turin, Italy
| | - Paola Marolo
- Division of Ophthalmology, Department of Surgical Sciences, University of Turin, Via Verdi, 8, 10124 Turin, Italy; (S.S.); (F.R.); (A.C.); (G.N.); (C.O.); (L.U.); (C.F.); (M.R.)
- Department of Ophthalmology, “City of Health and Science” Hospital, 10126 Turin, Italy
| | - Mario Damiano Toro
- Eye Clinic, Public Health Department, University of Naples Federico II, 80138 Naples, Italy;
| | - Francesco Bandello
- Department of Ophthalmology, Vita-Salute San Raffaele University, 20132 Milan, Italy;
- IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Michele Reibaldi
- Division of Ophthalmology, Department of Surgical Sciences, University of Turin, Via Verdi, 8, 10124 Turin, Italy; (S.S.); (F.R.); (A.C.); (G.N.); (C.O.); (L.U.); (C.F.); (M.R.)
- Department of Ophthalmology, “City of Health and Science” Hospital, 10126 Turin, Italy
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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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Crincoli E, Sacconi R, Querques L, Querques G. Artificial intelligence in age-related macular degeneration: state of the art and recent updates. BMC Ophthalmol 2024; 24:121. [PMID: 38491380 PMCID: PMC10943791 DOI: 10.1186/s12886-024-03381-1] [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: 12/05/2023] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
Abstract
Age related macular degeneration (AMD) represents a leading cause of vision loss and it is expected to affect 288 million people by 2040. During the last decade, machine learning technologies have shown great potential to revolutionize clinical management of AMD and support research for a better understanding of the disease. The aim of this review is to provide a panoramic description of all the applications of AI to AMD management and screening that have been analyzed in recent past literature. Deep learning (DL) can be effectively used to diagnose AMD, to predict short term risk of exudation and need for injections within the next 2 years. Moreover, DL technology has the potential to customize anti-VEGF treatment choice with a higher accuracy than expert human experts. In addition, accurate prediction of VA response to treatment can be provided to the patients with the use of ML models, which could considerably increase patients' compliance to treatment in favorable cases. Lastly, AI, especially in the form of DL, can effectively predict conversion to GA in 12 months and also suggest new biomarkers of conversion with an innovative reverse engineering approach.
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Affiliation(s)
- Emanuele Crincoli
- Ophthalmology Unit, "Fondazione Policlinico Universitario A. Gemelli IRCCS", Rome, Italy
| | - Riccardo Sacconi
- Department of Ophthalmology, University Vita-Salute IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Lea Querques
- Department of Ophthalmology, University Vita-Salute IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Giuseppe Querques
- Department of Ophthalmology, University Vita-Salute IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy.
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Jang B, Lee SY, Kim C, Park UC, Kim YG, Lee EK. Preliminary analysis of predicting the first recurrence in patients with neovascular age-related macular degeneration using deep learning. BMC Ophthalmol 2023; 23:499. [PMID: 38062449 PMCID: PMC10702052 DOI: 10.1186/s12886-023-03229-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 11/17/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND To predict, using deep learning, the first recurrence in patients with neovascular age-related macular degeneration (nAMD) after three monthly loading injections of intravitreal anti-vascular endothelial growth factor (anti-VEGF). METHODS Optical coherence tomography (OCT) images were obtained at baseline and after the loading phase. The first recurrence was defined as the initial appearance of a new retinal hemorrhage or intra/subretinal fluid accumulation after the initial resolution of exudative changes after three loading injections. Standard U-Net architecture was used to identify the three retinal fluid compartments, which include pigment epithelial detachment, subretinal fluid, and intraretinal fluid. To predict the first recurrence of nAMD, classification learning was conducted to determine whether the first recurrence occurred within three months after the loading phase. The recurrence classification architecture was built using ResNet50. The model with retinal regions of interest of the entire region and fluid region on OCT at baseline and after the loading phase is presented. RESULTS A total of 1,444 eyes of 1,302 patients were included. The mean duration until the first recurrence after the loading phase was 8.20 ± 15.56 months. The recurrence classification system revealed that the model with the fluid region of OCT after the loading phase provided the highest classification performance, with an area under the receiver operating characteristic curve (AUC) of 0.725 ± 0.012. Heatmap analysis revealed that three pathological fluids, subsided choroidal neovascularization lesions, and hyperreflective foci were important areas for the first recurrence. CONCLUSIONS The deep learning algorithm allowed for the prediction of the first recurrence for three months after the loading phase with adequate feasibility. An automated prediction system may assist in establishing patient-specific treatment plans and the provision of individualized medical care for patients with nAMD.
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Affiliation(s)
- Boa Jang
- Department of Transdisciplinary Medicine, Seoul National University Hospital, #101, Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
- Interdisciplinary Program in Bioengineering, College of Engineering, Seoul National University, Seoul, Republic of Korea
| | - Sang-Yoon Lee
- Seoul Shinsegae Eye Clinic, Seoul, Republic of Korea
| | - Chaea Kim
- Department of Transdisciplinary Medicine, Seoul National University Hospital, #101, Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Un Chul Park
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Hospital, #101, Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Young-Gon Kim
- Department of Transdisciplinary Medicine, Seoul National University Hospital, #101, Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea.
- Department of Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Eun Kyoung Lee
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Hospital, #101, Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea.
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Li D, Ran AR, Cheung CY, Prince JL. Deep learning in optical coherence tomography: Where are the gaps? Clin Exp Ophthalmol 2023; 51:853-863. [PMID: 37245525 PMCID: PMC10825778 DOI: 10.1111/ceo.14258] [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: 03/31/2023] [Revised: 04/24/2023] [Accepted: 05/03/2023] [Indexed: 05/30/2023]
Abstract
Optical coherence tomography (OCT) is a non-invasive optical imaging modality, which provides rapid, high-resolution and cross-sectional morphology of macular area and optic nerve head for diagnosis and managing of different eye diseases. However, interpreting OCT images requires experts in both OCT images and eye diseases since many factors such as artefacts and concomitant diseases can affect the accuracy of quantitative measurements made by post-processing algorithms. Currently, there is a growing interest in applying deep learning (DL) methods to analyse OCT images automatically. This review summarises the trends in DL-based OCT image analysis in ophthalmology, discusses the current gaps, and provides potential research directions. DL in OCT analysis shows promising performance in several tasks: (1) layers and features segmentation and quantification; (2) disease classification; (3) disease progression and prognosis; and (4) referral triage level prediction. Different studies and trends in the development of DL-based OCT image analysis are described and the following challenges are identified and described: (1) public OCT data are scarce and scattered; (2) models show performance discrepancies in real-world settings; (3) models lack of transparency; (4) there is a lack of societal acceptance and regulatory standards; and (5) OCT is still not widely available in underprivileged areas. More work is needed to tackle the challenges and gaps, before DL is further applied in OCT image analysis for clinical use.
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Affiliation(s)
- Dawei Li
- College of Future Technology, Peking University, Beijing, China
| | - An Ran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Carol Y. Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA
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Koseoglu ND, Grzybowski A, Liu TYA. Deep Learning Applications to Classification and Detection of Age-Related Macular Degeneration on Optical Coherence Tomography Imaging: A Review. Ophthalmol Ther 2023; 12:2347-2359. [PMID: 37493854 PMCID: PMC10441995 DOI: 10.1007/s40123-023-00775-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 07/14/2023] [Indexed: 07/27/2023] Open
Abstract
Age-related macular degeneration (AMD) is one of the leading causes of blindness in the elderly, more commonly in developed countries. Optical coherence tomography (OCT) is a non-invasive imaging device widely used for the diagnosis and management of AMD. Deep learning (DL) uses multilayered artificial neural networks (NN) for feature extraction, and is the cutting-edge technique for medical image analysis for diagnostic and prognostication purposes. Application of DL models to OCT image analysis has garnered significant interest in recent years. In this review, we aimed to summarize studies focusing on DL models used in classification and detection of AMD. Additionally, we provide a brief introduction to other DL applications in AMD, such as segmentation, prediction/prognostication, and models trained on multimodal imaging.
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Affiliation(s)
- Neslihan Dilruba Koseoglu
- Wilmer Eye Institute, Johns Hopkins University, 600 N. Wolfe St., Maumenee 726, Baltimore, MD, 21287, USA
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland
| | - T Y Alvin Liu
- Wilmer Eye Institute, Johns Hopkins University, 600 N. Wolfe St., Maumenee 726, Baltimore, MD, 21287, USA.
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Hanson RLW, Airody A, Sivaprasad S, Gale RP. Optical coherence tomography imaging biomarkers associated with neovascular age-related macular degeneration: a systematic review. Eye (Lond) 2023; 37:2438-2453. [PMID: 36526863 PMCID: PMC9871156 DOI: 10.1038/s41433-022-02360-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/13/2022] [Accepted: 12/06/2022] [Indexed: 12/23/2022] Open
Abstract
The aim of this systematic literature review is twofold, (1) detail the impact of retinal biomarkers identifiable via optical coherence tomography (OCT) on disease progression and response to treatment in neovascular age-related macular degeneration (nAMD) and (2) establish which biomarkers are currently identifiable by artificial intelligence (AI) models and the utilisation of this technology. Following the PRISMA guidelines, PubMed was searched for peer-reviewed publications dated between January 2016 and January 2022. POPULATION Patients diagnosed with nAMD with OCT imaging. SETTINGS Comparable settings to NHS hospitals. STUDY DESIGNS Randomised controlled trials, prospective/retrospective cohort studies and review articles. From 228 articles, 130 were full-text reviewed, 50 were removed for falling outside the scope of this review with 10 added from the author's inventory, resulting in the inclusion of 90 articles. From 9 biomarkers identified; intraretinal fluid (IRF), subretinal fluid, pigment epithelial detachment, subretinal hyperreflective material (SHRM), retinal pigmental epithelial (RPE) atrophy, drusen, outer retinal tabulation (ORT), hyperreflective foci (HF) and retinal thickness, 5 are considered pertinent to nAMD disease progression; IRF, SHRM, drusen, ORT and HF. A number of these biomarkers can be classified using current AI models. Significant retinal biomarkers pertinent to disease activity and progression in nAMD are identifiable via OCT; IRF being the most important in terms of the significant impact on visual outcome. Incorporating AI into ophthalmology practice is a promising advancement towards automated and reproducible analyses of OCT data with the ability to diagnose disease and predict future disease conversion. SYSTEMATIC REVIEW REGISTRATION This review has been registered with PROSPERO (registration ID: CRD42021233200).
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Affiliation(s)
- Rachel L W Hanson
- Academic Unit of Ophthalmology, York and Scarborough Teaching Hospitals NHS Foundation Trust, York, UK
| | - Archana Airody
- Academic Unit of Ophthalmology, York and Scarborough Teaching Hospitals NHS Foundation Trust, York, UK
| | - Sobha Sivaprasad
- Moorfields National Institute of Health Research, Biomedical Research Centre, London, UK
| | - Richard P Gale
- Academic Unit of Ophthalmology, York and Scarborough Teaching Hospitals NHS Foundation Trust, York, UK.
- Hull York Medical School, University of York, York, UK.
- York Biomedical Research Institute, University of York, York, UK.
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Muntean GA, Marginean A, Groza A, Damian I, Roman SA, Hapca MC, Muntean MV, Nicoară SD. The Predictive Capabilities of Artificial Intelligence-Based OCT Analysis for Age-Related Macular Degeneration Progression-A Systematic Review. Diagnostics (Basel) 2023; 13:2464. [PMID: 37510207 PMCID: PMC10378064 DOI: 10.3390/diagnostics13142464] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/16/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
The era of artificial intelligence (AI) has revolutionized our daily lives and AI has become a powerful force that is gradually transforming the field of medicine. Ophthalmology sits at the forefront of this transformation thanks to the effortless acquisition of an abundance of imaging modalities. There has been tremendous work in the field of AI for retinal diseases, with age-related macular degeneration being at the top of the most studied conditions. The purpose of the current systematic review was to identify and evaluate, in terms of strengths and limitations, the articles that apply AI to optical coherence tomography (OCT) images in order to predict the future evolution of age-related macular degeneration (AMD) during its natural history and after treatment in terms of OCT morphological structure and visual function. After a thorough search through seven databases up to 1 January 2022 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 1800 records were identified. After screening, 48 articles were selected for full-text retrieval and 19 articles were finally included. From these 19 articles, 4 articles concentrated on predicting the anti-VEGF requirement in neovascular AMD (nAMD), 4 articles focused on predicting anti-VEGF efficacy in nAMD patients, 3 articles predicted the conversion from early or intermediate AMD (iAMD) to nAMD, 1 article predicted the conversion from iAMD to geographic atrophy (GA), 1 article predicted the conversion from iAMD to both nAMD and GA, 3 articles predicted the future growth of GA and 3 articles predicted the future outcome for visual acuity (VA) after anti-VEGF treatment in nAMD patients. Since using AI methods to predict future changes in AMD is only in its initial phase, a systematic review provides the opportunity of setting the context of previous work in this area and can present a starting point for future research.
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Affiliation(s)
- George Adrian Muntean
- Department of Ophthalmology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Emergency County Hospital, 400347 Cluj-Napoca, Romania
| | - Anca Marginean
- Department of Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Adrian Groza
- Department of Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Ioana Damian
- Department of Ophthalmology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Emergency County Hospital, 400347 Cluj-Napoca, Romania
| | - Sara Alexia Roman
- Faculty of Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Mădălina Claudia Hapca
- Department of Ophthalmology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Emergency County Hospital, 400347 Cluj-Napoca, Romania
| | - Maximilian Vlad Muntean
- Plastic Surgery Department, "Prof. Dr. I. Chiricuta" Institute of Oncology, 400015 Cluj-Napoca, Romania
| | - Simona Delia Nicoară
- Department of Ophthalmology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Emergency County Hospital, 400347 Cluj-Napoca, Romania
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Chandra RS, Ying GS. Evaluation of Multiple Machine Learning Models for Predicting Number of Anti-VEGF Injections in the Comparison of AMD Treatment Trials (CATT). Transl Vis Sci Technol 2023; 12:18. [PMID: 36633874 PMCID: PMC9840444 DOI: 10.1167/tvst.12.1.18] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Purpose To apply machine learning models for predicting the number of pro re nata (PRN) injections of antivascular endothelial growth factor (anti-VEGF) for neovascular age-related macular degeneration (nAMD) in two years in the Comparison of AMD (age-related macular degeneration) Treatments Trials. Methods The data from 493 eligible participants randomized to PRN treatment of ranibizumab or bevacizumab were used for training (n = 393) machine learning models including support-vector machine (SVM), random forest, and extreme gradient boosting (XGBoost) models. Model performances of prediction using clinical and image data from baseline, weeks 4, 8, and 12 were evaluated by the area under the receiver operating characteristic curve (AUC) for predicting few (≤8) or many (≥19) injections, by R2 and mean absolute error (MAE) for predicting the total number of injections in two years. The best model was selected for final validation on a test dataset (n = 100). Results Using training data up to week 12, the models achieved AUCs of 0.79-0.82 and 0.79-0.81 for predicting few and many injections, respectively, with R2 of 0.34-0.36 (MAE = 4.45-4.58 injections) for predicting total injections in two years from cross-validation. In final validation on the test dataset, the SVM model had AUCs of 0.77 and 0.82 for predicting few and many injections, respectively, with R2 of 0.44 (MAE = 3.92 injections). Important features included fluid in optical coherence tomography, lesion characteristics, and treatment trajectory in the first three months. Conclusions Machine learning models using loading dose phase data have the potential to predict two-year anti-VEGF demand for nAMD and quantify feature importance for these predictions. Translational Relevance Prediction of anti-VEGF injections using machine learning models from readily available data, after further validation on independent datasets, has the potential to help optimize treatment protocols and outcomes for nAMD patients in an individualized manner.
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Affiliation(s)
- Rajat S. Chandra
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Gui-shuang Ying
- Center for Preventive Ophthalmology and Biostatistics, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Natural History of the Relative Ellipsoid Zone Reflectivity in Age-Related Macular Degeneration. Ophthalmol Retina 2022; 6:1165-1172. [PMID: 35709960 DOI: 10.1016/j.oret.2022.06.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 05/08/2022] [Accepted: 06/06/2022] [Indexed: 01/06/2023]
Abstract
PURPOSE Relative ellipsoid zone reflectivity (rEZR) has been reported to be reduced in intermediate age-related macular degeneration (iAMD). However, longitudinal changes in rEZR remain unknown. This study investigated the natural history of rEZR in iAMD and its association with risk factors for disease progression, including the presence or extent of drusen volume, reticular pseudodrusen (RPD), and pigmentary abnormalities (PAs). DESIGN Longitudinal observational study. PARTICIPANTS Subjects with bilateral large drusen. METHODS Spectral-domain (SD) OCT images of both eyes from each participant were obtained every 6 months for 3 years. Using an automated rEZR determination approach, the average rEZR of the central 20° macula was determined for each SD-OCT volume scan. Linear mixed models were used to determine the rate of change in rEZR with age (using the cross-sectional data at baseline) and over time (longitudinal data) and the interactions between the rate of rEZR changes with AMD risk factors at baseline. MAIN OUTCOME MEASURES Relative ellipsoid zone reflectivity and its rate of change with age and over time. RESULTS A total of 280 eyes from 140 individuals with bilateral large drusen were included in this study. Cross-sectional data showed that rEZR reduced with increasing age (-8.4 arbitrary units [AUs] per decade; 95% confidence interval [CI], -11.5 to -5.2; P < 0.001). Longitudinal data showed that, on average, rEZR declined at a rate of -2.1 AU per year (95% CI, -2.6 to -1.6 AU per year; P < 0.001). Larger RPD area (P = 0.042) at baseline was associated with a faster rate of rEZR decline over time, whereas the presence of PAs and the drusen volume at baseline showed no significant association with rEZR decline over time (P = 0.068 and P = 0.529, respectively). CONCLUSIONS The rEZR significantly reduces over 3 years in subjects with iAMD, and both the presence and increasing extent of coexistent RPD at baseline are associated with a faster rate of decline. These findings warrant further studies to understand the value of rEZR as a biomarker of AMD progression.
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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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12
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Bogunović H, Mares V, Reiter GS, Schmidt-Erfurth U. Predicting treat-and-extend outcomes and treatment intervals in neovascular age-related macular degeneration from retinal optical coherence tomography using artificial intelligence. Front Med (Lausanne) 2022; 9:958469. [PMID: 36017006 PMCID: PMC9396241 DOI: 10.3389/fmed.2022.958469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 07/05/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeTo predict visual outcomes and treatment needs in a treat & extend (T&E) regimen in neovascular age-related macular degeneration (nAMD) using a machine learning model based on quantitative optical coherence tomography (OCT) imaging biomarkers.Materials and methodsStudy eyes of 270 treatment-naïve subjects, randomized to receiving ranibizumab therapy in the T&E arm of a randomized clinical trial were considered. OCT volume scans were processed at baseline and at the first follow-up visit 4 weeks later. Automated image segmentation was performed, where intraretinal (IRF), subretinal (SRF) fluid, pigment epithelial detachment (PED), hyperreflective foci, and the photoreceptor layer were delineated using a convolutional neural network (CNN). A set of respective quantitative imaging biomarkers were computed across an Early Treatment Diabetic Retinopathy Study (ETDRS) grid to describe the retinal pathomorphology spatially and its change after the first injection. Lastly, using the computed set of OCT features and available clinical and demographic information, predictive models of outcomes and retreatment intervals were built using machine learning and their performance evaluated with a 10-fold cross-validation.ResultsData of 228 evaluable patients were included, as some had missing scans or were lost to follow-up. Of those patients, 55% reached and maintained long (8, 10, 12 weeks) and another 45% stayed at short (4, 6 weeks) treatment intervals. This provides further evidence for a high disease activity in a major proportion of patients. The model predicted the extendable treatment interval group with an AUROC of 0.71, and the visual outcome with an AUROC of up to 0.87 when utilizing both, clinical and imaging features. The volume of SRF and the volume of IRF, remaining at the first follow-up visit, were found to be the most important predictive markers for treatment intervals and visual outcomes, respectively, supporting the important role of quantitative fluid parameters on OCT.ConclusionThe proposed Artificial intelligence (AI) methodology was able to predict visual outcomes and retreatment intervals of a T&E regimen from a single injection. The result of this study is an urgently needed step toward AI-supported management of patients with active and progressive nAMD.
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Affiliation(s)
- Hrvoje Bogunović
- Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology, Medical University of Vienna, Vienna, Austria
| | - Virginia Mares
- Department of Ophthalmology, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Gregor S. Reiter
- Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology, Medical University of Vienna, Vienna, Austria
| | - Ursula Schmidt-Erfurth
- Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology, Medical University of Vienna, Vienna, Austria
- *Correspondence: Ursula Schmidt-Erfurth,
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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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Runoff Probability Prediction Model Based on Natural Gradient Boosting with Tree-Structured Parzen Estimator Optimization. WATER 2022. [DOI: 10.3390/w14040545] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Accurate and reliable runoff prediction is critical for solving problems related to water resource planning and management. Deterministic runoff prediction methods cannot meet the needs of risk analysis and decision making. In this study, a runoff probability prediction model based on natural gradient boosting (NGboost) with tree-structured parzen estimator (TPE) optimization is proposed. The model obtains the probability distribution of the predicted runoff. The TPE algorithm was used for the hyperparameter optimization of the model to improve the prediction. The model was applied to the prediction of runoff on the monthly, weekly and daily scales at the Yichang and Pingshan stations in the upper Yangtze River. We also tested the prediction effectiveness of the models using exponential, normal and lognormal distributions for different flow characteristics and time scales. The results show that in terms of deterministic prediction, the proposed model improved in all indicators compared to the benchmark model. The root mean square error of the monthly runoff prediction was reduced by 9% on average and 7% on the daily scale. In probabilistic prediction, the proposed model can provide reliable probabilistic prediction on weekly and daily scales.
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