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Ying B, Chandra RS, Wang J, Cui H, Oatts JT. Machine Learning Models for Predicting Cycloplegic Refractive Error and Myopia Status Based on Non-Cycloplegic Data in Chinese Students. Transl Vis Sci Technol 2024; 13:16. [PMID: 39120886 DOI: 10.1167/tvst.13.8.16] [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: 08/10/2024] Open
Abstract
Purpose To develop and validate machine learning (ML) models for predicting cycloplegic refractive error and myopia status using noncycloplegic refractive error and biometric data. Methods Cross-sectional study of children aged five to 18 years who underwent biometry and autorefraction before and after cycloplegia. Myopia was defined as cycloplegic spherical equivalent refraction (SER) ≤-0.5 Diopter (D). Models were evaluated for predicting SER using R2 and mean absolute error (MAE) and myopia status using area under the receiver operating characteristic (ROC) curve (AUC). Best-performing models were further evaluated using sensitivity/specificity and comparison of observed versus predicted myopia prevalence rate overall and in each age group. Independent data sets were used for training (n = 1938) and validation (n = 1476). Results In the validation dataset, ML models predicted cycloplegic SER with high R2 (0.913-0.935) and low MAE (0.393-0.480 D). The AUC for predicting myopia was high (0.984-0.987). The best-performing model for SER (XGBoost) had high sensitivity and specificity (91.1% and 97.2%). Random forest (RF), the best-performing model for myopia, had high sensitivity and specificity (92.2% and 96.9%). Within each age group, difference between predicted and actual myopia prevalence was within 4%. Conclusions Using noncycloplegic refractive error and ocular biometric data, ML models performed well for predicting cycloplegic SER and myopia status. When measuring cycloplegic SER is not feasible, ML may provide a useful tool for estimating cycloplegic SER and myopia prevalence rate in epidemiological studies. Translational Relevance Using ML to predict cycloplegic refraction based on noncycloplegic data is a powerful tool for large, population-based studies of refractive error.
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Affiliation(s)
- Bole Ying
- Lower Merion High School, Ardmore, PA, USA
| | - Rajat S Chandra
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jianyong Wang
- Department of Ophthalmology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, P. R. China
| | - Hongguang Cui
- Department of Ophthalmology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, P. R. China
| | - Julius T Oatts
- Department of Ophthalmology, University of California San Francisco, San Francisco, CA, USA
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Sorrentino FS, Gardini L, Fontana L, Musa M, Gabai A, Maniaci A, Lavalle S, D’Esposito F, Russo A, Longo A, Surico PL, Gagliano C, Zeppieri M. Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence. J Pers Med 2024; 14:690. [PMID: 39063944 PMCID: PMC11278069 DOI: 10.3390/jpm14070690] [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: 05/30/2024] [Revised: 06/24/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND An increasing amount of people are globally affected by retinal diseases, such as diabetes, vascular occlusions, maculopathy, alterations of systemic circulation, and metabolic syndrome. AIM This review will discuss novel technologies in and potential approaches to the detection and diagnosis of retinal diseases with the support of cutting-edge machines and artificial intelligence (AI). METHODS The demand for retinal diagnostic imaging exams has increased, but the number of eye physicians or technicians is too little to meet the request. Thus, algorithms based on AI have been used, representing valid support for early detection and helping doctors to give diagnoses and make differential diagnosis. AI helps patients living far from hub centers to have tests and quick initial diagnosis, allowing them not to waste time in movements and waiting time for medical reply. RESULTS Highly automated systems for screening, early diagnosis, grading and tailored therapy will facilitate the care of people, even in remote lands or countries. CONCLUSION A potential massive and extensive use of AI might optimize the automated detection of tiny retinal alterations, allowing eye doctors to perform their best clinical assistance and to set the best options for the treatment of retinal diseases.
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Affiliation(s)
| | - Lorenzo Gardini
- Unit of Ophthalmology, Department of Surgical Sciences, Ospedale Maggiore, 40100 Bologna, Italy; (F.S.S.)
| | - Luigi Fontana
- Ophthalmology Unit, Department of Surgical Sciences, Alma Mater Studiorum University of Bologna, IRCCS Azienda Ospedaliero-Universitaria Bologna, 40100 Bologna, Italy
| | - Mutali Musa
- Department of Optometry, University of Benin, Benin City 300238, Edo State, Nigeria
| | - Andrea Gabai
- Department of Ophthalmology, Humanitas-San Pio X, 20159 Milan, Italy
| | - Antonino Maniaci
- Department of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
| | - Salvatore Lavalle
- Department of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
| | - Fabiana D’Esposito
- Imperial College Ophthalmic Research Group (ICORG) Unit, Imperial College, 153-173 Marylebone Rd, London NW15QH, UK
- Department of Neurosciences, Reproductive Sciences and Dentistry, University of Naples Federico II, Via Pansini 5, 80131 Napoli, Italy
| | - Andrea Russo
- Department of Ophthalmology, University of Catania, 95123 Catania, Italy
| | - Antonio Longo
- Department of Ophthalmology, University of Catania, 95123 Catania, Italy
| | - Pier Luigi Surico
- Schepens Eye Research Institute of Mass Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy
| | - Caterina Gagliano
- Department of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
- Eye Clinic, Catania University, San Marco Hospital, Viale Carlo Azeglio Ciampi, 95121 Catania, Italy
| | - Marco Zeppieri
- Department of Ophthalmology, University Hospital of Udine, 33100 Udine, Italy
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Chandra RS, Ying GS. Predicting Visual Acuity Responses to Anti-VEGF Treatment in the Comparison of Age-related Macular Degeneration Treatments Trials Using Machine Learning. Ophthalmol Retina 2024; 8:419-430. [PMID: 38008218 PMCID: PMC11070304 DOI: 10.1016/j.oret.2023.11.010] [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: 07/25/2023] [Revised: 11/06/2023] [Accepted: 11/20/2023] [Indexed: 11/28/2023]
Abstract
PURPOSE To evaluate multiple machine learning (ML) models for predicting 2-year visual acuity (VA) responses to anti-vascular endothelial growth factor (anti-VEGF) treatment in the Comparison of Age-related Macular Degeneration (AMD) Treatments Trials (CATT) for patients with neovascular AMD (nAMD). DESIGN Secondary analysis of public data from a randomized clinical trial. PARTICIPANTS A total of 1029 CATT participants who completed 2 years of follow-up with untreated active nAMD and baseline VA between 20/25 and 20/320 in the study eye. METHODS Five ML models (support vector machine, random forest, extreme gradient boosting, multilayer perceptron neural network, and lasso) were applied to clinical and image data from baseline and weeks 4, 8, and 12 for predicting 4 VA outcomes (≥ 15-letter VA gain, ≥ 15-letter VA loss, VA change from baseline, and actual VA) at 2 years. The CATT data from 1029 participants were randomly split for training (n = 717), from which the models were trained using 10-fold cross-validation, and for final validation on a test data set (n = 312). MAIN OUTCOME MEASURES Performances of ML models were assessed by R2 and mean absolute error (MAE) for predicting VA change from baseline and actual VA at 2 years, by the area under the receiver operating characteristic curve (AUC) for predicting ≥ 15-letter VA gain and loss from baseline. RESULTS Using training data up to week 12, the ML models from cross-validation achieved mean R2 of 0.24 to 0.29 (MAE = 9.1-9.8 letters) for predicting VA change and 0.37 to 0.41 (MAE = 9.3-10.2 letters) for predicting actual VA at 2 years. The mean AUCs for predicting ≥ 15-letter VA gain and loss at 2 years was 0.84 to 0.85 and 0.58 to 0.73, respectively. In final validation on the test data set up to week 12, the models had an R2 of 0.33 to 0.38 (MAE = 8.9-9.9 letters) for predicting VA change, an R2 of 0.37 to 0.45 (MAE = 8.8-10.2 letters) for predicting actual VA at 2 years, and AUCs of 0.85 to 0.87 and 0.67 to 0.79 for predicting ≥ 15-letter VA gain and loss, respectively. CONCLUSIONS Machine learning models have the potential to predict 2-year VA response to anti-VEGF treatment using clinical and imaging features from the loading dose phase, which can aid in decision-making around treatment protocols for patients with nAMD. FINANCIAL DISCLOSURE(S) The author(s) have no proprietary or commercial interest in any materials discussed in this article.
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Affiliation(s)
- Rajat S Chandra
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Gui-Shuang Ying
- Department of Ophthalmology, Center for Preventive Ophthalmology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
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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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