Retina (Philadelphia, Pa.) · 2026
DIAGNOSTIC PERFORMANCE OF MACHINE LEARNING TECHNOLOGY USING OPTICAL COHERENCE TOMOGRAPHIC IMAGE IN RETINAL DISEASES PRESENTED WITH SUBRETINAL FLUID.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Khakhai Panita, Paisarnsrisomsuk Sarun, Wiratchotisatian Pitchaya, Tangprasert Kwankhao, Laovirojjanakul Wipada
Аннотация
PURPOSE: To study the diagnostic performance of machine learning in the diagnosis of three retinal diseases presented with subretinal fluid: central serous chorioretinopathy, polypoidal choroidal vasculopathy, and Vogt-Koyanagi-Harada disease by using optical coherence tomography (OCT) images.
METHODS: Optical coherence tomography scans from 259 patients presented with subretinal fluid from central serous chorioretinopathy, polypoidal choroidal vasculopathy, and Vogt-Koyanagi-Harada disease, and 108 patients with no subretinal fluid were used to train the machine learning. Three methods include using all scans from each eye, using only one-line across fovea images and using both infrared and OCT images. The result was reported in precision, recall, F-1 score, accuracy, and area under the curve.
RESULTS: Using one-line across the fovea OCT images combined with an infrared photo achieved the best performance with an overall area under the curve score of 0.965 and an accuracy of 87.10%. The precision, recall, and F1-score were 88.43%, 87.19%, and 87.18%, respectively.
CONCLUSION: The authors introduced a new aspect of developing a machine learning model to differentiate among three major retinal diseases presented with subretinal fluid from OCT images. The use of one-line across fovea OCT images combined with infrared photograph showed the highest diagnostic performance because of the possible imaging biomarkers.
METHODS: Optical coherence tomography scans from 259 patients presented with subretinal fluid from central serous chorioretinopathy, polypoidal choroidal vasculopathy, and Vogt-Koyanagi-Harada disease, and 108 patients with no subretinal fluid were used to train the machine learning. Three methods include using all scans from each eye, using only one-line across fovea images and using both infrared and OCT images. The result was reported in precision, recall, F-1 score, accuracy, and area under the curve.
RESULTS: Using one-line across the fovea OCT images combined with an infrared photo achieved the best performance with an overall area under the curve score of 0.965 and an accuracy of 87.10%. The precision, recall, and F1-score were 88.43%, 87.19%, and 87.18%, respectively.
CONCLUSION: The authors introduced a new aspect of developing a machine learning model to differentiate among three major retinal diseases presented with subretinal fluid from OCT images. The use of one-line across fovea OCT images combined with infrared photograph showed the highest diagnostic performance because of the possible imaging biomarkers.