Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022
Clinically applicable deep learning-based decision aids for treatment of neovascular AMD.
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.
Gutfleisch Matthias, Ester Oliver, Aydin Sökmen, Quassowski Martin, Spital Georg, Lommatzsch Albrecht, Rothaus Kai, Dubis Adam Michael, Pauleikhoff Daniel
Аннотация
PURPOSE: Anti-vascular endothelial growth factor (Anti-VEGF) therapy is currently seen as the standard for treatment of neovascular AMD (nAMD). However, while treatments are highly effective, decisions for initial treatment and retreatment are often challenging for non-retina specialists. The purpose of this study is to develop convolutional neural networks (CNN) that can differentiate treatment indicated presentations of nAMD for referral to treatment centre based solely on SD-OCT. This provides the basis for developing an applicable medical decision support system subsequently.
METHODS: SD-OCT volumes of a consecutive real-life cohort of 1503 nAMD patients were analysed and two experiments were carried out. To differentiate between no treatment class vs. initial treatment nAMD class and stabilised nAMD vs. active nAMD, two novel CNNs, based on SD-OCT volume scans, were developed and tested for robustness and performance. In a step towards explainable artificial intelligence (AI), saliency maps of the SD-OCT volume scans of 24 initial indication decisions with a predicted probability of > 97.5% were analysed (score 0-2 in respect to staining intensity). An AI benchmark against retina specialists was performed.
RESULTS: At the first experiment, the area under curve (AUC) of the receiver-operating characteristic (ROC) for the differentiation of patients for the initial analysis was 0.927 (standard deviation (SD): 0.018), for the second experiment (retreatment analysis) 0.865 (SD: 0.027). The results were robust to downsampling (¼ of the original resolution) and cross-validation (tenfold). In addition, there was a high correlation between the AI analysis and expert opinion in a sample of 102 cases for differentiation of patients needing treatment (κ = 0.824). On saliency maps, the relevant structures for individual initial indication decisions were the retina/vitreous interface, subretinal space, intraretinal cysts, subretinal pigment epithelium space, and the choroid.
CONCLUSION: The developed AI algorithms can define and differentiate presentations of AMD, which should be referred for treatment or retreatment with anti-VEGF therapy. This may support non-retina specialists to interpret SD-OCT on expert opinion level. The individual decision of the algorithm can be supervised by saliency maps.
METHODS: SD-OCT volumes of a consecutive real-life cohort of 1503 nAMD patients were analysed and two experiments were carried out. To differentiate between no treatment class vs. initial treatment nAMD class and stabilised nAMD vs. active nAMD, two novel CNNs, based on SD-OCT volume scans, were developed and tested for robustness and performance. In a step towards explainable artificial intelligence (AI), saliency maps of the SD-OCT volume scans of 24 initial indication decisions with a predicted probability of > 97.5% were analysed (score 0-2 in respect to staining intensity). An AI benchmark against retina specialists was performed.
RESULTS: At the first experiment, the area under curve (AUC) of the receiver-operating characteristic (ROC) for the differentiation of patients for the initial analysis was 0.927 (standard deviation (SD): 0.018), for the second experiment (retreatment analysis) 0.865 (SD: 0.027). The results were robust to downsampling (¼ of the original resolution) and cross-validation (tenfold). In addition, there was a high correlation between the AI analysis and expert opinion in a sample of 102 cases for differentiation of patients needing treatment (κ = 0.824). On saliency maps, the relevant structures for individual initial indication decisions were the retina/vitreous interface, subretinal space, intraretinal cysts, subretinal pigment epithelium space, and the choroid.
CONCLUSION: The developed AI algorithms can define and differentiate presentations of AMD, which should be referred for treatment or retreatment with anti-VEGF therapy. This may support non-retina specialists to interpret SD-OCT on expert opinion level. The individual decision of the algorithm can be supervised by saliency maps.