In The EPMA journal
AIMS : Computer-aided detection systems for retinal fluid could be beneficial for disease monitoring and management by chronic age-related macular degeneration (AMD) and diabetic retinopathy (DR) patients, to assist in disease prevention via early detection before the disease progresses to a "wet AMD" pathology or diabetic macular edema (DME), requiring treatment. We propose a proof-of-concept AI-based app to help predict fluid via a "fluid score", prevent fluid progression, and provide personalized, serial monitoring, in the context of predictive, preventive, and personalized medicine (PPPM) for patients at risk of retinal fluid complications.
METHODS : The app comprises a convolutional neural network-Vision Transformer (CNN-ViT)-based segmentation deep learning (DL) network, trained on a small dataset of 100 training images (augmented to 992 images) from the Singapore Epidemiology of Eye Diseases (SEED) study, together with a CNN-based classification network trained on 8497 images, that can detect fluid vs. non-fluid optical coherence tomography (OCT) scans. Both networks are validated on external datasets.
RESULTS : Internal testing for our segmentation network produced an IoU score of 83.0% (95% CI = 76.7-89.3%) and a DICE score of 90.4% (86.3-94.4%); for external testing, we obtained an IoU score of 66.7% (63.5-70.0%) and a DICE score of 78.7% (76.0-81.4%). Internal testing of our classification network produced an area under the receiver operating characteristics curve (AUC) of 99.18%, and a Youden index threshold of 0.3806; for external testing, we obtained an AUC of 94.55%, and an accuracy of 94.98% and an F1 score of 85.73% with Youden index.
CONCLUSION : We have developed an AI-based app with an alternative transformer-based segmentation algorithm that could potentially be applied in the clinic with a PPPM approach for serial monitoring, and could allow for the generation of retrospective data to research into the varied use of treatments for AMD and DR. The modular system of our app can be scaled to add more iterative features based on user feedback for more efficient monitoring. Further study and scaling up of the algorithm dataset could potentially boost its usability in a real-world clinical setting.
SUPPLEMENTARY INFORMATION : The online version contains supplementary material available at 10.1007/s13167-022-00301-5.
Quek Ten Cheer, Takahashi Kengo, Kang Hyun Goo, Thakur Sahil, Deshmukh Mihir, Tseng Rachel Marjorie Wei Wen, Nguyen Helen, Tham Yih-Chung, Rim Tyler Hyungtaek, Kim Sung Soo, Yanagi Yasuo, Liew Gerald, Cheng Ching-Yu
2022-Dec
Age-related macular degeneration, Anti-vascular endothelial growth factor, Computer-aided detection, Deep learning, Diabetic macular edema, Diabetic retinopathy, Ophthalmology, Optical coherence tomography, Predictive, preventive, and personalized OCT, Retinal fluid
