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Abstract #104897 Published in IGR 23-2
Predicting demographic characteristics from anterior segment OCT images with deep learning: A study protocol
Lee YJ; Sun S; Kim YKPLoS ONE 2022; 17: e0270493
INTRODUCTION: Anterior segment optical coherence tomography (AS-OCT) is a non-contact, rapid, and high-resolution in vivo modality for imaging of the eyeball's anterior segment structures. Because progressive anterior segment deformation is a hallmark of certain eye diseases such as angle-closure glaucoma, identification of AS-OCT structural changes over time is fundamental to their diagnosis and monitoring. Detection of pathologic damage, however, relies on the ability to differentiate it from normal, age-related structural changes. METHODS AND ANALYSIS: This proposed large-scale, retrospective cross-sectional study will determine whether demographic characteristics including age can be predicted from deep learning analysis of AS-OCT images; it will also assess the importance of specific anterior segment areas of the eyeball to the prediction. We plan to extract, from SUPREME®, a clinical data warehouse (CDW) of Seoul National University Hospital (SNUH; Seoul, South Korea), a list of patients (at least 2,000) who underwent AS-OCT imaging between 2008 and 2020. AS-OCT images as well as demographic characteristics including age, gender, height, weight and body mass index (BMI) will be collected from electronic medical records (EMRs). The dataset of horizontal AS-OCT images will be split into training (80%), validation (10%), and test (10%) datasets, and a Vision Transformer (ViT) model will be built to predict demographics. Gradient-weighted Class Activation Mapping (Grad-CAM) will be used to visualize the regions of AS-OCT images that contributed to the model's decisions. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) will be applied to evaluate the model performance. CONCLUSION: This paper presents a study protocol for prediction of demographic characteristics from AS-OCT images of the eyeball using a deep learning model. The results of this study will aid clinicians in understanding and identifying age-related structural changes and other demographics-based structural differences. TRIAL REGISTRATION: Registration ID with open science framework: 10.17605/OSF.IO/FQ46X.
Department of Ophthalmology, Seoul National University Hospital, Seoul, Korea.
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