2024/11/13 by João Melo Beirão, João Matos, Beirão, Maria Miguel +11
Medicine · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Glaucoma and retinal disorders #Machine Learning (cs.LG) #Retinal Imaging and Analysis #Retinal and Optic Conditions
paper · pdf · doi:10.48550/arxiv.2411.08935
openalex publication_date 2024/11/13 · openalex created_date 2024/11/17 · openalex updated_date 2026/07/28
Keratitis is an inflammatory corneal condition responsible for 10% of visual impairment in low- and middle-income countries (LMICs), with bacteria, fungi, or amoeba as the most common infection etiologies. While an accurate and timely diagnosis is crucial for the selected treatment and the patients' sight outcomes, due to the high cost and limited availability of laboratory diagnostics in LMICs, diagnosis is often made by clinical observation alone, despite its lower accuracy. In this study, we investigate and compare different deep learning approaches to diagnose the source of infection: 1) three separate binary models for infection type predictions; 2) a multitask model with a shared backbone and three parallel classification layers (Multitask V1); and, 3) a multitask model with a shared backbone and a multi-head classification layer (Multitask V2). We used a private Brazilian cornea dataset to conduct the empirical evaluation. We achieved the best results with Multitask V2, with an area under the receiver operating characteristic curve (AUROC) confidence intervals of 0.7413-0.7740 (bacteria), 0.8395-0.8725 (fungi), and 0.9448-0.9616 (amoeba). A statistical analysis of the impact of patient features on models' performance revealed that sex significantly affects amoeba infection prediction, and age seems to affect fungi and bacteria predictions.