用深度学习从角膜照片识别感染类型，助力低收入国家眼病诊疗
Classification of Keratitis from Eye Corneal Photographs using Deep Learning
- 设计多任务模型，共享特征提取层并分头分类三种感染源
- 对阿米巴感染的诊断准确率最高，达94.5%-96.2%的AUROC
- 发现性别影响阿米巴预测，年龄影响细菌和真菌判断
角膜炎是导致低收入和中等收入国家10%视觉损伤的原因，常见病因包括细菌、真菌或阿米巴。由于实验室检测成本高且资源有限，诊断常依赖临床观察，准确性较低。本研究比较三种深度学习方法：三个独立二分类模型；共享主干网络的多任务模型（Multitask V1）；共享主干网络的多头分类多任务模型（Multitask V2）。基于巴西私有角膜数据集评估，Multitask V2表现最佳，细菌感染的受试者工作特征曲线下面积（AUROC）置信区间为0.7413-0.7740，真菌为0.8395-0.8725，阿米巴为0.9448-0.9616。统计分析显示，性别显著影响阿米巴感染预测，年龄则可能影响细菌和真菌预测。
原文摘要 · Abstract (English)
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.
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