Automated detection of celiac disease on duodenal biopsy slides: a deep learning approach
This work addresses the need for more efficient and accurate diagnosis of celiac disease for pathologists, though it is incremental as it applies an existing deep learning method to a specific medical imaging task.
The researchers tackled the problem of diagnosing celiac disease from duodenal biopsy slides by developing a deep learning model that achieved accuracies of 95.3% for celiac disease, 91.0% for normal tissue, and 89.2% for nonspecific duodenitis, with AUCs over 0.95 for all classes.
Celiac disease prevalence and diagnosis have increased substantially in recent years. The current gold standard for celiac disease confirmation is visual examination of duodenal mucosal biopsies. An accurate computer-aided biopsy analysis system using deep learning can help pathologists diagnose celiac disease more efficiently. In this study, we trained a deep learning model to detect celiac disease on duodenal biopsy images. Our model uses a state-of-the-art residual convolutional neural network to evaluate patches of duodenal tissue and then aggregates those predictions for whole-slide classification. We tested the model on an independent set of 212 images and evaluated its classification results against reference standards established by pathologists. Our model identified celiac disease, normal tissue, and nonspecific duodenitis with accuracies of 95.3%, 91.0%, and 89.2%, respectively. The area under the receiver operating characteristic curve was greater than 0.95 for all classes. We have developed an automated biopsy analysis system that achieves high performance in detecting celiac disease on biopsy slides. Our system can highlight areas of interest and provide preliminary classification of duodenal biopsies prior to review by pathologists. This technology has great potential for improving the accuracy and efficiency of celiac disease diagnosis.