Automated Detection of Celiac Disease on Duodenal Biopsy Slides: A Deep Learning Approach

Abstract

Context: Celiac disease (CD) prevalence and diagnosis have increased substantially in recent years. The current gold standard for CD confirmation is visual examination of duodenal mucosal biopsies. An accurate computer-aided biopsy analysis system using deep learning can help pathologists diagnose CD more efficiently.

Subjects and methods: In this study, we trained a deep learning model to detect CD 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.

Results: Our model identified CD, 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 >0.95 for all classes.

Conclusions: We have developed an automated biopsy analysis system that achieves high performance in detecting CD on biopsy slides. Our system can highlight areas of interest and provide preliminary classification of duodenal biopsies before review by pathologists. This technology has great potential for improving the accuracy and efficiency of CD diagnosis.

Keywords: Celiac disease; deep learning; digital pathology; duodenal biopsy; whole-slide imaging.

Figure 1

Data flow diagram for allocating whole slides for training, development, and testing of our model. For training, patches were generated using the sliding window algorithm to train our residual network patch classifier. The development set was used to fine-tune hyperparameters and thresholds of our neural network. Finally, we evaluated our model on the test set of 212 whole-slide images with reference labels

Figure 2

Overview of detection of celiac disease on whole-slide biopsy images. We used a sliding window approach on a whole-slide image to generate patches, classified each patch with a residual network model, and used a heuristic on the aggregated patch predictions to classify the whole slide

Figure 3

Receiver operating characteristic curves and their area under the curve for our model's classifications on the independent test set of 212 whole-slide biopsy images

Figure 4

Visualization of patch predictions of our model at the whole-slide level (a-d) was correctly classified as normal, (e-h) was correctly classified as celiac disease, and (i-l) was correctly classified as nonspecific duodenitis

Figure 5

Class activation mapping heat maps highlighting the most informative regions of patches relevant to normal, celiac disease, and nonspecific duodenitis classes. Red regions indicate areas of attention for our residual neural network