Journal of Pathology Informatics Journal of Pathology Informatics
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Year : 2019  |  Volume : 10  |  Issue : 1  |  Page : 7

Automated detection of celiac disease on duodenal biopsy slides: A deep learning approach

1 Department of Biomedical Data Science; Department of Computer Science, Dartmouth College, Hanover, New Hampshire, USA
2 Department of Biomedical Data Science, Dartmouth College, Hanover, New Hampshire, USA
3 Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
4 Department of Biomedical Data Science; Department of Computer Science, Dartmouth College, Hanover, New Hampshire; Department of Epidemiology, Dartmouth College, Hanover, New Hampshire, USA

Correspondence Address:
Dr. Saeed Hassanpour
1 Medical Center Drive, HB 7261, Lebanon, New Hampshire 03756

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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jpi.jpi_87_18

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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.

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