| Users Online: 160
Month wise articles
Figures next to the month indicate the number of articles in that month
» Articles published in the past year
To view other articles click corresponding year from the navigation links on the left side.
Export selected to
Show all abstracts
Show selected abstracts
Export selected to
Add to my list
Automated detection of celiac disease on duodenal biopsy slides: A deep learning approach
Jason W Wei, Jerry W Wei, Christopher R Jackson, Bing Ren, Arief A Suriawinata, Saeed Hassanpour
J Pathol Inform
2019, 10:7 (8 March 2019)
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.
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.
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.
[HTML Full text]
[Mobile Full text]
[Sword Plugin for Repository]
© Journal of Pathology Informatics | Published by Wolters Kluwer -
Online since 10