Journal of Pathology Informatics Journal of Pathology Informatics
Contact us | Home | Login   |  Users Online: 679  Print this pageEmail this pageSmall font sizeDefault font sizeIncrease font size 


ORIGINAL RESEARCH
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

Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpi.jpi_87_18

Rights and Permissions

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.


[FULL TEXT] [PDF]*
Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed1184    
    Printed23    
    Emailed0    
    PDF Downloaded220    
    Comments [Add]    

Recommend this journal