Deep Learning for Predicting Pediatric Crohn's Disease Using Histopathological Imaging
Anahita H. Sharma, Burke W. Lawlor, Jason Y. Wang, Yash Sharma, Saurav Sengupta, Philip Fernandes, Fatima Zulqarnain, Eve May, Sana Syed, Donald E. Brown · 2022
The current gold standard for Crohn's disease diagnosis involves the examination of biopsied tissue by a trained physician. However, endoscopic images and histological features are only evident when the appropriate biopsy site is chosen and the image is of high quality [1]. Thus, to prevent delayed diagnoses or reclassifications over time, additional tools to reinforce pathologists' diagnoses are necessary. Recent studies have showcased successful applications of deep learning for developing whole-slide classifications of digital histology images. In this study, we developed a patch-level image classification model for prediction of Crohn's disease using a convolutional neural network. This study obtained data from two different hospitals: INOVA and Cincinnati Children's Hospital Medical Center (CCHMC). When trained and validated on the same data set, our INOVA and CCHMC models achieved validation accuracies of 84.6 % and 93.9 %, respectively. However, the models performed poorly when trained on data from one site and tested on data from the other site. To investigate this issue, we built an additional patch-level model that was able to predict hospital source of the biopsy with 99 % accuracy. These results suggest the presence of site-specific artifacts which are detectable by machine learning models. We reduced the effects of these artifacts using color-normalization, image cropping, and other transformations, lowering site-predictive accuracy to 74%. Therefore, we suggest further works investigate reasons for inter-site biopsy differences such that site-generalizable, histopathological deep learning models can be developed.