Artificial intelligence-based analytics for diagnosis of small bowel enteropathies and black box feature detection

Document Type



Paediatrics and Child Health


Objectives: Striking histopathological overlap between distinct but related conditions poses a disease diagnostic challenge. There is a major clinical need to develop computational methods enabling clinicians to translate heterogeneous biomedical images into accurate and quantitative diagnostics. This need is particularly salient with small bowel enteropathies; Environmental Enteropathy (EE) and Celiac Disease (CD). We built upon our preliminary analysis by developing an artificial intelligence (AI)-based image analysis platform utilizing deep learning convolutional neural networks (CNNs) for these enteropathies.
Methods: Data for secondary analysis was obtained from three primary studies at different sites. The image analysis platform for EE and CD was developed using convolutional neural networks (CNNs) including one with multi-zoom architecture. Gradient-weighted Class Activation Mappings (Grad-CAMs) were used to visualize the models' decision making process for classifying each disease. A team of medical experts simultaneously reviewed the stain color normalized images done for bias reduction and Grad-CAMs to confirm structural preservation and biomedical relevance, respectively.
Results: 461 high-resolution biopsy images from 150 children were acquired. Median age (interquartile range) was 37·5 (19·0 to 121·5) months with a roughly equal sex distribution; 77 males (51·3%). ResNet50 and Shallow CNN demonstrated 98% and 96% case-detection accuracy, respectively, which increased to 98·3% with an ensemble. Grad-CAMs demonstrated models' ability to learn different microscopic morphological features for EE, CD, and controls.
Conclusion: Our AI-based image analysis platform demonstrated high classification accuracy for small bowel enteropathies which was capable of identifying biologically relevant microscopic features and emulating human pathologist decision making process. Grad-CAMs illuminated the otherwise 'black box' of deep learning in medicine, allowing for increased physician confidence in adopting these new technologies in clinical practice.


Volume, issue, and pagination are not provided by the author/publisher


Journal of Pediatric Gastroenterology and Nutrition