Prediction of Celiac Disease Severity and Associated Endocrine Morbidities through Deep Learning-based Image Analytics

Objective Develop a deep learning-based methodology using the foundations of systems pathology to generate highly accurate predictive tools for complex gastrointestinal diseases, using celiac disease (CD) as a prototype. Design To predict the severity of CD, defined by Marsh–Oberhüber classification, we used deep learning to develop a model based on histopathologic features. Results The study was based on a pediatric cohort of 124 patients identified with different classes of CD severity. The model predicted CD with an overall 88.7% accuracy with the highest for Marsh IIIc (91.0%; 95% sensitivity; 91% specificity). The model identified EECs as a defining feature of children with Marsh IIIc CD and endocrinopathies which was confirmed using immunohistochemistry. Conclusion This deep learning image analysis platform has broad applications in disease treatment, management, and prognostication and paves the way for precision medicine. Summary What is already known about this subject? – Deep Learning has the potential to generate predictive models for complex gastrointestinal diseases. What are the new findings? – Our deep learning-based model used the foundations of systems pathology to generate a highly accurate predictive tool for complex gastrointestinal diseases, using a celiac disease (CD) pediatric cohort as a prototype. – The model predicated CD severity with high accuracy and identified enteroendocrine cells as a defining feature of children with severe CD and endocrinopathies. How might it impact on clinical practice in the foreseeable future? – Assessment of histopathological markers at the time of diagnosis that can predict risk of severity or complications can have broad applications in disease treatment, management, and prognostication and pave the way for precision medicine.