Developing a Neural Network Model for a Non-invasive Prediction of Histologic Activity in Inflammatory Bowel Diseases - Popa, Iolanda Valentina; Diculescu, Mircea; Mihai, Catalina; Prelipcean, Cristina Cijevschi; Burlacu, Alexandru (Turkish Journal of Gastroenterology, 2021)
Background: Colonoscopy with biopsy is the “gold” standard for evaluating disease activity in inflammatory bowel diseases (IBD). Currentresearch is geared toward finding non-invasive, cost-efficient methods that estimate disease activity. We aimed to develop a neuralnetwork (NN) model for the non-invasive prediction of histologic activity in IBD using routinely available clinical-biological parameters.Methods: Standard clinical-biological parameters and histologic activity from 371 ulcerative colitis (UC) and 115 Crohn’s disease (CD)patient records were collected. A training set, a test set, and a validation set were used for building/validating 2 models for each disease.All models had binary output predicting the active/inactive histologic disease status. For both diseases, the first model used both clinicaland biological inputs, while the second used only biological data.Results: First UC model obtained an accuracy of 95.59% on the test set and 96.67% on the validation set. The second UC modelachieved accuracies of 88.24% and 86.67% on the test and validation sets, respectively. The First CD classifier resulted in 90.48%accuracy on the test set and 91.67% on the validation set. Finally, the second CD classifier obtained an accuracy of 85.71% on the testset and 91.67% on the validation set.Conclusions: An accurate and non-invasive artificial intelligence system to predict histologic disease activity in IBD is designed. Ourmodels achieved similar or better results compared to the documented performance of fecal calprotectin (the best non-invasive IBDbiomarker to date). Given these favorable results, we anticipate the future utility in the clinical setting of a non-invasive disease activityprediction.