Development of a multimodal machine-learning fusion model to non-invasively assess ileal Crohn's disease endoscopic activity. (December 2022)

• Record Type: Journal Article
• Authors: Guez, Itai; Focht, Gili; Greer, Mary-Louise C.; Cytter-Kuint, Ruth; Pratt, Li-Tal; Castro, Denise A.; Turner, Dan; Griffiths, Anne M.; Freiman, Moti
• Is Part Of: Computer methods and programs in biomedicine. Volume 227(2022)
• Publication Date: 2022-12
• Subjects: Machine-learning -- Multimodal Learning in Medical Imaging and Informatics -- Crohn's disease -- Magnetic Resonance Enterography; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107207
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Multimodal Machine-Learning model for non-invasive assessment of ileal Crohn's disease endoscopic activity. Improved accuracy of non-invasive assessment of ileal Crohn's disease endoscopic activity compared to current approaches. Optimized set of radiological and biochemical features for machine-learning-based ileal Crohn's disease endoscopic activity assessment. Abstract: Background and Objective: Recurrent attentive non-invasive observation of intestinal inflammation is essential for the proper management of Crohn's disease (CD). The goal of this study was to develop and evaluate a multi-modal machine-learning (ML) model to assess ileal CD endoscopic activity by integrating information from Magnetic Resonance Enterography (MRE) and biochemical biomarkers. Methods: We obtained MRE, biochemical and ileocolonoscopy data from the multi-center ImageKids study database. We developed an optimized multimodal fusion ML model to non-invasively assess terminal ileum (TI) endoscopic disease activity in CD from MRE data. We determined the most informative features for model development using a permutation feature importance technique. We assessed model performance in comparison to the clinically recommended linear-regression MRE model in an experimental setup that consisted of stratified 2-fold validation, repeated 50 times, with the ileocolonoscopy-based Simple Endoscopic Score for CD at the TI (TI SES-CD) as a reference. We used the predictions' mean-squared-error (MSE) and the receiver operation characteristics (ROC) area under curve (AUC) for active disease classification (TI SEC-CD ≥ 3) as performance metrics. Results: 121 subjects out of the 240 subjects in the ImageKids study cohort had all required information (Non-active CD: 62 [51%], active CD: 59 [49%]). Length of disease segment and normalized biochemical biomarkers were the most informative features. The optimized fusion model performed better than the clinically recommended model determined by both a better median test MSE distribution (7.73 vs. 8.8, Wilcoxon test, p < 1e-5) and a better aggregated AUC over the folds (0.84 vs. 0.8, DeLong's test, p < 1e-9). Conclusions: Optimized ML models for ileal CD endoscopic activity assessment have the potential to enable accurate and non-invasive attentive observation of intestinal inflammation in CD patients. The presented model is available at https://tcml-bme.github.io/ML_SESCD.html.