CrohnTwin-X: An explainable digital twin framework for predicting postoperative recurrence in Crohn's disease using...

Crohn's disease (CD) is a chronic inflammatory disorder with a high rate of postoperative recurrence, posing major challenges for long-term management. Conventional clinical indices have limited capacity for individualized prognostic prediction. While Digital Twin (DT) technology-virtual replicas of individual patients-offers a transformative paradigm for clinical decision support, its application in CD postoperative management remains largely unexplored.

We developed CrohnTwin-X, an explainable digital twin framework that integrates transcriptomic and clinical features to predict postoperative recurrence in CD. Transcriptome and clinical data from GSE186582 (n = 464) and GSE102133 (n = 33) were obtained from GEO. After normalization and batch correction, KEGG pathway activation was quantified using gene set variation analysis and used for functional pathway-driven digital phenotyping to cluster patients into distinct digital-twin subtypes. Differentially expressed genes across subtypes were treated as key feature encoders and modeled using least absolute shrinkage and selection operator and multivariable logistic regression to derive a 17-gene biological fingerprint and twin inference engine. A clinician-digital twin interface (nomogram) was constructed by combining the gene-based risk score with clinical variables. Model performance was assessed by receiver operating characteristic curves, area under the curve (AUC), calibration, and decision curve analysis.

CrohnTwin-X stratified patients into three pathway-defined digital phenotypes with distinct immune-metabolic features and postoperative outcomes. The 17-gene fingerprint showed robust performance with AUC values of 0.876 (95% CI: 0.830-0.921), 0.714 (95% CI: 0.640-0.788), 0.789 (95% CI: 0.743-0.835), and 0.800 (95% CI: 0.650-0.950) in the training, testing, total, and external validation sets, respectively.

CrohnTwin-X is a novel, explainable digital twin framework for predicting postoperative recurrence in CD by integrating multi-omics and clinical features, bridging gastroenterology, data science, and biomedical engineering, and supporting personalized postoperative surveillance and treatment decision-making.