Patient-Specific Colorectal Digital Twins for Continuous Monitoring and Personalized Management of Colorectal Diseases: A Biomedical Informatics Perspective

Résumé fourni par la source

Objective: Colorectal cancer, inflammatory bowel disease, and diverticular disease are progressive conditions that are commonly managed through episodic clinical encounters, laboratory testing, endoscopy, and patient-reported symptoms. This Perspective argues that colorectal digital twins can support a shift from intermittent assessment toward continuous, patient-specific colorectal monitoring and personalized care support. Key Points: We propose a colorectal digital twin framework that integrates longitudinal data from wearable sensors, smartphone-based stool and symptom assessment, home biomarker testing, electronic health records, laboratory results, and patient-reported outcomes. These data can support hybrid mechanistic and machine learning models for colorectal state estimation, risk prediction, and personalized decision support. Conclusion: Colorectal digital twins could enable earlier detection of clinically meaningful changes and more personalized disease management, but safe translation will require rigorous validation, uncertainty quantification, workflow integration, governance, and clinician oversight.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.