A vision for a colorectal digital twin that enables proactive and personalized disease management
This work outlines a vision for improving disease management in colorectal conditions like cancer and IBD, but it is incremental as it presents a conceptual framework without deployment or validation.
The paper tackles the challenge of early detection and personalized management for colorectal diseases by proposing a conceptual framework for an integrated colorectal digital twin, which aims to enable non-invasive, continuous monitoring and proactive disease management through multimodal data and hybrid modeling.
Colorectal cancer, inflammatory bowel disease, and diverticular disease are progressive conditions that affect millions of individuals worldwide and impose substantial clinical and economic burdens. Early detection and personalized management are essential for slowing disease progression and improving patient outcomes. Current care pathways rely primarily on episodic clinical encounters, laboratory testing, and reactive interventions, limiting early detection and personalized longitudinal management. This paper introduces a conceptual framework for an integrated colorectal digital twin that supports non-invasive, continuous monitoring and personalized disease management. The framework integrates multimodal physiological and behavioral data streams, hybrid mechanistic-machine learning modeling of colorectal function, and a personalized artificial intelligence engine to support proactive disease management. Rather than presenting a deployed clinical system, this work outlines a clear vision and a structured approach for colorectal digital twins, identifying key technical, modeling, and translational challenges necessary for future implementation and validation.