A vision for a colorectal digital twin that enables proactive and personalized disease management

TL;DR

This paper proposes a conceptual framework for a colorectal digital twin that integrates multimodal data and AI to enable proactive, personalized disease management, aiming to improve early detection and patient outcomes.

Contribution

It introduces a structured vision and approach for developing colorectal digital twins, highlighting key technical and modeling challenges for future implementation.

Findings

Framework integrates physiological and behavioral data streams

Hybrid modeling combines mechanistic and machine learning approaches

Supports proactive, personalized disease management

Abstract

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.