A vision for a colorectal digital twin that enables proactive and personalized disease management
🤖 AI Summary
Current management of colorectal diseases relies heavily on episodic clinical visits and reactive interventions, which are insufficient for early warning and personalized long-term care. This work proposes, for the first time, a digital twin framework for colorectal health that integrates multimodal physiological and behavioral data with mechanistic models and machine learning to create a non-invasive, continuous monitoring and proactive intervention system powered by a personalized AI engine. The study systematically outlines the key technical pathways and clinical translation challenges associated with this approach, establishing a theoretical foundation for shifting colorectal health management from a passive treatment paradigm toward active prevention.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
colorectal digital twin
personalized disease management
early detection
continuous monitoring
progressive colorectal diseases
Innovation
Methods, ideas, or system contributions that make the work stand out.
digital twin
personalized AI
hybrid modeling
continuous monitoring
colorectal disease
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