Intelligent monitoring system for quality of life of colostomy patients based on deep learning and AR.

The clinical challenges in monitoring high-incidence complications in patients with colostomy after colorectal cancer surgery have led to the development of an intelligent monitoring system based on deep learning and augmented reality technology in this study. Traditional care relies on subjective scales for assessment, which has issues such as insufficient sensitivity and delayed response. The existing technical systems also have significant limitations in perception dimensions, environmental adaptability, and decision-making timeliness. Therefore, this study proposes a triple-technology integration solution: by fusing impedance sensing, pH-responsive hydrogels, and 3D depth cameras in a heterogeneous manner, a multi-modal perception network is established to achieve the collaborative collection of physiological and biochemical parameters and morphological features; a cross-modal spatio-temporal fusion network is designed, using dynamic attention mechanisms and differential manifold optimization algorithms to solve the feature coupling problem caused by the heterogeneity of multi-source data; an augmented reality visualization system is developed, combining spatial projection optimization and programmable haptic feedback to build a "visual-tactile-spatial" multi-dimensional human-machine interaction channel. The system was verified through multi-center randomized controlled trials, confirming its significant improvement in the early warning performance of complications, enhancement of the standardization of nursing operations, and reduction of medical resource consumption. The core innovation lies in the first introduction of the federated learning framework into the field of colostomy care, combined with neural architecture search for lightweight model deployment, and the quantification of health economic benefits through Markov models. This research marks a paradigm shift in postoperative care from experience-driven to data-driven, providing an extensible technical model for the intelligent monitoring of chronic diseases. In the future, it is necessary to expand the cross-disease transfer learning architecture and develop flexible and degradable sensors to enhance the accessibility of primary medical care.