Tracheostomy tube change risk assessment algorithm

By constructing personalized digital twins and augmented reality navigation, combined with multi-source heterogeneous data and fluid-structure interaction simulation, the optimal surgical strategy is generated, solving the dynamic and open-loop problem of risk assessment during tracheostomy cannula replacement, and improving the safety and accuracy of tracheostomy cannula replacement.

CN122290987APending Publication Date: 2026-06-26THE FIRST AFFILIATED HOSPITAL ZHEJIANG UNIV COLLEGE OF MEDICINE
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
THE FIRST AFFILIATED HOSPITAL ZHEJIANG UNIV COLLEGE OF MEDICINE
Filing Date
2026-02-13
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing technologies lack objective and quantitative decision support during tracheostomy tube replacement, cannot simulate the biomechanical interaction risks during dynamic operations, and have an open-loop decision-making process that cannot integrate multi-source heterogeneous data, resulting in insufficient operational safety and accuracy.

Method used

A personalized digital twin is constructed, and the optimal surgical strategy is generated through multi-source heterogeneous data fusion, bidirectional fluid-structure interaction simulation, and hierarchical reinforcement learning. Real-time risk control is achieved through augmented reality navigation, forming a closed-loop system.

Benefits of technology

It enables dynamic risk prediction and real-time visualization guidance during tracheostomy cannula replacement, significantly reducing reliance on operator experience, improving operational safety and accuracy, and optimizing system performance through federated learning.

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Abstract

This invention relates to the fields of intelligent surgery and precision medicine, specifically a risk assessment method for tracheostomy cannula replacement. The method mainly includes: constructing a personalized digital twin integrating the patient's individual anatomy and respiratory mechanics; using bidirectional coupled simulation of computational fluid dynamics and solid mechanics to pre-simulate the entire surgical process in a virtual environment and quantify dynamic risk fields such as tissue damage and ventilation obstruction; based on a hierarchical reinforcement learning agent, autonomously learning and generating optimal surgical paths and strategies in the simulation environment; using augmented reality technology to overlay the pre-simulated risk heatmap and planned path onto the surgical field in real time, and achieving intraoperative adaptive adjustment through a pre-simulated strategy database; and finally, using a federated learning framework to continuously optimize system performance using multi-center desensitized surgical data. This invention significantly improves the predictability, safety, and accuracy of this high-risk procedure, realizing a paradigm shift from static assessment to dynamic navigation.
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