体外循环系统的血气监测方法及系统
By actively adjusting oxygenator parameters using a blood gas prediction model and a deep learning framework, the lag problem in blood gas monitoring in extracorporeal circulation systems has been solved, enabling real-time and accurate blood gas monitoring and treatment. This reduces the risk of tissue and organ damage and improves treatment efficiency and accuracy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- THE SECOND AFFILIATED HOSPITAL ARMY MEDICAL UNIV
- Filing Date
- 2025-05-30
- Publication Date
- 2026-07-17
AI Technical Summary
Existing extracorporeal circulation system blood gas monitoring lacks an effective early warning mechanism, resulting in delayed alarms, which increases the risk of irreversible damage to tissues and organs. Furthermore, prediction methods that rely on doctors' experience are inefficient and have a high risk of misjudgment.
The system employs a blood gas prediction model for automatic data analysis and trend prediction, actively adjusts oxygenator parameters, and combines a deep learning framework and recurrent neural network to capture the time-series characteristics of blood gas data, screen key blood gas features, dynamically adjust oxygenation parameter strategies, and promptly alert doctors for intervention.
It enables real-time and accurate monitoring and proactive intervention of blood gas parameters, reducing labor costs, improving treatment efficiency and accuracy, reducing the risk of misdiagnosis, and ensuring patient safety.
Smart Images
Figure CN120459421B_ABST