Real-time auxiliary method and device for double-cavity tracheal intubation and electronic equipment

Through the deep learning-based real-time recognition system of airway anatomy, the accuracy and safety issues caused by the traditional double-lumen endotracheal intubation's reliance on experience are solved, individualized anesthesia plan optimization and real-time intubation strategy generation are achieved, and the accuracy and safety of intubation are improved.

CN120613077APending Publication Date: 2025-09-09ZHONGSHAN HOSPITAL FUDAN UNIV
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Patent Information

Application Number
CN202510737872.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-04
Publication Date
2025-09-09

AI Technical Summary

Technical Problem

Traditional double-lumen endotracheal intubation decisions rely on the anesthesiologist's personal experience, making it difficult to accurately assess the difficulty of intubation and develop the optimal plan in complex cases and emergencies, leading to surgical delays and increased risk of perioperative complications.

Method used

A deep learning-based real-time airway anatomical structure recognition system was used to obtain video streams through a medical endoscope camera system. Key frames were extracted and screened using the dynamic threshold method. The EfficientNet-B1 model was used to identify key anatomical structures of the trachea and generate intubation strategies.

Benefits of technology

It achieves patient-specific anesthesia plan optimization and real-time monitoring, improves the accuracy and safety of intubation, and reduces surgical delays and the risk of complications.

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Abstract

The invention provides a real-time auxiliary method and device for double-cavity tracheal intubation and electronic equipment, and relates to the technical field of medical auxiliary decision making. The method comprises the steps that a tracheal intubation operation video stream is obtained in real time through a medical endoscope camera system; carrying out key frame extraction on the tracheal intubation operation video stream, and determining effective frames by adopting a dynamic threshold value method; and inputting the effective frame into a double-lumen tracheal intubation auxiliary identification model, outputting a double-lumen tracheal key anatomical structure in real time, and assisting in generating a double-lumen tracheal intubation strategy. According to the invention, individualized anesthesia scheme optimization can be carried out based on clinical data of a patient, key indexes such as a lumen position and ventilation parameters can be monitored in real time, and dynamic adjustment suggestions can be provided.
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