Artificial intelligence-based difficult airway evaluation method and device

Abstract

The invention relates to a difficult airway evaluation method and device based on artificial intelligence. The method comprises the following steps: acquiring facial images of various postures; constructing a feature extraction network based on facial recognition, and extracting feature information of the facial image through the trained feature extraction network; and constructing a difficult airway classifier based on a machine learning algorithm, and performing difficult airway severity scoring on the extracted feature information of the facial image through the trained difficult airway classifier to obtain an evaluation result of the difficult airway. According to the invention, early warning can be accurately carried out on difficult airways in clinical anesthesia.

Description

technical field [0001] The invention relates to the field of computer-aided technology, in particular to an artificial intelligence-based difficult airway assessment method and device. Background technique [0002] Endotracheal intubation is an important means for anesthesiologists to manage the airway of patients under general anesthesia. It plays an important role in maintaining airway patency, ventilation and oxygen supply, respiratory support, and maintenance of oxygenation. However, despite great advances and improvements in endotracheal intubation techniques and equipment, the incidence of perioperative complications and disability due to difficult airways has not been significantly improved, especially for unanticipated difficulties airway. At present, methods for evaluating difficult airways generally include Mallampatti grade, LEMON score, Wilson score, neck radiotherapy history, and auxiliary CT, MRI, US, etc. The process is complicated and the positive evaluation...

AI Technical Summary

Problems solved by technology

However, despite great advances and improvements in endotracheal intubation techniques and equipment, the incidence of perioperative complications and disability due to difficult airways has not been significantly improved, especially for unanticipated difficulties airway

Currently, methods for evaluating difficult airways generally include Mallampatti grade, LEMON score, Wilson score, neck radiotherapy history, and auxiliary CT, MRI, US, etc. The process is complicated and the positive evaluation value is not high, all of which have certain limitations

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