Exploration and Application of Intelligent Difficult Airway Assessment Scheme

The research background is based on a critical issue in anesthetic practice - the management of difficult airways. A difficult airway refers to the challenges encountered during mask ventilation or endotracheal intubation, which can lead to severe complications or even death. The American Society of Anesthesiologists (ASA) defines a difficult airway as one that presents difficulties in mask ventilation, laryngoscopy, use of supraglottic airway devices, endotracheal intubation, extubation, or invasive airway access, including but not limited to situations that are anticipated or unanticipated. Studies have shown that the incidence of difficult and unventilatable mask airways in the general subject population is 2.2% and 0.15%, respectively, while the rate of difficult or failed intubation in general anesthesia events is between 0.43% and 0.52%. These data highlight the importance of accurate airway assessment before anesthesia.

In China, due to the large population, there is a significant demand for medical and health resources, especially in the fields of surgery and anesthesia. The diversity of China's population, including regional, ethnic, and genetic background differences, may affect airway structure, thereby influencing airway management strategies. Therefore, conducting research on the assessment and management of difficult airways in China has important clinical and social significance.

Preoperative airway assessment is a key step in preventing complications associated with difficult airways. Currently, the modified Mallampati classification and the Cormack-Lehane grading are commonly used assessment tools in clinical practice. The modified Mallampati classification assesses the difficulty of the airway by observing the visibility of structures within the oral cavity, while the Cormack-Lehane grading assesses it through the visibility of laryngeal structures during direct laryngoscopy. Although these methods are widely used in clinical practice, they have issues with subjectivity, poor repeatability, and limited accuracy.

With the advancement of medical technology, especially the rapid development of artificial intelligence (AI) and telemedicine, new opportunities have emerged to improve the assessment methods for difficult airways. The use of intelligent devices and algorithms can provide more precise measurements and analysis of the subject's airway anatomy, thereby improving the accuracy and reliability of the assessment. This new method not only reduces the interference of subjectivity but also improves the repeatability of the assessment through standardized image collection and analysis processes.

This study proposes an intelligent airway assessment system that combines phonation modulation and tongue position adjustment, aiming to improve the accuracy and reliability of assessments. The system uses deep learning algorithms to analyze oral images of subjects to predict airway difficulty. The study will also explore the correlation of this system with traditional assessment methods and establish a predictive model for difficult airways.