Intraoperative neural monitoring (IONM) combines electrophysiology with anatomy to monitor nerve function during thyroid surgery,and has become an important auxiliary technology for neuroprotection. After more than ten years of development,the technology has been widely applied and popularized in China,promoting the development of a number of new technologies in the industry. Combined with the questionnaire survey data of the Chinese Neural Monitoring Study Group,this paper aims to summarize the clinical effect of IONM technology, the application status and existing problems at home and abroad, and propose the possible future development direction.

Intraoperative neural monitoring (IONM) is a critical technique for the protection of recurrent laryngeal nerve (RLN) function during thyroid surgery. In recent years, continuous innovations in IONM have established it as a cornerstone of modern thyroid surgery. However, current methodologies still exhibit limitations in the accurate assessment of neural function, and guidelines have yet to provide clear strategies for managing patients with intraoperative signal loss who subsequently develop postoperative voice disorders. This article reviews representative advances in the application of deep learning to bioelectrophysiological signals and systematically summarizes the use of deep learning in the field of voice medicine, thereby exploring the feasibility of integrating IONM with deep learning technologies.

Intraoperative neural monitoring (IONM) is a critical technique for the protection of recurrent laryngeal nerve (RLN) function during thyroid surgery. In recent years, continuous innovations in IONM have established it as a cornerstone of modern thyroid surgery. However, current methodologies still exhibit limitations in the accurate assessment of neural function, and guidelines have yet to provide clear strategies for managing patients with intraoperative signal loss who subsequently develop postoperative voice disorders. This article reviews representative advances in the application of deep learning to bioelectrophysiological signals and systematically summarizes the use of deep learning in the field of voice medicine, thereby exploring the feasibility of integrating IONM with deep learning technologies.

Intraoperative neural monitoring (IONM) combines electrophysiology with anatomy to monitor nerve function during thyroid surgery,and has become an important auxiliary technology for neuroprotection. After more than ten years of development,the technology has been widely applied and popularized in China,promoting the development of a number of new technologies in the industry. Combined with the questionnaire survey data of the Chinese Neural Monitoring Study Group,this paper aims to summarize the clinical effect of IONM technology, the application status and existing problems at home and abroad, and propose the possible future development direction.

With the popularization of intraoperative nerve monitoring (IONM) , it is possible to determine the intraoperative nerve function, which provides evidence-based basis for surgical decision making. Intraoperative loss of nerve signal (LOS) often indicates postoperative vocal cord dyskinesia. Once LOS occurs intraoperatively, the next surgical strategy adopted by the operator has always been controversial among Chinese and western experts. Therefore, this paper makes a comparative analysis of the differences between the viewpoints of domestic experts and western scholars and the possible causes through the investigation of domestic and foreign literature to provide theoretical basis for better thyroid surgery decision.