2025/09/04 by Sen Yang, Surong Hua, Zhihong Wang +8 · 1 voice
Medicine · #Airway Management and Intubation Techniques #Thyroid and Parathyroid Surgery #Voice and Speech Disorders
paper · doi:10.1016/j.imed.2025.08.005
openalex publication_date 2025/09/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/04
Background Recurrent laryngeal nerve (RLN) injury is a notable complication in endoscopic thyroidectomy. Although convolutional neural networks (CNNs) have been extensively developed for medical applications, their use in surgery has predominantly focused on identifying static objects such as surgical tools and anatomical landmarks. This study introduces a CNN-based model for real-time RLN detection, aimed at improving surgical safety. Methods Video data from 2 endoscopic thyroidectomy techniques, the chest-breast approach (ETCB) and trans-axillary approach (ETTA), were retrospectively collected at Peking Union Medical College Hospital between February 2020 and August 2021. Eligible cases were selected using predefined inclusion and exclusion criteria. RLN-specific video segments were annotated by expert thyroid surgeons, and the dataset was randomly divided into training (83.3%) and test (16.7%) sets. A modified YOLOv3 algorithm with oriented bounding boxes was used for real-time RLN detection. Model performance was evaluated using recall and precision at 2 intersection over union (IoU) thresholds (0.1 and 0.5), and statistical analysis was performed using Python-based tools. Results A total of 92 videos (113,690 frames) were included, with the training set comprising 75,700 frames from 42 ETCB videos and 23,111 frames from 37 ETTA videos. The test set consisted of 9,912 ETCB and 4,967 ETTA frames. The model achieved recall rates of 80.6% for ETCB and 91.4% for ETTA, and precision rates of 78.4% and 82.6%, respectively, at an IoU threshold of 0.1. These metrics underscore the model’s robustness in detecting RLN across diverse operative settings. Conclusion The developed CNN model reliably detected RLN in real-time during endoscopic thyroidectomy procedures, demonstrating the potential of artificial intelligence to dynamically recognize complex anatomical structures in varied surgical contexts.