Background: Endoscopic thyroidectomy by anterior chest approach is an effective procedure with both excellent aesthetic benefits and reduced physical injury, avoiding neck scarring—a particularly attractive feature for appearance-conscious patients. Research indicates high patient satisfaction with cosmetic outcomes exceeding 90%, making it especially suitable for patients over 30 years old.1 Although its cosmetic results may be slightly inferior to axillary approaches, they remain superior to open surgery. Recurrent laryngeal nerve (RLN) injury represents a major surgical risk in thyroid procedures, potentially causing hoarseness or dysphagia. Studies show that transient RLN paralysis rates in transareolar anterior chest approach surgeries range from 0% to 5%, comparable to the 1%–2% permanent injury rate observed in open surgeries.1 The application of intraoperative neuromonitoring significantly enhances RLN identification and protection.2 Artificial intelligence applications in thyroid surgery are evolving rapidly, particularly through deep learning algorithms for RLN identification, which may further mitigate injury risks. While no direct studies have focused on AI applications in transareolar anterior chest approaches, existing AI models have demonstrated promising accuracy rates (86%–93%) in RLN recognition across various surgical modalities.3
Wang et al. (Thu,) studied this question.