Real-time AI-based detection of excessive traction on the recurrent laryngeal nerve during robot-assisted minimally invasive esophagectomy: a proof-of-concept study

Abstract

Recurrent laryngeal nerve (RLN) palsy often occurs due to excessive traction (ET) on the nerve during esophagectomy. Use of a nerve integrity monitor (NIM) sometimes can prevent RLN palsy, but it does not detect injury before it occurs. The aim of this proof-of-concept study was to use an artificial intelligence (AI) system for pre-injury detection of ET on the left RLN during robot-assisted minimally invasive esophagectomy (RAMIE). We extended a previously developed AI model for anatomical recognition by labeling video frames from 130 RAMIE patients as ET or non-ET according to visual indicators of nerve tension. The system quantifies the probability of ET in real-time as an excessive traction risk (ETR), which is displayed on the surgical video. The AI correctly identified 84.4% of scenes involving unintended nerve traction in 10 surgical patients, and the ETR showed a correspondence with the actual degree of traction applied. Notably, in a representative patient, the AI detected ET earlier than the decrease in the NIM amplitude. By providing real-time proactive feedback, the system enables earlier recognition of hazardous traction, which may allow adjustment before irreversible nerve damage occurs.