Operative difficulty in laparoscopic cholecystectomy: considering the role of machine learning platforms in clinical practice

Aim: Computer vision is a subset of machine learning (ML) technology that allows automated analysis of large operative video datasets. The aim of this study was to use a commercially available ML-driven platform to evaluate a subjective grading of operative difficulty in laparoscopic cholecystectomy (LC). Methods: Patients undergoing LC prospectively consented, and their operations were recorded. The intra-operative findings were prospectively graded (1-4) based on intraoperative gallbladder appearance assessments. Deidentified videos were uploaded to Touch SurgeryTMand run through the platform’s algorithm, providing automated analytics including the total operative length and operative phase length. The rate of critical view of safety (CVS) achievement was also included in the analysis. Results: 206 LC were included. 27 LC were excluded due to incomplete video recording and were therefore not amenable to the final data analysis. Grade 1 and 2 patients had significantly shorter operative time than grade 3 and 4 patients [17min and 53s (IQR 15min and 24s- 21min and 38s) vs. 25 min and 49s (IQR 20min and 12s-38min and 38s) (P < 0.010)]. The operative phases for each step were significantly longer in patients with gallbladders graded 3 or 4 compared to those patients graded 1 or 2 (P < 0.043). The CVS was achieved in 94% of grade 1 patients, 88% of grade 2 patients, 85% of grade 3 patients and 73% of grade 4 patients (P = 0.177). Conclusion: Increased operative time and decreased ability to achieve the CVS with more difficult intraoperative findings supports the utility of the proposed grading system. ML in surgery is a nascent field, but this study demonstrates the potential of commercially available platforms for use in operative analytics, documentation, audit and training of future surgeons.