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.
<i>Background and Aims:</i> The pre-operative determination of resectability of pancreatic and peri-ampullary neoplasia assists the selection of patients for surgical or non-surgical treatment. This study investigated whether the addition of laparoscopy with laparoscopic ultrasound to dual-phase helical CT could improve the accuracy of assessment of resectability. <i>Patients and Methods:</i> Prospective study of 305 patients referred to a single unit for consideration of pancreatic resection who underwent dual-phase helical CT scanning ± laparoscopy with laparoscopic ultrasound. Data were collected on patient demographics, CT findings, assessment of operability, laparoscopic assessment (LA), surgical procedures and histology. <i>Results:</i> LA was undertaken in 239/305 patients, 190 of whom were considered CT resectable, and 49 CT unresectable. Of the 190 CT resectable patients, LA correctly identified unresectability in 28 (15%: metastases in 15; vascular encasement in 6; anaesthesia for laparoscopy found 7 unfit for major resection) and incorrectly in 2 (vascular encasement), but did not identify unresectability in 33; LA correctly confirmed resectability in the remainder (prediction improved, χ<sup>2</sup> = 9.73, p < 0.01). Of the 49 CT unresectable patients, LA correctly identified resectability in 4, and incorrectly in 12, and correctly identified unresectability in the remaining 33. Sixty-six of the 305 patients did not undergo LA, of whom 23 underwent resection. <i>Conclusion:</i> When added to dual-phase helical CT, laparoscopy with laparoscopic ultrasound provides valuable information that significantly improves the selection of patients for surgical or non-surgical treatment.
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