Decoding surgical skill: an objective and efficient algorithm for surgical skill classification based on surgical gesture features -experimental studies.

BACKGROUND: Various surgical skills lead to differences in patient outcomes and identifying poorly skilled surgeons with constructive feedback contributes to surgical quality improvement. The aim of the study was to develop an algorithm for evaluating surgical skills in laparoscopic cholecystectomy based on the features of elementary functional surgical gestures (Surgestures). MATERIALS AND METHODS: Seventy-five laparoscopic cholecystectomy videos were collected from 33 surgeons in five hospitals. The phase of mobilization hepatocystic triangle and gallbladder dissection from the liver bed of each video were annotated with 14 Surgestures. The videos were grouped into competent and incompetent based on the quantiles of modified global operative assessment of laparoscopic skills (mGOALS). Surgeon-related information, clinical data, and intraoperative events were analyzed. Sixty-three Surgesture features were extracted to develop the surgical skill classification algorithm. The area under the receiver operating characteristic curve of the classification and the top features were evaluated. RESULTS: Correlation analysis revealed that most perioperative factors had no significant correlation with mGOALS scores. The incompetent group has a higher probability of cholecystic vascular injury compared to the competent group (30.8 vs 6.1%, P =0.004). The competent group demonstrated fewer inefficient Surgestures, lower shift frequency, and a larger dissection-exposure ratio of Surgestures during the procedure. The area under the receiver operating characteristic curve of the classification algorithm achieved 0.866. Different Surgesture features contributed variably to overall performance and specific skill items. CONCLUSION: The computer algorithm accurately classified surgeons with different skill levels using objective Surgesture features, adding insight into designing automatic laparoscopic surgical skill assessment tools with technical feedback.

Molten-salt-composite of Pyrite and Silver Nanoparticle as Electrocatalyst for Hydrogen Peroxide Sensing.

A conductive molten salt was synthesized by using natural pyrite (PR) and silver nanoparticles (Ag) at 450°C using a molten salt method. The molten-salt-composite (PR/Ag) was used as an electrocatalyst to detect hydrogen peroxide (H2O2). The as-prepared PR/Ag possessed higher conductivity than natural PR. It exhibited a high sensitivity of 603.54 µA mM-1 cm-2 for the detection of H2O2, with a linear range of 0.1 to 30 mM, and a detection limit of 0.02 mM (S/N = 3). In addition, the PR/Ag sensor exhibited good selectivity to H2O2, resisting interference from other potential interferent compounds (e.g. uric acid, glucose, fructose and common metal ions (K+, Mg2+, Na+)). The approach is considered to provide a sensitive, selective, and reliable tool for highly detection of H2O2.