Objective surgical skill assessment based on automatic recognition of dissection and exposure times in robot-assisted radical prostatectomy.

Abstract

Purpose: Assessing surgical skills is vital for training surgeons, but creating objective, automated evaluation systems is challenging, especially in robotic surgery. Surgical procedures generally involve dissection and exposure (D/E), and their duration and proportion can be used for skill assessment. This study aimed to develop an AI model to acquire D/E parameters in robot-assisted radical prostatectomy (RARP) and verify if these parameters could distinguish between novice and expert surgeons.

Methods: This retrospective study used 209 RARP videos from 18 Japanese institutions. Dissection time was defined as the duration of forceps energy activation, and exposure time as the combined duration of manipulating the third arm and camera. To measure these times, an AI-based interface recognition model was developed to automatically extract instrument status from the da Vinci Surgical System^® UI. We compared novices and experts by measuring dissection and exposure times from the model's output.

Results: The overall accuracies of the UI recognition model for recognizing the forceps type, energy activation status, and camera usage status were 0.991, 0.998, and 0.991, respectively. Dissection time was 45.2 vs. 35.1 s (novice vs. expert, p = 0.374), exposure time was 195.7 vs. 89.7 s (novice vs. expert, p < 0.001), and the D/E ratio was 0.174 vs. 0.315 (novice vs. expert, p = 0.003).

Conclusions: We successfully developed a model to automatically acquire dissection and exposure parameters for RARP. Exposure time may serve as an objective parameter to distinguish between novices and experts in RARP, and automated technical evaluation in RARP is feasible.

Trial registration number and date: This study was approved by the Institutional Review Board of the National Cancer Center Hospital East (No.2020 - 329) on January 28, 2021.