Assessing operative variability in robot-assisted radical prostatectomy (RARP) through AI

Area of Science:

• Urology
• Surgical Technology
• Artificial Intelligence in Medicine

Background:

• Robotic-assisted radical prostatectomy (RARP) is a prevalent urological procedure.
• Assessing surgical practices and their link to patient outcomes is crucial for improving care quality.
• Artificial intelligence (AI) offers a novel method for analyzing surgical videos.

Purpose of the Study:

• To utilize a surgical intelligence platform to assess key surgical practices during RARP.
• To analyze the variability of these practices across different institutions.
• To explore the potential of surgical intelligence in improving clinical outcomes.

Main Methods:

• Analysis of 883 RARP cases from six institutions between 2017 and 2023.
• Employment of a surgical intelligence platform to identify six specific surgical practices.
• Descriptive statistical analysis of surgical practice adoption rates and surgical duration.

Main Results:

• Significant variability in the adoption rates of key surgical practices was observed across institutions.
• For example, bladder neck suture adoption ranged from 0% to 50%, and Rocco stitch from 0% to 75%.
• Leak tests and delayed DVC ligation showed high adoption rates (92.9% and 87.3% median, respectively).

Conclusions:

• Surgical intelligence effectively measures practice variability in RARP across institutions.
• Objective metrics from surgical intelligence can inform best practices, potentially mitigating adverse effects.
• This technology can enhance the quality of care and improve clinical outcomes in RARP.