Accuracy of warm ischemia time measurement using a surgical intelligence software in partial nephrectomies: A validation study

Abstract

Objectives

The objectives of this study are to compare the accuracy of warm ischemia times (WITs) derived by a surgical artificial intelligence (AI) software to those documented in surgeon operative reports during partial nephrectomy procedures and to assess the potential of this technology in evaluating postoperative renal function.

Patients and methods

A surgical AI software (Theator Inc., Palo Alto, CA) was used to capture and analyse videos of partial nephrectomies performed between October 2023 and April 2024. The platform utilized computer vision algorithms to detect clamp placement and removal, enabling precise WIT measurement. Expert-reviewed surgical videos served as the ground truth. Platform-derived WITs were compared to those in surgeon operative reports using paired-sample t-tests. Additionally, we analysed the correlation between platform-derived WITs and postoperative creatinine levels extracted from electronic health records (EHRs) integrated via health level seven (HL7) messaging protocols.

Results

Of 64 eligible cases, 61 were included in the final analysis. Platform-derived WITs were within 1 min of the ground truth in all procedures, within 30 s in 97%, and within 10 s in over 80%. The mean difference between platform-derived WITs and ground truth was 8.3 s, significantly lower than the 2.45 min difference for operative reports (p < 0.001). No significant correlation was found between platform-derived WIT and postoperative creatinine changes, aligning with the view that WIT may not independently determine postoperative renal function. Although not the primary goal of this study, significant correlations were observed between WIT, tumour size and RENAL score.

Conclusion

This study demonstrates the high accuracy of a surgical intelligence platform in measuring WIT during partial nephrectomies. The findings support the use of AI-based surgical time measurement for precise intraoperative documentation and highlight the potential of integrating these data with EHRs to advance research on surgical outcomes.