Khandekar Archan, Porto Joao G, Daher Jean C, Freitas Pedro F S, Asselman Dotan, Suarez Maritza M, Gonzalgo Mark L, Parekh Dipen J, Punnen Sanoj
Desai Sethi Urology Institute, Miller School of Medicine University of Miami Miami Florida USA.
Theator Inc. Palo Alto California USA.
BJUI Compass. 2024 Oct 26;5(12):1263-1268. doi: 10.1002/bco2.452. eCollection 2024 Dec.
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.
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 -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.
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 ( < 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.
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.
本研究的目的是比较手术人工智能（AI）软件得出的热缺血时间（WIT）与部分肾切除术过程中外科医生手术报告中记录的热缺血时间的准确性，并评估该技术在评估术后肾功能方面的潜力。
使用手术AI软件（Theator公司，加利福尼亚州帕洛阿尔托）捕获并分析2023年10月至2024年4月期间进行的部分肾切除术的视频。该平台利用计算机视觉算法检测夹子的放置和移除，从而实现精确的WIT测量。经过专家审核的手术视频作为基准事实。使用配对样本检验将平台得出的WIT与外科医生手术报告中的WIT进行比较。此外，我们分析了平台得出的WIT与通过健康级别7（HL7）消息协议集成的电子健康记录（EHR）中提取的术后肌酐水平之间的相关性。
在64例符合条件的病例中，61例纳入最终分析。在所有手术中，平台得出的WIT与基准事实相差在1分钟以内，97%的相差在30秒以内，超过80%的相差在10秒以内。平台得出的WIT与基准事实之间的平均差异为8.3秒，显著低于手术报告中的2.45分钟差异（<0.001）。未发现平台得出的WIT与术后肌酐变化之间存在显著相关性，这与WIT可能无法独立决定术后肾功能的观点一致。虽然这不是本研究的主要目标，但观察到WIT、肿瘤大小和RENAL评分之间存在显著相关性。
本研究证明了手术智能平台在部分肾切除术中测量WIT的高度准确性。这些发现支持使用基于AI的手术时间测量进行精确的术中记录，并突出了将这些数据与EHR集成以推进手术结果研究的潜力。
