Khalid Muhammad Uzair, Laplante Simon, Masino Caterina, Alseidi Adnan, Jayaraman Shiva, Zhang Haochi, Mashouri Pouria, Protserov Sergey, Hunter Jaryd, Brudno Michael, Madani Amin
Temerty Faculty of Medicine, University of Toronto, Medical Sciences Building, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada.
Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada.
Surg Endosc. 2023 Dec;37(12):9467-9475. doi: 10.1007/s00464-023-10403-4. Epub 2023 Sep 11.
Bile duct injuries (BDIs) are a significant source of morbidity among patients undergoing laparoscopic cholecystectomy (LC). GoNoGoNet is an artificial intelligence (AI) algorithm that has been developed and validated to identify safe ("Go") and dangerous ("No-Go") zones of dissection during LC, with the potential to prevent BDIs through real-time intraoperative decision-support. This study evaluates GoNoGoNet's ability to predict Go/No-Go zones during LCs with BDIs.
Eleven LC videos with BDI (BDI group) were annotated by GoNoGoNet. All tool-tissue interactions, including the one that caused the BDI, were characterized in relation to the algorithm's predicted location of Go/No-Go zones. These were compared to another 11 LC videos with cholecystitis (control group) deemed to represent "safe cholecystectomy" by experts. The probability threshold of GoNoGoNet annotations were then modulated to determine its relationship to Go/No-Go predictions. Data is shown as % difference [99% confidence interval].
Compared to control, the BDI group showed significantly greater proportion of sharp dissection (+ 23.5% [20.0-27.0]), blunt dissection (+ 32.1% [27.2-37.0]), and total interactions (+ 33.6% [31.0-36.2]) outside of the Go zone. Among injury-causing interactions, 4 (36%) were in the No-Go zone, 2 (18%) were in the Go zone, and 5 (45%) were outside both zones, after maximizing the probability threshold of the Go algorithm.
AI has potential to detect unsafe dissection and prevent BDIs through real-time intraoperative decision-support. More work is needed to determine how to optimize integration of this technology into the operating room workflow and adoption by end-users.
胆管损伤（BDI）是接受腹腔镜胆囊切除术（LC）患者发病的重要原因。GoNoGoNet是一种人工智能（AI）算法，已开发并验证可识别LC手术过程中的安全（“可行”）和危险（“不可行”）解剖区域，具有通过实时术中决策支持预防BDI的潜力。本研究评估GoNoGoNet在伴有BDI的LC手术中预测可行/不可行区域的能力。
GoNoGoNet对11个伴有BDI的LC视频（BDI组）进行注释。所有工具与组织的相互作用，包括导致BDI的相互作用，均根据算法预测的可行/不可行区域位置进行特征描述。将这些与另外11个患有胆囊炎的LC视频（对照组）进行比较，专家认为这些视频代表“安全胆囊切除术”。然后调整GoNoGoNet注释的概率阈值，以确定其与可行/不可行预测的关系。数据以%差异[99%置信区间]表示。
与对照组相比，BDI组在可行区域外进行锐性解剖（+23.5%[20.0-27.0]）、钝性解剖（+32.1%[27.2-37.0]）和总相互作用（+33.6%[31.0-36.2]）的比例显著更高。在导致损伤的相互作用中，在将Go算法的概率阈值最大化后，4次（36%）在不可行区域，2次（18%）在可行区域，5次（45%）在两个区域之外。
人工智能有潜力通过实时术中决策支持检测不安全解剖并预防BDI。需要开展更多工作来确定如何优化将该技术整合到手术室工作流程以及终端用户的采用情况。
