Noroozi Mohammad, St John Ace, Masino Caterina, Laplante Simon, Hunter Jaryd, Brudno Michael, Madani Amin, Kersten-Oertel Marta
Applied Perception Lab, Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada.
University of Maryland Medical Center, Baltimore, MD, United States.
JMIR Form Res. 2024 Jul 25;8:e52878. doi: 10.2196/52878.
Major bile duct injuries during laparoscopic cholecystectomy (LC), often stemming from errors in surgical judgment and visual misperception of critical anatomy, significantly impact morbidity, mortality, disability, and health care costs.
To enhance safe LC learning, we developed an educational mobile game, LapBot Safe Chole, which uses an artificial intelligence (AI) model to provide real-time coaching and feedback, improving intraoperative decision-making.
LapBot Safe Chole offers a free, accessible simulated learning experience with real-time AI feedback. Players engage with intraoperative LC scenarios (short video clips) and identify ideal dissection zones. After the response, users receive an accuracy score from a validated AI algorithm. The game consists of 5 levels of increasing difficulty based on the Parkland grading scale for cholecystitis.
Beta testing (n=29) showed score improvements with each round, with attendings and senior trainees achieving top scores faster than junior residents. Learning curves and progression distinguished candidates, with a significant association between user level and scores (P=.003). Players found LapBot enjoyable and educational.
LapBot Safe Chole effectively integrates safe LC principles into a fun, accessible, and educational game using AI-generated feedback. Initial beta testing supports the validity of the assessment scores and suggests high adoption and engagement potential among surgical trainees.
腹腔镜胆囊切除术(LC)期间的主要胆管损伤,通常源于手术判断失误和对关键解剖结构的视觉误判,对发病率、死亡率、残疾率和医疗保健成本有重大影响。
为了加强安全的LC学习,我们开发了一款教育手机游戏LapBot Safe Chole,它使用人工智能(AI)模型提供实时指导和反馈,改善术中决策。
LapBot Safe Chole提供免费、易获取的模拟学习体验以及实时AI反馈。玩家参与术中LC场景(短视频片段)并识别理想的解剖区域。回答后,用户会从经过验证的AI算法获得一个准确率分数。该游戏基于胆囊炎的帕克兰分级量表设置了5个难度递增的级别。
β测试(n = 29)显示每一轮分数都有所提高,主治医生和高级住院医生比初级住院医生更快获得高分。学习曲线和进展区分了参与者,用户级别与分数之间存在显著关联(P = .003)。玩家认为LapBot既有趣又有教育意义。
LapBot Safe Chole利用AI生成的反馈有效地将安全的LC原则融入一款有趣、易获取且有教育意义的游戏中。初步的β测试支持评估分数的有效性,并表明该游戏在外科住院医生中有很高的采用率和参与潜力。