Luo Yitian, Wang Jingjie, Yan Zongting, He Jingjing, Fu Liye, Wang Shenghan, Han Ying, Fu Yaoyu, Wang Xiandi, Li Kang, Yin Rong, Pu Dan
West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Pittsburgh Institute, Sichuan University, Chengdu, China.
BMC Med Educ. 2025 May 8;25(1):677. doi: 10.1186/s12909-025-07242-3.
Although beneficial for patients through its minimally invasive nature, laparoscopic surgery creates unique training challenges due to limited instrument maneuverability, absence of stereovision, and inadequate real-time feedback. Traditional training models rely on subjective instructor evaluations, which are time-consuming and lack objective error detection. This study evaluates the efficacy of an Automated Error Detection System (AEDS), designed to provide real-time feedback on mistouch error counts, in improving laparoscopic skill acquisition compared to conventional methods.
Forty novice participants were recruited and randomized into Group A (AEDS-enhanced training) and Group B (traditional training). Group A underwent a crossover design: 10 min of baseline training without AEDS followed by 10 min with AEDS. Group B completed 20 min of traditional training. The training program encompassed standardized laparoscopic tasks designed to simulate real surgical procedures. Performance metrics, including task completion time and the number of errors made, were recorded for each participant through AEDS. Confidence levels were assessed through self-reported questionnaires. Furthermore, statistical analysis was performed to evaluate the effectiveness of AEDS. A paired t-test was utilized to assess error reductions within the AEDS group, and Bland-Altman analysis was used to analyze the self-estimate error bias. Also, a Wilcoxon signed-rank test evaluated improvements in confidence levels attributable to the system, while a Mann-Whitney U test was conducted to compare performance metrics between the AEDS and traditional training groups.
Group A demonstrated a 24% reduction in errors post-AEDS (mean: 78.1 to 59.4, p < 0.001), outperforming Group B (mean: 67.4, p < 0.001). Participants significantly underestimated errors without AEDS (mean bias: +9.9 errors). Confidence levels in Group A increased from 2.4 to 3.6, significantly surpassing Group B's improvement (median: 3) (p < 0.001). Real-time feedback bridged perceptual gaps, enhancing both technical precision and self-assessment accuracy.
The integration of AEDS into laparoscopic training significantly reduces operational errors, accelerates skill acquisition, and boosts trainee confidence by providing objective feedback. These findings advocate for adopting AEDS in surgical education to standardize training outcomes, mitigate overconfidence, and improve patient safety. Future studies should explore AEDS scalability across advanced procedural modules and diverse trainee cohorts.
Not applicable.
尽管腹腔镜手术因其微创特性对患者有益，但由于器械可操作性有限、缺乏立体视觉以及实时反馈不足，它带来了独特的培训挑战。传统培训模式依赖主观的教员评估，既耗时又缺乏客观的错误检测。本研究评估了一种自动错误检测系统（AEDS）在提高腹腔镜技能习得方面的效果，该系统旨在提供关于误触错误计数的实时反馈，并与传统方法进行比较。
招募了40名新手参与者，并随机分为A组（AEDS强化训练）和B组（传统训练）。A组采用交叉设计：先进行10分钟无AEDS的基线训练，然后进行10分钟有AEDS的训练。B组完成20分钟的传统训练。训练项目包括旨在模拟实际手术操作的标准化腹腔镜任务。通过AEDS记录每个参与者的性能指标，包括任务完成时间和所犯错误的数量。通过自我报告问卷评估信心水平。此外，进行统计分析以评估AEDS的有效性。采用配对t检验评估AEDS组内的错误减少情况，使用Bland-Altman分析来分析自我估计误差偏差。同时，采用Wilcoxon符号秩检验评估该系统带来的信心水平提升，而进行Mann-Whitney U检验以比较AEDS组和传统训练组之间的性能指标。
A组在使用AEDS后错误减少了24%（平均值：从78.1降至59.4，p < 0.001），优于B组（平均值：67.4，p < 0.001）。参与者在没有AEDS时显著低估了错误（平均偏差：+9.9个错误）。A组的信心水平从2.4提高到3.6，显著超过B组的提高幅度（中位数：3）（p < 0.001）。实时反馈弥合了感知差距，提高了技术精度和自我评估准确性。
将AEDS整合到腹腔镜训练中可显著减少操作错误，加速技能习得，并通过提供客观反馈增强学员信心。这些发现主张在外科教育中采用AEDS，以标准化训练结果，减轻过度自信，并提高患者安全性。未来的研究应探索AEDS在高级程序模块和不同学员群体中的可扩展性。
不适用。
