Enhancing laparoscopic surgery training: a comparative study of traditional models and automated error detection system
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| Publicado en: | BMC Medical Education vol. 25 (2025), p. 1 |
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| Autor principal: | |
| Otros Autores: | , , , , , , , , , , |
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Springer Nature B.V.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 3201523502 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1472-6920 | ||
| 024 | 7 | |a 10.1186/s12909-025-07242-3 |2 doi | |
| 035 | |a 3201523502 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 58506 |2 nlm | ||
| 100 | 1 | |a Luo, Yitian | |
| 245 | 1 | |a Enhancing laparoscopic surgery training: a comparative study of traditional models and automated error detection system | |
| 260 | |b Springer Nature B.V. |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a BackgroundAlthough 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.MethodsForty 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.ResultsGroup 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.ConclusionThe 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.Clinical trial numberNot applicable. | |
| 653 | |a Students | ||
| 653 | |a Usability | ||
| 653 | |a Training | ||
| 653 | |a Questionnaires | ||
| 653 | |a Laparoscopy | ||
| 653 | |a Automation | ||
| 653 | |a Skills | ||
| 653 | |a Simulation | ||
| 653 | |a Surgery | ||
| 653 | |a Error correction & detection | ||
| 653 | |a Experiments | ||
| 653 | |a Data collection | ||
| 653 | |a Computer Simulation | ||
| 653 | |a Observational Learning | ||
| 653 | |a Error Correction | ||
| 653 | |a Learning Processes | ||
| 653 | |a Modeling (Psychology) | ||
| 653 | |a Time | ||
| 653 | |a Required Courses | ||
| 653 | |a Skill Development | ||
| 653 | |a Comparative Analysis | ||
| 653 | |a Outcomes of Education | ||
| 700 | 1 | |a Wang, Jingjie | |
| 700 | 1 | |a Zongting Yan | |
| 700 | 1 | |a He, Jingjing | |
| 700 | 1 | |a Fu, Liye | |
| 700 | 1 | |a Wang, Shenghan | |
| 700 | 1 | |a Han, Ying | |
| 700 | 1 | |a Fu, Yaoyu | |
| 700 | 1 | |a Wang, Xiandi | |
| 700 | 1 | |a Kang, Li | |
| 700 | 1 | |a Yin, Rong | |
| 700 | 1 | |a Pu, Dan | |
| 773 | 0 | |t BMC Medical Education |g vol. 25 (2025), p. 1 | |
| 786 | 0 | |d ProQuest |t Healthcare Administration Database | |
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| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3201523502/fulltext/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3201523502/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |