A scalable solution: effective AI implementation in laparoscopic simulation training assessments

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dc.article.number46
dc.catalogadoraba
dc.contributor.authorCruz, Enrique
dc.contributor.authorSelman, Rafael
dc.contributor.authorFigueroa, Úrsula
dc.contributor.authorBelmar, Francisca
dc.contributor.authorJarry T., Cristián
dc.contributor.authorSanhueza, Diego
dc.contributor.authorEscalona, Gabriel
dc.contributor.authorCarnier, Martín
dc.contributor.authorVaras Cohen, Julián
dc.date.accessioned2025-05-08T20:00:36Z
dc.date.available2025-05-08T20:00:36Z
dc.date.issued2025
dc.description.abstractPurpose Simulation-based training offers considerable benefits in surgical education, especially in mastering minimally invasive techniques such as laparoscopy. However, its widespread adoption faces challenges, particularly in teaching scalability and consistent assessment of trainees’ performance. Artificial Intelligence (AI) has emerged as a promising tool to address these issues, offering possible solutions for optimizing exercise evaluation in simulation programs. This study aims to validate the integration of the YOLO v4 artificial intelligence model into a laparoscopic simulation training program from a video-based feedback-oriented platform. Methods Focusing on object detection and time measurement, the evaluation included a dataset of 7673 videos, extracting 100 random video frames per exercise for object detection with precision, recall, and F1-score calculations based on AI expert tags for each frame, and using 80 videos per exercise for time analysis compared to established times by platform expert teachers. Results With a total of 1100 frames assessed, the YOLO v4 model consistently exhibited high precision with a mean precision of 0.94 and mean recall of 0.89 across object detection tasks, with F1-scores ranging from 0.81 to 0.97. Time measurement accuracy based on 880 assessed videos showed a correlation with mean Pearson correlation coefficient of 0.960 for mean absolute error (MAE) across exercises with the timings of platform expert evaluators. Conclusions The YOLO v4 model proves to be an effective tool for laparoscopic training assessments. The model's high precision and strong correlation with expert assessments demonstrate its usefulness in optimizing the evaluation process in simulated laparoscopic training exercises. This intervention leads the way in surgery education by effectively addressing the need for scalable and standardized assessment methods facilitating the transition towards automated evaluations.
dc.fuente.origenORCID
dc.identifier.doi10.1007/s44186-025-00355-9
dc.identifier.issn2731-4588
dc.identifier.urihttps://doi.org/10.1007/s44186-025-00355-9
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/104104
dc.information.autorucEscuela de Medicina; Jarry T., Cristián; 0000-0003-3548-4909; 205691
dc.information.autorucEscuela de Medicina; Varas Cohen, Julián; 0000-0002-5828-9623; 134158
dc.language.isoen
dc.nota.accesocontenido parcial
dc.revistaGlobal Surgical Education - Journal of the Association for Surgical Education
dc.rightsacceso restringido
dc.subjectSimulation training
dc.subjectSurgical education
dc.subjectLaparoscopy
dc.subjectArtificial intelligence
dc.subjectObject detection
dc.subject.ddc610
dc.subject.deweyMedicina y saludes_ES
dc.subject.ods04 Quality education
dc.subject.ods09 Industry, innovation and infrastructure
dc.subject.odspa04 Educación de calidad
dc.subject.odspa09 Industria, innovación e infraestructura
dc.titleA scalable solution: effective AI implementation in laparoscopic simulation training assessments
dc.typeartículo
dc.volumen4
sipa.codpersvinculados205691
sipa.codpersvinculados134158
sipa.trazabilidadORCID;2025-04-21
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