From gaze to proficiency: deep learning-driven prediction of novice performance in laparoscopic training using AOI-dependent metrics
The findings from this work are significant, indicating that novices' skill levels may differ even in their early-stage training, and extracted metrics have the potential to classify and predict novices' skill levels and visual behavior. This is important for customizing and adapting trainees' training programs to enhance their performance.
Authors
- Shawn D. Safford (ORCID: https://orcid.org/0000-0002-4881-8093)
- Jessica Menold (ORCID: https://orcid.org/0000-0002-9775-8736)
- Jason Z. Moore (ORCID: https://orcid.org/0000-0002-8830-2833)
- Sanaz Motamedi (ORCID: https://orcid.org/0009-0005-0938-9029)
- Aseel F. Khanfar
Institutions
- Pennsylvania State University (US)
- Children's Hospital of Pittsburgh (US)
- Yarmouk University (JO)
Publication Details
- Journal
- Surgical Endoscopy
- Published
- 2025-12-05
- DOI
- https://doi.org/10.1007/s00464-025-12369-x
- Primary Topic
- Surgical Simulation and Training
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- University of Pennsylvania
- Pennsylvania State University