{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T19:40:18Z","timestamp":1782416418604,"version":"3.54.5"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T00:00:00Z","timestamp":1764892800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T00:00:00Z","timestamp":1764892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Chester Ray Trout Chair in Pediatric Surgery at Penn State\u2019s Hershey Medical Center"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Surg Endosc"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The fundamentals of laparoscopic surgery (FLS) program uses box trainers to develop laparoscopic skills. However, these simulators lack personalized training, real-time objective assessment, and primarily represent adult anatomies, neglecting pediatric cases. To address these limitations, advanced objective evaluations like motion analysis and eye-tracking are needed to track trainees\u2019 progress and provide real-time formative feedback. However, dynamic training environments challenge eye-tracking data extraction due to shifting areas of interest (AOI). This study aimed to extract AOI-dependent and motion metrics for differentiating and predicting trainees\u2019 skill levels across different box trainer anatomies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>Medical students and residents performed the peg transfer task on adult and pediatric box trainers. Computer Vision-Deep Learning (CV-DL) algorithms were integrated with eye-tracking data to automatically detect AOIs and extract AOI-dependent (fixation rates on objects and tools) and motion (tool speed) metrics. K-means clustering was used to differentiate trainees\u2019 skill levels. To predict trainees\u2019 visual behavior, we employed multiple Machine Learning (ML) techniques, including Random Forest, Support Vector Machine, Artificial Neural Networks, and Decision Trees. These methods were used to evaluate which technique could most accurately predict trainees\u2019 visual attention patterns.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The extracted metrics successfully classified novices into High and Mid-Low skill levels, with significant differences in all extracted metrics between visual behavior levels (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05). Random Forest achieved the highest accuracy for visual behavior prediction, highlighting the importance of fixation rates on objects and tool speed as key predictors using Gini importance. Results showed consistency in novices\u2019 visual attention between pediatric and adult box trainers (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&gt;\u20090.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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\u2019 skill levels and visual behavior. This is important for customizing and adapting trainees\u2019 training programs to enhance their performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1007\/s00464-025-12369-x","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T17:09:17Z","timestamp":1764954557000},"page":"1925-1940","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["From gaze to proficiency: deep learning-driven prediction of novice performance in laparoscopic training using AOI-dependent metrics"],"prefix":"10.1007","volume":"40","author":[{"given":"Aseel F.","family":"Khanfar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanaz","family":"Motamedi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shawn D.","family":"Safford","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason","family":"Moore","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica","family":"Menold","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0938-9029","authenticated-orcid":false,"given":"Scarlett","family":"Miller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"key":"12369_CR1","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.surg.2020.11.046","volume":"170","author":"KS Chan","year":"2021","unstructured":"Chan KS, Wang ZK, Syn N, Goh BK (2021) Learning curve of laparoscopic and robotic pancreas resections: a systematic review. 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