{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:23:02Z","timestamp":1792783382812,"version":"4.5.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Tel Aviv University","id":[{"id":"https:\/\/ror.org\/04mhzgx49","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Updates Surg"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Routine real-world use of a surgical artificial-intelligence (AI) platform has led us to adopt Parkland\u2019s Grading Scale (PGS) for disease-severity in laparoscopic cholecystectomy (LC). We evaluated model performance in estimating PGS and explored correlations with surgical outcomes. The platform routinely video-captured LCs and automatically assigned PGS scores across 249 consecutive LCs that were classified into Low (PGS 1\u20132;\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200978, 31.3%) and High (PGS 3\u20135;\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009171, 68.7%) groups. Surgical outcomes were compared. Case-control matching (CCM;\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200984) was performed to balance possible confounders. Two surgeons independently reviewed a video sample (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200975) twice to establish ground-truth for F1-score calculations. Model discrimination (AUC) and calibration were evaluated separately against each rater. High-severity cases presented with significantly older age (63.2y vs. 47.1y), higher ASA scores (\n                    <jats:italic>\u2265<\/jats:italic>\n                    \u20093: 29.8% vs. 9%), cholecystitis (47.4% vs. 10.3%), and urgent surgery (12.3% vs. 0) (all\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001). The High group experienced longer operative durations (57.6 vs. 35.3\u00a0min;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001), more intraoperative events (80.1% vs. 61.5%;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.003) and bailouts (10.5% vs. 0;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001). Hospitalizations were longer (2d vs. 1d;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001), with more 90-day major complications and readmissions (10.5% vs. 2.6%;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.04). After CCM, operative durations (mean rank 49.51 vs. 35.49;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.008) and hemorrhage-related events (mean rank 46.0 vs. 39.0;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.047) maintained significance. The AI model achieved high F1 scores (High\u2009=\u20090.96, Low\u2009=\u20090.93), strong discrimination (AUC: 0.932 and 0.896), and robust calibration- peaking at PGS\u2009=\u20093. The AI model shows promise for surgical outcome prediction in LC and, with further validation, could be integrated into routine clinical frameworks.\n                  <\/jats:p>","DOI":"10.1007\/s13304-026-02761-0","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T15:57:06Z","timestamp":1789487826000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence assessment of Parkland\u2019s grading scale in laparoscopic cholecystectomy: a step toward real-world outcome prediction"],"prefix":"10.1007","author":[{"given":"Yuval","family":"Mirkin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monica","family":"Ortenzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eran","family":"Nizri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3065-741X","authenticated-orcid":false,"given":"Danit","family":"Dayan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"2761_CR1","doi-asserted-by":"publisher","first-page":"3310","DOI":"10.1007\/s00464-024-10847-2","volume":"38","author":"D Dayan","year":"2024","unstructured":"Dayan D, Dvir N, Agbariya H, Nizri E (2024) Implementation of artificial intelligence-based computer vision model in laparoscopic appendectomy: validation, reliability, and clinical correlation. 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He had no decision-making authority within the company, and held minimal financial interest in the company. Dr. Monica Ortenzi in an advisor at Theator and holds minimal financial interest in the company. She did not receive funding for this study. Drs. Prof. Eran Nizri and Dr. Dayan report no conflicts of interest and no financial or personal relationships to disclose that could inappropriately bias the work.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}