Decoding surgical proficiency and complexity: A machine learning framework for robotic herniorrhaphy

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dc.authorid0000-0003-2076-270X
dc.authorid0000-0003-0592-861X
dc.authorid0000-0003-0832-7991
dc.authorid0000-0002-3331-9910
dc.authorid0000-0002-5211-1779
dc.authorid0000-0001-6723-0909
dc.contributor.authorShin, Thomas H.
dc.contributor.authorFanta, Abeselom
dc.contributor.authorGökçal, Fahri
dc.contributor.authorShields, Mallory
dc.contributor.authorBenlice, Çiğdem
dc.contributor.authorKudsi, Omar Yusef
dc.date.accessioned2026-01-22T16:21:55Z
dc.date.available2026-01-22T16:21:55Z
dc.date.issued2025
dc.departmentFakülteler, Tıp Fakültesi, Cerrahi Tıp Bilimleri Bölümü, Genel Cerrahi Ana Bilim Dalı
dc.description.abstractObjective To evaluate the predictive value of objective performance indicators (OPIs) for case complexity assessment and explore their role in quantifying skill acquisition during robotic ventral herniorrhaphy. Summary background data Despite advances in herniorrhaphy techniques, unclear metrics of case complexity have signifi cant implications for operative planning, resource allocation, and patient outcomes. While existing complexity definitions rely primarily on clinical factors external to operator behavior, the expanding adoption of robotic platforms in ventral her nia repair provides unprecedented access to quantifiable surgical performance metrics. However, the relationship between these objective performance indicators and both case complexity and skill development remains incompletely understood, representing a gap that machine learning approaches may help address. Methods OPI and clinical data from 561 consecutive robotic ventral hernia repairs over eight years were analyzed using iterative ensemble machine learning models to predict case complexity. Dimensional reduction analyses using t-distributed stochastic neighbor embedding tracked skill evolution, with Euclidean distances calculated between successive cases to quantify skill acquisition over time. Results Gradient boosting models integrating clinical and OPI variables achieved F1 score of 0.87, while OPIs alone scored 0.58. Longitudinal analysis revealed high OPI variability during early cases, stabilizing within 10 months despite increas ing case complexity, indicating skill acquisition may compensate for procedural difficulty. Dimensional reduction analyses captured this evolution through weighted Euclidean distances. Conclusions Objective performance indicators poorly predict case complexity independently, yet their temporal evolution reveals surgical skill acquisition. The concurrent stabilization of OPI stochasticity and progression to more complex cases demonstrates that surgical proficiency and complexity assessment are interdependent phenomena, establishing digital metrics as tools for understanding the dynamic relationship between surgeon learning and case difficulty.
dc.identifier.citationShin, T. H., Fanta, A., Gökçal, F., Shields, M., Benlice, Ç., & Kudsi, O. Y. (2025). Decoding surgical proficiency and complexity: A machine learning framework for robotic herniorrhaphy. Surgical Endoscopy, 40(1), pp. 774-783. https://doi.org/10.1007/s00464-025-12412-x
dc.identifier.doi10.1007/s00464-025-12412-x
dc.identifier.endpage783
dc.identifier.issn1432-2218
dc.identifier.issn0930-2794
dc.identifier.issue1
dc.identifier.pmidPMID: 41331529
dc.identifier.scopus2-s2.0-105023868791
dc.identifier.scopusqualityQ1
dc.identifier.startpage774
dc.identifier.urihttps://doi.org/10.1007/s00464-025-12412-x
dc.identifier.urihttps://hdl.handle.net/20.500.13055/1264
dc.identifier.volume40
dc.identifier.wosWOS:001629278800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.indekslendigikaynak.otherSCI-E - Science Citation Index Expanded
dc.institutionauthorBenlice, Çiğdem
dc.institutionauthorid0000-0002-5211-1779
dc.language.isoen
dc.publisherSpringer Nature Link
dc.relation.ispartofSurgical Endoscopy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectObjective Performance Indicators
dc.subjectMachine Learning
dc.subjectRobotic Ventral Hernia Repair
dc.titleDecoding surgical proficiency and complexity: A machine learning framework for robotic herniorrhaphy
dc.typeArticle
dspace.entity.typePublication

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