25dkou648 10.3205/25dkou648 urn:nbn:de:0183-25dkou6485 Meeting Abstract Twardy Twardy Vanessa V

Klinik und Poliklinik für Orthopädie und Sportorthopädie des Klinikums rechts der Isar, München, Deutschland

author Hinterwimmer Hinterwimmer Florian F

Institut für KI und Informatik in der Medizin (AIIM) des Klinikums rechts der Isar der Technischen Universität München, München, Deutschland

author Fütterer Fütterer Cornelia C

Institut für KI und Informatik in der Medizin (AIIM) des Klinikums rechts der Isar der Technischen Universität München, München, Deutschland

author Haller Haller Bernhard B

Institut für KI und Informatik in der Medizin (AIIM) des Klinikums rechts der Isar der Technischen Universität München, München, Deutschland

author Hömann Hömann Niklas N

Klinik und Poliklinik für Orthopädie und Sportorthopädie des Klinikums rechts der Isar, München, Deutschland

author Willinger Willinger Lukas L

Sektion Sportorthopädie, Klinik und Poliklinik für Orthopädie und Sportorthopädie des Klinikums rechts der Isar, München, Deutschland

author Blobner Blobner Kilian K

Klinik und Poliklinik für Orthopädie und Sportorthopädie des Klinikums rechts der Isar, München, Deutschland

author von Eisenhart-Rothe von Eisenhart-Rothe Rüdiger R

Klinik und Poliklinik für Orthopädie und Sportorthopädie des Klinikums rechts der Isar, München, Deutschland

author Banke Banke Ingo I

Klinik und Poliklinik für Orthopädie und Sportorthopädie des Klinikums rechts der Isar, München, Deutschland

author German Medical Science GMS Publishing House

Düsseldorf

610 20251031 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0634 648 Deutsche Gesellschaft für Orthopädie und Unfallchirurgie Deutsche Gesellschaft für Orthopädie und Orthopädische Chirurgie Deutsche Gesellschaft für Unfallchirurgie Berufsverband für Orthopädie und Unfallchirurgie Deutscher Kongress für Orthopädie und Unfallchirurgie (DKOU 2025) Abstracts | AGiTEC; Sektion Bildgebende Verfahren | FUTURE TREND: KI unterstützte Bildgebung & Navigation in O&U Berlin 20251028 20251031 BS33-3205 TextObjectives and questions: Success of technically challenging hip arthroscopy for femoroacetabular impingement syndrome (FAIS) with demanding (steep) learning curve and increased risk of complications critically depends on optimal intraoperative visualization. For the first, time Machine Learning Algorithm (MLA) and statistical modelling approaches are been utilized to predict and intelligently identify key parameter patterns that influence visibility during arthroscopy.Material and methods: A large-scale big data analysis powered by artificial intelligence was conducted on 11,403 measurements. In detail visualization conditions, categorized on a scale from 1–2 (good) to 3–5 (poor), were assessed at 10 key surgical steps (central/peripheral) during supine FAIS arthroscopy in a prospective consecutive monocentric single-surgeon cohort level 2 study involving 211 patients, and correlated with corresponding blood pressure readings, arthroscopy tower parameters, and patient- and surgery-related data. Preprocessing included handling of missing values, standardization, and categorical encoding. LASSO regression identified the most relevant features, which were then used to train an XGBoost classifier. The dataset was split into training (80%) and test (20%) subsets, and model performance was evaluated using accuracy, precision, recall, and F1-score.Results: Across all timepoints, the model demonstrated strong overall performance, achieving an average accuracy of 83.6%, precision of 83.8%, recall of 89.6%, and F1-score of 86.5%. At beginning of arthroscopic procedure, the MLA model achieved even higher performance with 90.7% accuracy, 92.9% precision, 97.5% recall, and a 95.1% F1-score. LASSO regression identified age, prior hip surgery, arthrosis (Tönnis-grade), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) as key predictors. Across all timepoints, most frequently selected variables were SBP, irrigation fluid flow rate (Flow), MAP, Tönnis-grade, and BMI. XGBoost feature importance ranked SBP as the strongest predictor, followed by Flow, MAP, and Tönnis-grade. Discussion and conclusion: According to our MLA, the key predictors for intraoperative visualization are SBP > Flow > MAP > Tönnis-grade. These findings underscore the critical role of hemodynamic parameters and patient-specific factors in optimizing hip joint visibility during arthroscopy and highlighting the potential for real-time monitoring with intraoperative adjustments. Future advancements should focus on developing interactive, high-automation arthroscopy towers capable of dynamically adapting to intraoperative conditions. The integration of MLA-driven advanced analytics in arthroscopy holds promise for optimizing complex, non-linear parameter interactions, with the objective of shortening the learning curve, improving patient outcomes, and facilitating complication-free outpatient procedures. 0 0 0 0