26dkou584 10.3205/26dkou584 urn:nbn:de:0183-26dkou5840 Meeting Abstract Rubenthaler Rubenthaler Cedric C

Klinik für Unfall-, Hand- und Wiederherstellungschirurgie, Universitätsklinikum Essen, Essen, Deutschland

author Herten Herten Monika M

Klinik für Unfall-, Hand- und Wiederherstellungschirurgie, Universitätsklinikum Essen, Essen, Deutschland

author Simmler Simmler Lea L

Klinik für Unfall-, Hand- und Wiederherstellungschirurgie, Universitätsklinikum Essen, Essen, Deutschland

author Hohenberger Hohenberger Samuel S

Klinik für Unfall-, Hand- und Wiederherstellungschirurgie, Universitätsklinikum Essen, Essen, Deutschland

author Haubold Haubold Johannes J

Institut für Artifizielle Intelligenz in der Medizin, Universitätsklinikum Essen, Essen, Deutschland Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen, Deutschland

author Meyer Meyer Heinz-Lothar HL

Klinik für Unfall-, Hand- und Wiederherstellungschirurgie, Universitätsklinikum Essen, Essen, Deutschland

author Mester Mester Bastian B

Klinik für Unfall-, Hand- und Wiederherstellungschirurgie, Universitätsklinikum Essen, Essen, Deutschland

author Burggraf Burggraf Manuel M

Klinik für Orthopädie, Unfall-, Hand-, Fußchirurgie, GFO Kliniken Mettmann-Süd, Hilden, Deutschland

author Dudda Dudda Marcel M

Klinik für Unfall-, Hand- und Wiederherstellungschirurgie, Universitätsklinikum Essen, Essen, Deutschland Klinik für Orthopädie und Unfallchirurgie, BG Klinikum Duisburg, Duisburg, Deutschland

author Polan Polan Christina C

Klinik für Unfall-, Hand- und Wiederherstellungschirurgie, Universitätsklinikum Essen, Essen, Deutschland

author German Medical Science GMS Publishing House

Düsseldorf

610 20261020 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). M0659 584 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 2026) Poster experimentell | Varia Berlin 20261020 20261023 DKOU26_Abs_2235 TextObjectives and questions: Axial deformities of the lower limbs in adolescents and young adults are clinically relevant due to their association with progressive joint degeneration, patellar instability and functional impairment. Precise assessment of lower limb alignment is essential for diagnosis, preoperative planning and postoperative monitoring. Standing long-leg radiographs are the clinical standard for evaluating limb alignment, requiring precise angle and length measurements. Recent advances in artificial intelligence (AI) offer the potential for automated, rapid and reproducible measurement, which could reduce the clinical workload. This study aimed to compare software-assisted manual measurements with fully automated, AI-based analysis.Material and methods: This retrospective study included adolescents with closed growth plates and adults up to 40 years of age who were referred between 2013 and 2023. Of the 4,770 legs screened, strict radiographic quality criteria resulted in a final cohort of 304 legs. Most cases were non-operative and pathology-free. The most common pathological findings were standing blocks, metal implants, followed by tumors and fractures. Radiographs were analysed manually using TraumaCAD (Brainlab, Munich, Germany) and by AI software BoneMetrics (Gleamer, Paris, France). The comparison of both measurements included the following parameters: mechanical Lateral Proximal Femoral Angle (mLPFA), mechanical Lateral Distal Femoral Angle (mLDFA), mechanical Medial Proximal Tibial Angle (mMPTA), mechanical Lateral Distal Tibial Angle (mLDTA), joint line convergence angle (JLCA), Mechanical Axis Deviation (MAD), femur length, tibial length and total leg length. Data analysis included Bland-Altman plots, intraclass correlation coefficient (ICC) regression analyses.Results: The mean bias for angular measurements ranged from −0.48° to 0.57°, indicating no systematic over- or underestimation. Length measurements showed a small negative bias, ranging from −47.38 mm to −0.05 mm, suggesting slight underestimation. Overall, AI-based measurements demonstrated clinically acceptable agreement with manual analysis. Angular measurements were more reliable than length measurements, likely due to dependency on calibration. ICC analysis revealed moderate to excellent agreement (ICC>0.72) for all parameters, except for the JLCA, which demonstrated poor reliability (ICC=0.15).Discussion and conclusions: Overall, AI-based measurements showed clinically acceptable agreement with manual measurements, with angular measurements demonstrating higher reliability than bone length measurements. This difference may be attributable to calibration dependency affecting length assessment. The comparatively poor reliability of AI-based JLCA measurements may reflect inherent uncertainty or systematic errors. This finding is of particular clinical relevance, as JLCA has prognostic significance in corrective osteotomies. While AI-based analysis may serve as a support tool in clinical practice, caution is required when interpreting JLCA values. 0 0 0 0