Kaji, Ellie

Introduction: Manual measurement of leg length (LL) and offset can be tedious. This study developed an automated algorithm for measuring LL and offset from pre- and postoperative AP pelvis radiographs in a large cohort of THA patients. Methods: Using a deep learning model trained on 1100 total AP pelvis radiographs, an algorithm was developed to calculate LL and offset. Algorithm measurements were compared with manual measurements by 4 raters on a sample of 100 pre- and postoperative image pairs. Inter- and intra-rater consistency was calculated using the intraclass correlation coefficient (ICC). The algorithm was applied to calculate the pre- and postoperative LL and offset discrepancies and the change in LL and offset bilaterally in a cohort of 15,951 image pairs. Results: ICC values between the algorithm and human raters ranged from 0.83 to 0.88 for offset measurements and 0.92 to 0.97 for LL measurements. Human raters demonstrated good-to-excellent inter-rater ICC and uniformly excellent intra-rater ICC. Entire database measurements demonstrated shorter LLs for arthritic joints versus the contralateral leg preoperatively and reduced LL discrepancy post-arthroplasty. Conclusions: We present a deep learning algorithm for calculating LL and offset using AP pelvis radiographs. This tool can support population-level studies and may assist operative management.

Abstract Morphological hip abnormalities (MHAs) significantly influence lifelong prognosis of the hip, contributing to early-onset osteoarthritis and impaired functionality. Developmental dysplasia of the hip (DDH) and femoroacetabular impingement (FAI) represent key pathologies, resulting from insufficient or excessive femoral head coverage, respectively. These abnormalities alter hip biomechanics, leading to structural damage, pain, and accelerated joint degeneration. Advances in genetic research have illuminated the interplay between genetics and mechanical loading in shaping hip morphology. Genes associated with osteoarthritis, DDH, and FAI include COL1A1, MMP13, and IL-6. Genes associated with FAI and osteoarthritis include ADAMTS4. Genes associated with DDH and osteoarthritis include FRZB, CX3CR1, ASPN, DKK1, PDRG1, GDF5, UQCC1, and TGF-β1. The mechanisms linking morphological derangements to symptomatic osteoarthritis remain incompletely understood. Multimodal approaches integrating imaging, biomechanics, and genetics may uncover distinct disease subtypes, enabling personalized interventions. Early detection of MHAs is critical in preventing early-onset osteoarthritis. Incorporating advanced imaging techniques, such as statistical shape modelling, can enhance the understanding of complex 3D hip morphologies and their progression to osteoarthritis. Future research should explore the genetic underpinnings of other morphologic hip conditions, including Slipped Capital Femoral Epiphysis and Legg–Calvé–Perthes disease, to refine preventive and therapeutic strategies. A comprehensive approach combining genetics, imaging, and clinical insights holds promise for mitigating the lifelong impact of MHAs.

Abstract Purpose Developing large‐scale, standardized radiographic registries for anterior cruciate ligament (ACL) injuries with artificial intelligence (AI) tools can enhance personalized orthopaedics. We propose deploying Artificial Intelligence for Knee Imaging Registration and Analysis (AKIRA), a trio of deep learning (DL) algorithms, to automatically classify and annotate radiographs. We hypothesize that algorithms can efficiently organize radiographs based on laterality, projection, identify implants and classify osteoarthritis (OA) grade. Methods A collection of 20,836 knee radiographs from all time points of treatment (mean orthopaedic follow‐up 70.7 months; interquartile range [IQR]: 6.8–172 months) were aggregated from 1628 ACL‐injured patients (median age 26 years [IQR: 19–42], 57% male). Three DL algorithms (EfficientNet, YOLO [You Only Look Once] and Residual Network) were employed. Radiograph laterality and projection (anterior‐posterior [AP], lateral, sunrise, posterior‐anterior, hip–knee–ankle and Camp‐Coventry intercondylar [notch]) were labelled by a DL model. Manually provided labels of metal fixation implants were used to develop a DL object detection algorithm. The degree of OA, both as measured by specific Kellgren–Lawrence (KL) grades, as well as based on a binarized label of OA (defined as KL Grade ≥2), on standing AP radiographs were classified using a DL algorithm. Individual model performances were evaluated on a subset of images prior to the deployment of AKIRA to registry construction using all ACL radiographs. Results The classification algorithms showed excellent performance in classifying radiographic laterality (F1 score: 0.962–0.975) and projection (F1 score: 0.941–1.0). The object detection algorithm achieved high precision–recall (area under the precision‐recall curve: 0.695–0.992) for identifying various metal fixations. The KL classifier reached concordances of 0.39–0.40, improving to 0.81–0.82 for binary OA labels. Sequential deployment of AKIRA following internal validation processed and labelled all 20,836 images with the appropriate views, implants, and the presence of OA within 88 min. Conclusion AKIRA effectively automated the classification and object detection in a large radiograph cohort of ACL injuries, creating an AI‐enabled radiographic registry with comprehensive details on laterality, projection, implants and OA. Study Design Cross‐sectional study. Level of Evidence Level IV.