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Aims: Distal femoral osteotomies (DFOs) are commonly used for the correction of valgus deformities and lateral compartment osteoarthritis. However, the impact of a DFO on subsequent total knee arthroplasty (TKA) function remains a subject of debate. Therefore, the purpose of this study was to determine the effect of a unilateral DFO on subsequent TKA function in patients with bilateral TKAs, using the contralateral knee as a self-matched control group. Methods: The inclusion criteria consisted of patients who underwent simultaneous or staged bilateral TKA after prior unilateral DFO between 1972 and 2023. The type of osteotomy performed, osteotomy hardware fixation, implanted TKA components, and revision rates were recorded. Postoperative outcomes including the Forgotten Joint Score-12 (FJS-12), Tegner Activity Scale score, and subjective knee preference were also obtained at final follow-up. Results: A total of 21 patients underwent bilateral TKA following unilateral DFO and were followed for a mean of 31.5 years (SD 11.1; 20.2 to 74.2) after DFO. The mean time from DFO to TKA conversion was 13.1 years (SD 9.7) with 13 (61.9%) of DFO knees converting to TKA more than ten years after DFO. There was no difference in arthroplasty implant systems employed in both the DFO-TKA and TKA-only knees (p > 0.999). At final follow-up, the mean FJS-12 of the DFO-TKA knee was 62.7 (SD 36.6), while for the TKA-only knee it was 65.6 (SD 34.7) (p = 0.328). In all, 80% of patients had no subjective knee preference or preferred their DFO-TKA knee. Three DFO-TKA knees and two TKA-only knees underwent subsequent revision following index arthroplasty at a mean of 12.8 years (SD 6.9) and 8.5 years (SD 3.8), respectively (p > 0.999). Conclusion: In this self-matched study, DFOs did not affect subsequent TKA function as clinical outcomes, subjective knee preference, and revision rates were similar in both the DFO-TKA and TKA-only knees at mean 32-year follow-up. Cite this article: Bone Jt Open 2024;5(11):1013–1019.

Background: Discrepancies in medical data sets can perpetuate bias, especially when training deep learning models, potentially leading to biased outcomes in clinical applications. Understanding these biases is crucial for the development of equitable healthcare technologies. This study employs generative deep learning technology to explore and understand radiographic differences based on race among patients undergoing total hip arthroplasty. Methods: Utilizing a large institutional registry, we retrospectively analyzed pelvic radiographs from total hip arthroplasty patients, characterized by demographics and image features. Denoising diffusion probabilistic models generated radiographs conditioned on demographic and imaging characteristics. Fréchet Inception Distance assessed the generated image quality, showing the diversity and realism of the generated images. Sixty transition videos were generated that showed transforming White pelvises to their closest African American counterparts and vice versa while controlling for patients’ sex, age, and body mass index. Two expert surgeons and 2 radiologists carefully studied these videos to understand the systematic differences that are present in the 2 races’ radiographs. Results: Our data set included 480,407 pelvic radiographs, with a predominance of White patients over African Americans. The generative denoising diffusion probabilistic model created high-quality images and reached an Fréchet Inception Distance of 6.8. Experts identified 6 characteristics differentiating races, including interacetabular distance, osteoarthritis degree, obturator foramina shape, femoral neck-shaft angle, pelvic ring shape, and femoral cortical thickness. Conclusions: This study demonstrates the potential of generative models for understanding disparities in medical imaging data sets. By visualizing race-based differences, this method aids in identifying bias in downstream tasks, fostering the development of fairer healthcare practices.

Hip and knee arthroplasty are high-volume procedures undergoing rapid growth. The large volume of procedures generates a vast amount of data available for next-generation analytics. Techniques in the field of artificial intelligence (AI) can assist in large-scale pattern recognition and lead to clinical insights. AI methodologies have become more prevalent in orthopaedic research. This review will first describe an overview of AI in the medical field, followed by a description of the 3 arthroplasty research areas in which AI is commonly used (risk modeling, automated radiographic measurements, arthroplasty registry construction). Finally, we will discuss the next frontier of AI research focusing on model deployment and uncertainty quantification.

Purpose: To report the clinical outcomes and reoperation rates of arthroscopic and endoscopic iliopsoas release at short-term follow-up after ipsilateral total hip arthroplasty (THA) at 2 separate medical institutions and to evaluate whether demographic and radiographic parameters are associated with postoperative patient-reported outcomes (PROs). Methods: Patients with iliopsoas tendinitis in the setting of prior THA who underwent arthroscopic iliopsoas fractional lengthening from 1988 to 2023 at 2 academic institutions were reviewed. Patients were included if they had 12 months of follow-up and underwent evaluation of preoperative anterior acetabular component overhang, surgery satisfaction, postoperative subjective hip flexion strength and anterior groin pain improvement, modified Harris Hip Score, Single Assessment Numeric Evaluation score, Tegner activity scale score, visual analog scale (VAS) score, and revision hip arthroplasty. Results: Sixty hips in 58 patients (19 male and 39 female patients) were followed up for a mean of 39.3 months (range, 12.0-105.9 months) postoperatively. Of the patients, 77% reported feeling “much better” or “slightly better,” 75% reported improved anterior groin pain, and 60% reported improved subjective hip flexion strength. The surgery satisfaction rating was 7.2 ± 3.3 (scale of 0 to 10). The mean postoperative modified Harris Hip Score, VAS score for pain at rest, VAS score for pain with use, and Single Assessment Numeric Evaluation score were 73.9 ± 19.4, 1.3 ± 2.4, 3.8 ± 2.9, and 71.9 ± 21.9, respectively. Preoperative anterior acetabular component overhang was 3.3 ± 6.5 mm and did not significantly correlate with postoperative PROs (P ≥ .45). The Tegner score improved from 2.5 ± 1.7 preoperatively to 2.9 ± 1.4 postoperatively (P = .0253). Three patients underwent revision arthroplasty at a mean of 25.3 months (range, 11.6-40.4 months) postoperatively, with an acetabular component revision rate of 3.3%. Conclusions: Satisfactory outcomes and low revision arthroplasty rates were observed in patients undergoing arthroscopic iliopsoas lengthening after THA. There was no statistically significant relation between anterior acetabular component overhang and final PROs. Level of Evidence: Level IV, therapeutic case series.