Aims. Recent studies have suggested that corticosteroid injections into the knee may harm the joint resulting in cartilage loss and possibly accelerating the progression of osteoarthritis (OA). The aim of this study was to assess whether patients with, or at risk of developing, symptomatic osteoarthritis of the knee who receive intra-articular corticosteroid injections have an increased risk of requiring arthroplasty. Methods. We used data from the Osteoarthritis Initiative (OAI), a multicentre observational cohort study that followed 4,796 patients with, or at risk of developing, osteoarthritis of the knee on an annual basis with follow-up available up to nine years. Increased risk for symptomatic OA was defined as frequent knee symptoms (pain, aching, or stiffness) without radiological evidence of OA and two or more risk factors, while OA was defined by the presence of both femoral osteophytes and frequent symptoms in one or both knees. Missing data were imputed with multiple imputations using chained equations. Time-dependent propensity score matching was performed to match patients at the time of receving their first injection with controls. The effect of corticosteroid injections on the rate of subsequent (total and partial) knee arthroplasty was estimated using Cox proportional-hazards survival analyses. Results. After removing patients lost to follow-up, 3,822 patients remained in the study. A total of 249 (31.3%) of the 796 patients who received corticosteroid injections, and 152 (5.0%) of the 3,026 who did not, had knee arthroplasty. In the matched cohort, Cox proportional-hazards regression resulted in a hazard ratio of 1.57 (95% confidence interval (CI) 1.37 to 1.81; p < 0.001) and each injection increased the absolute risk of arthroplasty by 9.4% at nine years’ follow-up compared with those who did not receive injections. Conclusion. Corticosteroid injections seem to be associated with an increased risk of knee arthroplasty in patients with, or at risk of developing, symptomatic OA of the knee. These findings suggest that a conservative approach regarding the treatment of these patients with corticosteroid injections should be recommended. Cite this article: Bone Joint J 2020;102-B(5):586–592

Aims. Accurate identification of the ankle joint centre is critical for estimating tibial coronal alignment in total knee arthroplasty (TKA). The purpose of the current study was to leverage artificial intelligence (AI) to determine the accuracy and effect of using different radiological anatomical landmarks to quantify mechanical alignment in relation to a traditionally defined radiological ankle centre. Methods. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A sub-cohort of 250 radiographs were annotated for landmarks relevant to knee alignment and used to train a deep learning (U-Net) workflow for angle calculation on the entire database. The radiological ankle centre was defined as the midpoint of the superior talus edge/tibial plafond. Knee alignment (hip-knee-ankle angle) was compared against 1) midpoint of the most prominent malleoli points, 2) midpoint of the soft-tissue overlying malleoli, and 3) midpoint of the soft-tissue sulcus above the malleoli. Results. A total of 932 bilateral full-limb radiographs (1,864 knees) were measured at a rate of 20.63 seconds/image. The knee alignment using the radiological ankle centre was accurate against ground truth radiologist measurements (inter-class correlation coefficient (ICC) = 0.99 (0.98 to 0.99)). Compared to the radiological ankle centre, the mean midpoint of the malleoli was 2.3 mm (SD 1.3) lateral and 5.2 mm (SD 2.4) distal, shifting alignment by 0.34. o. (SD 2.4. o. ) valgus, whereas the midpoint of the soft-tissue sulcus was 4.69 mm (SD 3.55) lateral and 32.4 mm (SD 12.4) proximal, shifting alignment by 0.65. o. (SD 0.55. o. ) valgus. On the intermalleolar line, measuring a point at 46% (SD 2%) of the intermalleolar width from the medial malleoli (2.38 mm medial adjustment from midpoint) resulted in knee alignment identical to using the radiological ankle centre. Conclusion. The current study leveraged AI to create a consistent and objective model that can estimate patient-specific adjustments necessary for optimal landmark usage in extramedullary and computer-guided navigation for tibial coronal alignment to match radiological planning. Cite this article: Bone Jt Open 2022;3(10):767–776

Aims. Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. Methods. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures. Results. Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations. Conclusion. Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice. Cite this article: Bone Joint J 2024;106-B(11):1216–1222

Aims. Distal femoral resection in conventional total knee arthroplasty (TKA) utilizes an intramedullary guide to determine coronal alignment, commonly planned for 5° of valgus. However, a standard 5° resection angle may contribute to malalignment in patients with variability in the femoral anatomical and mechanical axis angle. The purpose of the study was to leverage deep learning (DL) to measure the femoral mechanical-anatomical axis angle (FMAA) in a heterogeneous cohort. Methods. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A DL workflow was created to measure the FMAA and validated against human measurements. To reflect potential intramedullary guide placement during manual TKA, two different FMAAs were calculated either using a line approximating the entire diaphyseal shaft, and a line connecting the apex of the femoral intercondylar sulcus to the centre of the diaphysis. The proportion of FMAAs outside a range of 5.0° (SD 2.0°) was calculated for both definitions, and FMAA was compared using univariate analyses across sex, BMI, knee alignment, and femur length. Results. The algorithm measured 1,078 radiographs at a rate of 12.6 s/image (2,156 unique measurements in 3.8 hours). There was no significant difference or bias between reader and algorithm measurements for the FMAA (p = 0.130 to 0.563). The FMAA was 6.3° (SD 1.0°; 25% outside range of 5.0° (SD 2.0°)) using definition one and 4.6° (SD 1.3°; 13% outside range of 5.0° (SD 2.0°)) using definition two. Differences between males and females were observed using definition two (males more valgus; p < 0.001). Conclusion. We developed a rapid and accurate DL tool to quantify the FMAA. Considerable variation with different measurement approaches for the FMAA supports that patient-specific anatomy and surgeon-dependent technique must be accounted for when correcting for the FMAA using an intramedullary guide. The angle between the mechanical and anatomical axes of the femur fell outside the range of 5.0° (SD 2.0°) for nearly a quarter of patients. Cite this article: Bone Jt Open 2024;5(2):101–108

Aims. To explore the clinical relevance of joint space width (JSW) narrowing on standardized-flexion (SF) radiographs in the assessment of cartilage degeneration in specific subregions seen on MRI sequences in knee osteoarthritis (OA) with neutral, valgus, and varus alignments, and potential planning of partial knee arthroplasty. Methods. We retrospectively reviewed 639 subjects, aged 45 to 79 years, in the Osteoarthritis Initiative (OAI) study, who had symptomatic knees with Kellgren and Lawrence grade 2 to 4. Knees were categorized as neutral, valgus, and varus knees by measuring hip-knee-angles on hip-knee-ankle radiographs. Femorotibial JSW was measured on posteroanterior SF radiographs using a special software. The femorotibial compartment was divided into 16 subregions, and MR-tomographic measurements of cartilage volume, thickness, and subchondral bone area were documented. Linear regression with adjustment for age, sex, body mass index, and Kellgren and Lawrence grade was used. Results. We studied 345 neutral, 87 valgus, and 207 varus knees. Radiological JSW narrowing was significantly (p < 0.01) associated with cartilage volume and thickness in medial femorotibial compartment in neutral (r = 0.78, odds ratio (OR) 2.33) and varus knees (r = 0.86, OR 1.92), and in lateral tibial subregions in valgus knees (r = 0.87, OR 3.71). A significant negative correlation was found between JSW narrowing and area of subchondral bone in external lateral tibial subregion in valgus knees (r = −0.65, p < 0.01) and in external medial tibial subregion in varus knees (r = −0.77, p < 0.01). No statistically significant correlation was found in anterior and posterior subregions. Conclusion. SF radiographs can be potentially used for initial detection of cartilage degeneration as assessed by MRI in medial and lateral but not in anterior or posterior subregions. Cite this article: Bone Joint Res 2021;10(3):173–187

The June 2024 Hip & Pelvis Roundup³⁶⁰ looks at: Machine learning did not outperform conventional competing risk modelling to predict revision arthroplasty; Unravelling the risks: incidence and reoperation rates for femoral fractures post-total hip arthroplasty; Spinal versus general anaesthesia for hip arthroscopy: a COVID-19 pandemic- and opioid epidemic-driven study; Development and validation of a deep-learning model to predict total hip arthroplasty on radiographs; Ambulatory centres lead in same-day hip and knee arthroplasty success; Exploring the impact of smokeless tobacco on total hip arthroplasty outcomes: a deeper dive into postoperative complications.

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered.

Cite this article: Bone Joint J 2022;104-B(12):1292–1303.

We studied whether the presence of lateral osteophytes on plain radiographs was a predictor for the quality of cartilage in the lateral compartment of patients with varus osteoarthritic of the knee (Kellgren and Lawrence grade 2 to 3).

The baseline MRIs of 344 patients from the Osteoarthritis Initiative (OAI) who had varus osteoarthritis (OA) of the knee on hip-knee-ankle radiographs were reviewed. Patients were categorised using the Osteoarthritis Research Society International (OARSI) osteophyte grading system into 174 patients with grade 0 (no osteophytes), 128 grade 1 (mild osteophytes), 28 grade 2 (moderate osteophytes) and 14 grade 3 (severe osteophytes) in the lateral compartment (tibia). All patients had Kellgren and Lawrence grade 2 or 3 arthritis of the medial compartment. The thickness and volume of the lateral cartilage and the percentage of full-thickness cartilage defects in the lateral compartment was analysed.

There was no difference in the cartilage thickness or cartilage volume between knees with osteophyte grades 0 to 3. The percentage of full-thickness cartilage defects on the tibial side increased from < 2% for grade 0 and 1 to 10% for grade 3.

The lateral compartment cartilage volume and thickness is not influenced by the presence of lateral compartment osteophytes in patients with varus OA of the knee. Large lateral compartment osteophytes (grade 3) increase the likelihood of full-thickness cartilage defects in the lateral compartment.

Cite this article: Bone Joint J 2015;97-B:1634–9.

The June 2013 Research Roundup³⁶⁰ looks at: a contact patch to rim distance and metal ions; the matrix of hypoxic cartilage; CT assessment of early fracture healing; Hawthornes and radiographs; cardiovascular mortality and fragility fractures; and muscle strength decline preceding OA changes.

Early and accurate prediction of hospital length-of-stay (LOS) in patients undergoing knee replacement is important for economic and operational reasons. Few studies have systematically developed a multivariable model to predict LOS. We performed a retrospective cohort study of 1609 patients aged ≥ 50 years who underwent elective, primary total or unicompartmental knee replacements. Pre-operative candidate predictors included patient demographics, knee function, self-reported measures, surgical factors and discharge plans. In order to develop the model, multivariable regression with bootstrap internal validation was used. The median LOS for the sample was four days (interquartile range 4 to 5). Statistically significant predictors of longer stay included older age, greater number of comorbidities, less knee flexion range of movement, frequent feelings of being down and depressed, greater walking aid support required, total (versus unicompartmental) knee replacement, bilateral surgery, low-volume surgeon, absence of carer at home, and expectation to receive step-down care. For ease of use, these ten variables were used to construct a nomogram-based prediction model which showed adequate predictive accuracy (optimism-corrected R² = 0.32) and calibration. If externally validated, a prediction model using easily and routinely obtained pre-operative measures may be used to predict absolute LOS in patients following knee replacement and help to better manage these patients.

Cite this article: Bone Joint J 2013;95-B:1490–6.