Our aim was to assess the ability of radiography-based bone texture parameters in proximal femur and acetabulum to predict incident radiographic hip osteoarthritis (rHOA) over a 10 years period. Pelvic radiographs from CHECK (Cohort Hip and Cohort Knee) at baseline (987 hips) were analyzed for bone texture using fractal signature analysis in proximal femur and acetabulum. Elastic net (machine learning) was used to predict the incidence of rHOA (Kellgren-Lawrence grade (KL) ≥ 2 or total hip replacement (THR)), joint space narrowing score (JSN, range 0-3), and osteophyte score (OST, range 0-3) after 10 years. Performance of prediction models was assessed using the area under the receiver operating characteristic curve (ROC AUC). Of the 987 hips without rHOA at baseline, 435 (44%) had rHOA at 10-year follow-up. Of the 667 hips with JSN grade 0 at baseline, 471 (71%) had JSN grade ≥ 1 at 10-year follow-up. Of the 613 hips with OST grade 0 at baseline, 526 (86%) had OST grade ≥ 1 at 10-year follow-up. AUCs for the models including age, gender, and body mass index to predict incident rHOA, JSN, and OST were 0.59, 0.54, and 0.51, respectively. The inclusion of bone texture parameters in the models improved the prediction of incident rHOA (ROC AUC 0.66 and 0.71 when baseline KL was also included in the model) and JSN (ROC AUC 0.62), but not incident OST (ROC AUC 0.53). Bone texture analysis provides additional information for predicting incident rHOA or THR over 10 years.

Aims The aetiologies of common degenerative spine, hip, and knee pathologies are still not completely understood. Mechanical theories have suggested that those diseases are related to sagittal pelvic morphology and spinopelvic-femoral dynamics. The link between the most widely used parameter for sagittal pelvic morphology, pelvic incidence (PI), and the onset of degenerative lumbar, hip, and knee pathologies has not been studied in a large-scale setting. Methods A total of 421 patients from the Cohort Hip and Cohort Knee (CHECK) database, a population-based observational cohort, with hip and knee complaints < 6 months, aged between 45 and 65 years old, and with lateral lumbar, hip, and knee radiographs available, were included. Sagittal spinopelvic parameters and pathologies (spondylolisthesis and degenerative disc disease (DDD)) were measured at eight-year follow-up and characteristics of hip and knee osteoarthritis (OA) at baseline and eight-year follow-up. Epidemiology of the degenerative disorders and clinical outcome scores (hip and knee pain and Western Ontario and McMaster Universities Osteoarthritis Index) were compared between low PI (< 50°), normal PI (50° to 60°), and high PI (> 60°) using generalized estimating equations. Results Demographic details were not different between the different PI groups. L4 to L5 and L5 to S1 spondylolisthesis were more frequently present in subjects with high PI compared to low PI (L4 to L5, OR 3.717; p = 0.024 vs L5 to S1 OR 7.751; p = 0.001). L5 to S1 DDD occurred more in patients with low PI compared to high PI (OR 1.889; p = 0.010), whereas there were no differences in L4 to L5 DDD among individuals with a different PI. The incidence of hip OA was higher in participants with low PI compared to normal (OR 1.262; p = 0.414) or high PI (OR 1.337; p = 0.274), but not statistically different. The incidence of knee OA was higher in individuals with a high PI compared to low PI (OR 1.620; p = 0.034). Conclusion High PI is a risk factor for development of spondylolisthesis and knee OA. Low pelvic incidence is related to DDD, and may be linked to OA of the hip. Level of Evidence: 1b Cite this article: Bone Joint J 2020;102-B(9):1261–1267.

Facility location allocation is key to success of urban design, mainly in designing transport systems, finding locations for warehouse, fire stations and so on. The problem of determining locations of k facilities so that provides service to n customers, also known as p-median problems, is one of the well-known N Phard problems. Several heuristics have been proposed to solve location allocation problems, each of which has several limitations such as accuracy, time and flexibility, besides their advantages. In this paper, we propose to solve the p-median problems using crowdsourcing and gamification techniques. We present a crowdsourced game, called SolveIt, which employs wisdom and intelligence of the crowd to solve location allocation problems. We have presented a data model for representing p-median problems, designed and implemented the game and tested it using gold standards generated using a genetic algorithm tool. We have also compared the results obtained from SolveIt with the results of a well-known approach called Cooper. The evaluations show the accuracy and superiority of the results obtained from SolveIt players. We have also discussed the limitations and possible applications of the proposed approach.

Diagnosis of ankle impingement is performed primarily by clinical examination, whereas medical imaging is used for severity staging and treatment guidance.The association of impingement symptoms with regional three-dimensional (3D) bone shape variaties visible in medical images has not been systematically explored, nor do we know the type and magnitude of this relation. In this cross-sectional case-control study, we hypothesized that 3D talus bone shape could be used to quantitatively formulate the discriminating shape variations between ankles with impingement from ankles without impingement, and we aimed to characterize and quantify these variations. We used statistical shape modeling (SSM) methods to determine the most prevalent modes of shape variations that discriminate between the impinged and nonimpinged ankles. Results of the compactness and parallel analysis test on the statistical shape model identify 8 prominent shape modes of variations (MoVs) representing approximately 78% of the total 3D variations in the population of shapes, among which two modes captured discriminating features between impinged and nonimpinged ankles (p value of 0.023 and 0.042). Visual inspection confirms that these two shape modes, capturing abnormalities in the anterior and posterior parts of talus, represent the two main bony risk factors in anterior and posterior ankle impingement. In conclusion, in this research using SSM we have identified shape MoVs that were found to correlate significantly with bony ankle impingement. We also illustrated potential guidance from SSMs for surgical planning.

Magnetic resonance Imaging is the gold standard for assessment of soft tissues; however, X‐ray‐based techniques are required for evaluating bone‐related pathologies. This study evaluated the performance of synthetic computed tomography (sCT), a novel MRI‐based bone visualization technique, compared with CT, for the scoring of knee osteoarthritis. sCT images were generated from the 3T T1‐weighted gradient‐echo MR images using a trained machine learning algorithm. Two readers scored the severity of osteoarthritis in tibiofemoral and patellofemoral joints according to OACT, which enables the evaluation of osteoarthritis, from its characteristics of joint space narrowing, osteophytes, cysts and sclerosis in CT (and sCT) images. Cohen's κ was used to assess the interreader agreement for each modality, and intermodality agreement of CT‐ and sCT‐based scores for each reader. We also compared the confidence level of readers for grading CT and sCT images using confidence scores collected during grading. Inter‐reader agreement for tibiofemoral and patellofemoral joints were almost‐perfect for both modalities (κ = 0.83–0.88). The intermodality agreement of osteoarthritis scores between CT and sCT was substantial to almost‐perfect for tibiofemoral (κ = 0.63 and 0.84 for the two readers) and patellofemoral joints (κ = 0.78 and 0.81 for the two readers). The analysis of diagnosis confidence scores showed comparable visual quality of the two modalities, where both are showing acceptable confidence levels for scoring OA. In conclusion, in this single‐center study, sCT and CT were comparable for the scoring of knee OA.