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Patellar dislocation is a disorder in the human knee where patella slips out of its expected position. The orthopedic experts need to manually measure the parameters indicating patellar instability from knee scans, which is a laborious and time‐consuming task. An automated method for measuring these parameters can resolve the issue of inter‐observer and intra‐observer variations and significantly lessen the burden of the experts. Therefore, in this work, a two‐step approach has been proposed for the automated measurement of the sulcus angle from knee magnetic resonance (MR) images to assist the experts in the diagnosis of patellar dislocation. Firstly, a variant of U‐Net architecture has been proposed harnessing the capabilities of the residual network to segment the region of interest (ROI) from the axial knee MR images. Secondly, the contour of ROI is used for the detection of key points required to compute sulcus angle. A T2‐weighted knee magnetic resonance imaging dataset of 50 patients obtained from Post Graduate Institute of Medical Education & Research, Chandigarh, has been used. Out of 50 patients, the randomly selected knee MR images of 25 patients have been used to train the segmentation model. The intersection over union and dice similarity coefficient values of 94.71% and 97.28%, respectively, were achieved for the proposed segmentation model. The automated measurement of the sulcus angle resulted in a mean error of 2.52 ± 1.52° when tested on another set of axial knee MR images of 25 patients. The proposed framework produced results comparable to the manual expert measurements. [ABSTRACT FROM AUTHOR]

The purpose of present review paper is to introduce the reader to key directions of manual, semi-automatic and automatic knee osteoarthritis (OA) severity classification from plain radiographs. This is a narrative review article in which we have described recent developments in severity evaluation of knee OA from X-ray images. We have primarily focussed on automatic analysis and have reviewed articles in which machine learning, transfer learning, active learning, etc. have been employed on X-ray images to access and classify the severity of knee OA. All original research articles on OA detection and classification using X-ray images published in English were searched on PubMed database, Google Scholar, RSNA radiology databases in year 2019. The search terms of "knee Osteoarthritis" were combined with search terms "Machine Learning", 'severity" and "X-ray". The initial search on various publication databases revealed a total of 743 results, out of which only 26 articles were considered relevant to radiographic knee OA severity analysis. The majority of the articles were based on automatic analysis. Manual segmentation based articles were least in numbers. Computer aided methods to diagnose knee OA are great tools to detect OA at ealry stages. Advancements in Human Computer Interface systems have led the researchers to bridge the gap between machine learning algorithms and expert healthcare professionals to provide better and timely treatment options to the knee OA affected patients. [ABSTRACT FROM AUTHOR]

Purpose: Cadaveric studies have shown that deficiency of the posterior horn of the medial meniscus (PHMM) increases strain on the anterior cruciate ligament (ACL) graft. However, its influence on the clinical and radiological outcome after ACL reconstruction is less discussed and hence evaluated in this study. Materials and Methods: This study included 77 cases of ACL reconstruction with a minimum 18-month follow-up. Of the 77 cases, 41 patients with intact menisci were compared clinically and radiologically with 36 patients with an injury to the PHMM that required various grades of meniscectomy. The knees were evaluated using subjective International Knee Documentation Committee (IKDC) score and Orthopadische Arbeitsgruppe Knie (OAK) score. Results: Cases with intact menisci showed better stability (p=0.004) at an average of 44.51 months after surgery. No significant differences were noted in the overall OAK score, subjective IKDC score, and functional OAK score (p=0.082, p=0.526, and p=0.363, respectively). The incidence of radiological osteoarthrosis was significantly higher in the posterior horn deficient knees (p=0.022). Conclusions: The tendency toward relatively higher objective instability and increased incidence of osteoarthrosis in the group with absent posterior horn reinforces its importance as a secondary stabiliser of the knee. [ABSTRACT FROM AUTHOR]

Purpose: To identify and quantify the presence of extra-articular tibia vara that might influence the mechanical axis alignment after total knee arthroplasty (TKA). Materials and Methods: A total of 48 TKAs in 30 osteoarthritic Indian patients were prospectively evaluated. The hip-knee-ankle angle (HKA), joint line convergence angle, and varus angulation at the femur and tibia were measured from the preoperative and postoperative standing hip-to-ankle radiographs. Four different methods were used to measure the varus angulation at the tibia: metaphyseo-diaphyseal angle (MDA), the angle between the anatomical axis and mechanical axis of the tibia, the angle between the proximal third and distal third of tibia and the angle between the proximal half and distal half of tibia. Results: Extra-articular tibia vara quantified using MDA had the most positive correlation with HKA. Receiver operating characteristic plotting showed that MDA of >4º predicts abnormal postoperative HKA. Twenty-eight out of 48 knees had MDA of >4º, and 78.6% of these had postoperative HKA under-correction and 21.4% had less than ideal tibial component position. Conclusions: A significant inherent extra-articular varus angulation best measured using MDA exists in the proximal tibia in osteoarthritic Indian patients undergoing TKA. MDA of >4º is associated with abnormal postoperative HKA. Computer navigation may be useful for achieving ideal correction in such cases. [ABSTRACT FROM AUTHOR]