Periprosthetic joint infection (PJI) is a difficult complication requiring a comprehensive eradication protocol. Cure rates have essentially stalled in the last two decades, using methods of antimicrobial cement
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. 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.Aims
Methods
The objective of this study is to assess the use of ultrasound (US) as a radiation-free imaging modality to reconstruct 3D anatomy of the knee for use in preoperative templating in knee arthroplasty. Using an US system, which is fitted with an electromagnetic (EM) tracker that is integrated into the US probe, allows 3D tracking of the probe, femur, and tibia. The raw US radiofrequency (RF) signals are acquired and, using real-time signal processing, bone boundaries are extracted. Bone boundaries and the tracking information are fused in a 3D point cloud for the femur and tibia. Using a statistical shaping model, the patient-specific surface is reconstructed by optimizing bone geometry to match the point clouds. An accuracy analysis was conducted for 17 cadavers by comparing the 3D US models with those created using CT. US scans from 15 users were compared in order to examine the effect of operator variability on the output.Aims
Methods
A comprehensive classification for coronal lower limb alignment with predictive capabilities for knee balance would be beneficial in total knee arthroplasty (TKA). This paper describes the Coronal Plane Alignment of the Knee (CPAK) classification and examines its utility in preoperative soft tissue balance prediction, comparing kinematic alignment (KA) to mechanical alignment (MA). A radiological analysis of 500 healthy and 500 osteoarthritic (OA) knees was used to assess the applicability of the CPAK classification. CPAK comprises nine phenotypes based on the arithmetic HKA (aHKA) that estimates constitutional limb alignment and joint line obliquity (JLO). Intraoperative balance was compared within each phenotype in a cohort of 138 computer-assisted TKAs randomized to KA or MA. Primary outcomes included descriptive analyses of healthy and OA groups per CPAK type, and comparison of balance at 10° of flexion within each type. Secondary outcomes assessed balance at 45° and 90° and bone recuts required to achieve final knee balance within each CPAK type.Aims
Methods
We compared the clinical outcomes of curved intertrochanteric varus osteotomy (CVO) with bone impaction grafting (BIG) with CVO alone for the treatment of osteonecrosis of the femoral head (ONFH). This retrospective comparative study included 81 patients with ONFH; 37 patients (40 hips) underwent CVO with BIG (BIG group) and 44 patients (47 hips) underwent CVO alone (CVO group). Patients in the BIG group were followed-up for a mean of 12.2 years (10.0 to 16.5). Patients in the CVO group were followed-up for a mean of 14.5 years (10.0 to 21.0). Assessment parameters included the Harris Hip Score (HHS), Oxford Hip Score (OHS), Japanese Orthopaedic Association Hip-Disease Evaluation Questionnaire (JHEQ), complication rates, and survival rates, with conversion to total hip arthroplasty (THA) and radiological failure as the endpoints.Aims
Methods
Periprosthetic joint infection (PJI) is one of
the most feared and challenging complications following total knee arthroplasty.
We provide a detailed description of our current understanding regarding
the management of PJI of the knee, including diagnostic aids,
pre-operative planning, surgical treatment, and outcome. Cite this article:
