Functional alignment (FA) in total knee arthroplasty (TKA) aims to achieve balanced gaps by adjusting implant positioning while minimizing changes to constitutional joint line obliquity (JLO). Although FA uses kinematic alignment (KA) as a starting point, the final implant positions can vary significantly between these two approaches. This study used the Coronal Plane Alignment of the Knee (CPAK) classification to compare differences between KA and final FA positions. A retrospective analysis compared pre-resection and post-implantation alignments in 2,116 robotic-assisted FA TKAs. The lateral distal femoral angle (LDFA) and medial proximal tibial angle (MPTA) were measured to determine the arithmetic hip-knee-ankle angle (aHKA = MPTA – LDFA), JLO (JLO = MPTA + LDFA), and CPAK type. The primary outcome was the proportion of knees that varied ≤ 2° for aHKA and ≤ 3° for JLO from their KA to FA positions, and direction and magnitude of those changes per CPAK phenotype. Secondary outcomes included proportion of knees that maintained their CPAK phenotype, and differences between sexes.Aims
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Addressing bone defects is a complex medical challenge that involves dealing with various skeletal conditions, including fractures, osteoporosis (OP), bone tumours, and bone infection defects. Despite the availability of multiple conventional treatments for these skeletal conditions, numerous limitations and unresolved issues persist. As a solution, advancements in biomedical materials have recently resulted in novel therapeutic concepts. As an emerging biomaterial for bone defect treatment, graphene oxide (GO) in particular has gained substantial attention from researchers due to its potential applications and prospects. In other words, GO scaffolds have demonstrated remarkable potential for bone defect treatment. Furthermore, GO-loaded biomaterials can promote osteoblast adhesion, proliferation, and differentiation while stimulating bone matrix deposition and formation. Given their favourable biocompatibility and osteoinductive capabilities, these materials offer a novel therapeutic avenue for bone tissue regeneration and repair. This comprehensive review systematically outlines GO scaffolds’ diverse roles and potential applications in bone defect treatment. Cite this article:
Periprosthetic joint infection (PJI) represents a complex challenge in orthopaedic surgery associated with substantial morbidity and healthcare expenditures. The debridement, antibiotics, and implant retention (DAIR) protocol is a viable treatment, offering several advantages over exchange arthroplasty. With the evolution of treatment strategies, considerable efforts have been directed towards enhancing the efficacy of DAIR, including the development of a phased debridement protocol for acute PJI management. This article provides an in-depth analysis of DAIR, presenting the outcomes of single-stage, two-stage, and repeated DAIR procedures. It delves into the challenges faced, including patient heterogeneity, pathogen identification, variability in surgical techniques, and antibiotics selection. Moreover, critical factors that influence the decision-making process between single- and two-stage DAIR protocols are addressed, including team composition, timing of the intervention, antibiotic regimens, and both anatomical and implant-related considerations. By providing a comprehensive overview of DAIR protocols and their clinical implications, this annotation aims to elucidate the advancements, challenges, and potential future directions in the application of DAIR for PJI management. It is intended to equip clinicians with the insights required to effectively navigate the complexities of implementing DAIR strategies, thereby facilitating informed decision-making for optimizing patient outcomes. Cite this article:
As advancements in total knee arthroplasty progress at an exciting pace, two areas are of special interest, as they directly impact implant design and surgical decision making. Knee morphometry considers the three-dimensional shape of the articulating surfaces within the knee joint, and knee phenotyping provides the ability to categorize alignment into practical groupings that can be used in both clinical and research settings. This annotation discusses the details of these concepts, and the ways in which they are helping us better understand the individual subtleties of each patient’s knee. Cite this article:
Pain is the most frequent complaint associated with osteonecrosis of the femoral head (ONFH), but the factors contributing to such pain are poorly understood. This study explored diverse demographic, clinical, radiological, psychological, and neurophysiological factors for their potential contribution to pain in patients with ONFH. This cross-sectional study was carried out according to the “STrengthening the Reporting of OBservational studies in Epidemiology” statement. Data on 19 variables were collected at a single timepoint from 250 patients with ONFH who were treated at our medical centre between July and December 2023 using validated instruments or, in the case of hip pain, a numerical rating scale. Factors associated with pain severity were identified using hierarchical multifactor linear regression.Aims
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Introduction. With advances in artificial intelligence, the use of computer-aided detection and diagnosis in clinical imaging is gaining traction. Typically, very large datasets are required to train machine-learning models, potentially limiting use of this technology when only small datasets are available. This study investigated whether pretraining of fracture detection models on large, existing datasets could improve the performance of the model when locating and classifying wrist fractures in a small X-ray image dataset. This concept is termed “transfer learning”. Method. Firstly, three detection models, namely, the faster region-based convolutional neural network (faster R-CNN), you only look once version eight (YOLOv8), and RetinaNet, were pretrained using the large, freely available dataset, common objects in context (COCO) (330000 images). Secondly, these models were pretrained using an open-source wrist X-ray dataset called “Graz Paediatric Wrist Digital X-rays” (GRAZPEDWRI-DX) on a (1) fracture detection dataset (20327 images) and (2) fracture location and classification dataset (14390 images). An orthopaedic surgeon classified the small available dataset of 776 distal radius X-rays (Arbeidsgmeischaft für Osteosynthesefragen Foundation / Orthopaedic Trauma Association; AO/OTA), on which the models were tested. Result. Detection models without pre-training on the large datasets were the least precise when tested on the small distal radius dataset. The model with the best accuracy to detect and classify wrist fractures was the YOLOv8 model pretrained on the GRAZPEDWRI-DX fracture detection dataset (mean average
Introduction. Knee Osteoarthritis (KOA) is a prevalent joint disease requiring accurate diagnosis and prompt management. The condition occurs due to cartilage deterioration and bone remodeling. Ultrasonography has emerged as a promising modality for diagnosing KOA. Medial meniscus extrusion (MME), characterized by displacement of medial meniscus beyond the joint line has been recognized as a significant marker of KOA progression. This study aimed to explore potentials Ultrasound findings in timely detection of MME and compare it to magnetic resonance imaging (MRI) as a reference standard. Method. A comprehensive literature search was performed in 4 databases from inception to May 1 2024. Two independent reviewers, initiated screening protocols and selected the articles based on inclusion and exclusion criteria and then extracted the data. Meta-analysis was conducted using R 4.3.2 packages mada and metafor. Result. A total of 2500 articles from 4 databases was retrieved; however, following the application of inclusion and exclusion criteria 23 articles were finally extracted. These studies collectively encompassed a total of 777 patients with mean age of 53.2±7.4. The mean BMI calculated for patients was 28.31 ± 2.45. All patients underwent non-weight bearing knee ultrasonography in supine position with 0° flexion. The reported medial meniscus extrusion was 2.58 mm for articles using MRI and 2.65 mm for those using Ultrasound (MD: 0.05 ± 0.12, P= 0.65, I. 2. : 54%). Our meta-analysis revealed insignificant difference between US and MRI. (SMD: 0.03, 95% CI: -0.18 _0.23, P= 0.77, I. 2. : 56%) Meta analysis for diagnostic accuracy measures yielded a pooled sensitivity and specificity of 90.8% and 77% (95% CI: 84.2% – 94.8%, 35.5% – 95.3%, respectively, I. 2. : 44%). Conclusion. Our results indicate a close alignment in the accuracy of measurements obtained using Ultrasound modality. The narrow range suggests a minimal discrepancy in MME values between MRI and ultrasound, highlighting their comparable
Understanding spinopelvic mechanics is important for the success of total hip arthroplasty (THA). Despite significant advancements in appreciating spinopelvic balance, numerous challenges remain. It is crucial to recognize the individual variability and postoperative changes in spinopelvic parameters and their consequential impact on prosthetic component positioning to mitigate the risk of dislocation and enhance postoperative outcomes. This review describes the integration of advanced diagnostic approaches, enhanced technology, implant considerations, and surgical planning, all tailored to the unique anatomy and biomechanics of each patient. It underscores the importance of accurately predicting postoperative spinopelvic mechanics, selecting suitable imaging techniques, establishing a consistent nomenclature for spinopelvic stiffness, and considering implant-specific strategies. Furthermore, it highlights the potential of artificial intelligence to personalize care. Cite this article:
The surgical target for optimal implant positioning in robotic-assisted total knee arthroplasty remains the subject of ongoing discussion. One of the proposed targets is to recreate the knee’s functional behaviour as per its pre-diseased state. The aim of this study was to optimize implant positioning, starting from mechanical alignment (MA), toward restoring the pre-diseased status, including ligament strain and kinematic patterns, in a patient population. We used an active appearance model-based approach to segment the preoperative CT of 21 osteoarthritic patients, which identified the osteophyte-free surfaces and estimated cartilage from the segmented bones; these geometries were used to construct patient-specific musculoskeletal models of the pre-diseased knee. Subsequently, implantations were simulated using the MA method, and a previously developed optimization technique was employed to find the optimal implant position that minimized the root mean square deviation between pre-diseased and postoperative ligament strains and kinematics.Aims
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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
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Treatment of high-grade limb bone sarcoma that invades a joint requires en bloc extra-articular excision. MRI can demonstrate joint invasion but is frequently inconclusive, and its predictive value is unknown. We evaluated the diagnostic accuracy of direct and indirect radiological signs of intra-articular tumour extension and the performance characteristics of MRI findings of intra-articular tumour extension. We performed a retrospective case-control study of patients who underwent extra-articular excision for sarcoma of the knee, hip, or shoulder from 1 June 2000 to 1 November 2020. Radiologists blinded to the pathology results evaluated preoperative MRI for three direct signs of joint invasion (capsular disruption, cortical breach, cartilage invasion) and indirect signs (e.g. joint effusion, synovial thickening). The discriminatory ability of MRI to detect intra-articular tumour extension was determined by receiver operating characteristic analysis.Aims
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This study aimed to investigate the optimal sagittal positioning of the uncemented femoral component in total knee arthroplasty to minimize the risk of aseptic loosening and periprosthetic fracture. Ten different sagittal placements of the femoral component, ranging from -5 mm (causing anterior notch) to +4 mm (causing anterior gap), were analyzed using finite element analysis. Both gait and squat loading conditions were simulated, and Von Mises stress and interface micromotion were evaluated to assess fracture and loosening risk.Aims
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This study aims to describe a new method that may be used as a supplement to evaluate humeral rotational alignment during intramedullary nail (IMN) insertion using the profile of the perpendicular peak of the greater tuberosity and its relation to the transepicondylar axis. We called this angle the greater tuberosity version angle (GTVA). This study analyzed 506 cadaveric humeri of adult patients. All humeri were CT scanned using 0.625 × 0.625 × 0.625 mm cubic voxels. The images acquired were used to generate 3D surface models of the humerus. Next, 3D landmarks were automatically calculated on each 3D bone using custom-written C++ software. The anatomical landmarks analyzed were the transepicondylar axis, the humerus anatomical axis, and the peak of the perpendicular axis of the greater tuberosity. Lastly, the angle between the transepicondylar axis and the greater tuberosity axis was calculated and defined as the GTVA.Aims
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This study aimed to define the histopathology of degenerated humeral head cartilage and synovial inflammation of the glenohumeral joint in patients with omarthrosis (OmA) and cuff tear arthropathy (CTA). Additionally, the potential of immunohistochemical tissue biomarkers in reflecting the degeneration status of humeral head cartilage was evaluated. Specimens of the humeral head and synovial tissue from 12 patients with OmA, seven patients with CTA, and four body donors were processed histologically for examination using different histopathological scores. Osteochondral sections were immunohistochemically stained for collagen type I, collagen type II, collagen neoepitope C1,2C, collagen type X, and osteocalcin, prior to semiquantitative analysis. Matrix metalloproteinase (MMP)-1, MMP-3, and MMP-13 levels were analyzed in synovial fluid using enzyme-linked immunosorbent assay (ELISA).Aims
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The sensitivity and specificity of electrodiagnostic parameters in diagnosing carpal tunnel syndrome (CTS) have been reported differently, and this study aims to address this gap. This case-control study was conducted on 57 cases with CTS and 58 controls without complaints, such as pain or paresthesia on the median nerve. The main assessed electrodiagnostic parameters were terminal latency index (TLI), residual latency (RL), median ulnar F-wave latency difference (FdifMU), and median sensory latency-ulnar motor latency difference (MSUMLD).Aims
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The subject of noise in the operating theatre was recognized as early as 1972 and has been compared to noise levels on a busy highway. While noise-induced hearing loss in orthopaedic surgery specifically has been recognized as early as the 1990s, it remains poorly studied. As a result, there has been renewed focus in this occupational hazard. Noise level is typically measured in decibels (dB), whereas noise adjusted for human perception uses A-weighted sound levels and is expressed in dBA. Mean operating theatre noise levels range between 51 and 75 dBA, with peak levels between 80 and 119 dBA. The greatest sources of noise emanate from powered surgical instruments, which can exceed levels as high as 140 dBA. Newer technology, such as robotic-assisted systems, contribute a potential new source of noise. This article is a narrative review of the deleterious effects of prolonged noise exposure, including noise-induced hearing loss in the operating theatre team and the patient, intraoperative miscommunication, and increased cognitive load and stress, all of which impact the surgical team’s overall performance. Interventions to mitigate the effects of noise exposure include the use of quieter surgical equipment, the implementation of sound-absorbing personal protective equipment, or changes in communication protocols. Future research endeavours should use advanced research methods and embrace technological innovations to proactively mitigate the effects of operating theatre noise. Cite this article:
This study aimed to quantify the shoulder kinematics during an apprehension-relocation test in patients with anterior shoulder instability (ASI) and glenoid bone loss using the radiostereometric analysis (RSA) method. Kinematics were compared with the patient’s contralateral healthy shoulder. A total of 20 patients with ASI and > 10% glenoid bone loss and a healthy contralateral shoulder were included. RSA imaging of the patient’s shoulders was performed during a repeated apprehension-relocation test. Bone volume models were generated from CT scans, marked with anatomical coordinate systems, and aligned with the digitally reconstructed bone projections on the RSA images. The glenohumeral joint (GHJ) kinematics were evaluated in the anteroposterior and superoinferior direction of: the humeral head centre location relative to the glenoid centre; and the humeral head contact point location on the glenoid.Aims
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To report the development of the technique for minimally invasive lumbar decompression using robotic-assisted navigation. Robotic planning software was used to map out bone removal for a laminar decompression after registration of CT scan images of one cadaveric specimen. A specialized acorn-shaped bone removal robotic drill was used to complete a robotic lumbar laminectomy. Post-procedure advanced imaging was obtained to compare actual bony decompression to the surgical plan. After confirming accuracy of the technique, a minimally invasive robotic-assisted laminectomy was performed on one 72-year-old female patient with lumbar spinal stenosis. Postoperative advanced imaging was obtained to confirm the decompression.Aims
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Despite the vast quantities of published artificial intelligence (AI) algorithms that target trauma and orthopaedic applications, very few progress to inform clinical practice. One key reason for this is the lack of a clear pathway from development to deployment. In order to assist with this process, we have developed the Clinical Practice Integration of Artificial Intelligence (CPI-AI) framework – a five-stage approach to the clinical practice adoption of AI in the setting of trauma and orthopaedics, based on the IDEAL principles ( Cite this article:
