Aims

The aim of this study was to produce clinical consensus recommendations about the non-surgical treatment of children with Perthes’ disease. The recommendations are intended to support clinical practice in a condition for which there is no robust evidence to guide optimal care.

Aims

The aims of this study were to develop an automatic system capable of calculating four radiological measurements used in the diagnosis and monitoring of cerebral palsy (CP)-related hip disease, and to demonstrate that these measurements are sufficiently accurate to be used in clinical practice.

Aims. The diagnosis of developmental dysplasia of the hip (DDH) is challenging owing to extensive variation in paediatric pelvic anatomy. Artificial intelligence (AI) may represent an effective diagnostic tool for DDH. Here, we aimed to develop an anteroposterior pelvic radiograph deep learning system for diagnosing DDH in children and analyze the feasibility of its application. Methods. In total, 10,219 anteroposterior pelvic radiographs were retrospectively collected from April 2014 to December 2018. Clinicians labelled each radiograph using a uniform standard method. Radiographs were grouped according to age and into ‘dislocation’ (dislocation and subluxation) and ‘non-dislocation’ (normal cases and those with dysplasia of the acetabulum) groups based on clinical diagnosis. The deep learning system was trained and optimized using 9,081 radiographs; 1,138 test radiographs were then used to compare the diagnoses made by deep learning system and clinicians. The accuracy of the deep learning system was determined using a receiver operating characteristic curve, and the consistency of acetabular index measurements was evaluated using Bland-Altman plots. Results. In all, 1,138 patients (242 males; 896 females; mean age 1.5 years (SD 1.79; 0 to 10) were included in this study. The area under the receiver operating characteristic curve, sensitivity, and specificity of the deep learning system for diagnosing hip dislocation were 0.975, 276/289 (95.5%), and 1,978/1,987 (99.5%), respectively. Compared with clinical diagnoses, the Bland-Altman 95% limits of agreement for acetabular index, as determined by the deep learning system from the radiographs of non-dislocated and dislocated hips, were -3.27° - 2.94° and -7.36° - 5.36°, respectively (p < 0.001). Conclusion. The deep learning system was highly consistent, more convenient, and more effective for diagnosing DDH compared with clinician-led diagnoses. Deep learning systems should be considered for analysis of anteroposterior pelvic radiographs when diagnosing DDH. The deep learning system will improve the current artificially complicated screening referral process. Cite this article: Bone Joint J 2020;102-B(11):1574–1581

Aims

The aim of this study was to determine the consensus best practice approach for the investigation and management of children (aged 0 to 15 years) in the UK with musculoskeletal infection (including septic arthritis, osteomyelitis, pyomyositis, tenosynovitis, fasciitis, and discitis). This consensus can then be used to ensure consistent, safe care for children in UK hospitals and those elsewhere with similar healthcare systems.

Aims

The aim of this study was to evaluate the epidemiology and treatment of Perthes’ disease of the hip.

Untreated acetabular dysplasia following treatment for developmental dysplasia of the hip (DDH) leads to early degenerative joint disease. Clinicians must accurately and reliably recognise dysplasia in order to intervene appropriately with secondary acetabular or femoral procedures. This study sought early predictors of residual dysplasia in order to establish empirically-based indications for treatment. DDH treated by closed or open reduction alone was reviewed. Residual hip dysplasia was defined according to the Severin classification at skeletal maturity. Future hip replacement in a subset of these patients was compared with the Severin classification. Serial measurements of acetabular development and subluxation of the femoral head were collected, as were the age at reduction, type of reduction, and Tonnis grade prior to reduction. These variables were used to predict the Severin classification. The mean age at reduction in 72 hips was 16 months (1 to 46). On the final radiograph, 47 hips (65%) were classified as Severin I/II, and 25 as Severin III/IV (35%). At 40 years after reduction, five of 43 hips (21%) had had a total hip replacement (THR). The Severin grade was predictive for THR. Early measurements of the acetabular index (AI) were predictive for Severin grade. For example, an AI of 35° or more at two years after reduction was associated with an 80% probability of becoming a Severin grade III/IV hip. This study links early acetabular remodelling, residual dysplasia at skeletal maturity and the long-term risk of THR. It presents evidence describing the diagnostic value of early predictors of residual dysplasia, and therefore, of the long-term risk of degenerative change

Aims

The aim of this study was to determine the extent to which patient demographics, clinical presentation, and blood parameters vary in Kingella kingae septic arthritis when compared with those of other organisms, and whether this difference needs to be considered when assessing children in whom a diagnosis of septic arthritis is suspected.