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 prospective cohort study was to evaluate the effectiveness of the neonatal hip instability screening programme.

The aim of this retrospective multicentre study was to report the continued occurrence of compartment syndrome secondary to paediatric supracondylar humeral fractures in the period 1995 to 2005. The inclusion criteria were children with a closed, low-energy supracondylar fracture with no associated fractures or vascular compromise, who subsequently developed compartment syndrome. There were 11 patients (seven girls and four boys) identified from eight hospitals in three countries. Ten patients with severe elbow swelling documented at presentation had a mean delay before surgery of 22 hours (6 to 64). One patient without severe swelling documented at presentation suffered arterial entrapment following reduction, with a subsequent compartment syndrome requiring fasciotomy 25 hours after the index procedure.

This series is noteworthy, as all patients had low-energy injuries and presented with an intact radial pulse. Significant swelling at presentation and delay in fracture reduction may be important warning signs for the development of a compartment syndrome in children with supracondylar fractures of the humerus.

We examined 204 children (137 boys and 67 girls) aged 12 years and under with septic arthritis. Their mean age was 31.1 months (1 to 144; SD 41.6). The most common joints affected were the knees and shoulders. Joints in the upper limb were affected more often in younger children and in the lower limb in those who were older. The mean age for an infection was 12 months in the shoulder and 73 months in the hip. The most common organisms cultured were species of Salmonella.