Background Salter innominate osteotomy has been identified as an effective additional surgery for the dysplastic hip. However, because in this procedure, the distal segment of the pelvis is displaced laterally and anteriorly, it may predispose the patient to acetabular retroversion. The degree to which this may be the case, however, remains incompletely characterized. Questions/purposes We asked, in a group of pediatric patients with acetabular dysplasia who underwent Salter osteotomy, whether the operated hip developed (1) acetabular retroversion compared with contralateral unaffected hips; (2) radiographic evidence of osteoarthritis; or (3) worse functional scores. (4) In addition, we asked whether femoral head deformity resulting from aseptic necrosis was a risk factor for acetabular retroversion. Methods Between 1971 and 2001, we performed 213 Salter innominate osteotomies for unilateral pediatric dysplasia, of which 99 hips (47%) in 99 patients were available for review at a mean of 16 years after surgery (range, 12-25 years). Average patient age at surgery was 4 years (range, 2-9 years) and the average age at the most recent followup was 21 years (range, 18-29 years). Acetabular retroversion was diagnosed based on the presence of a positive crossover sign and prominence of the ischial spine sign at the final visit. The center-edge angle, acetabular angle of Sharp, and acetabular index were measured at preoperative and final visits. Contralateral unaffected hips were used as controls, and statistical comparison was made in each patient. Clinical findings, including Harris hip score (HHS) and the anterior impingement sign, were recorded at the final visit. 123Clin Orthop Relat Res (2015) 473:1755-1762 DOI 10.1007 Clinical Orthopaedics and Related Research ® A Publication of The Association of Bone and Joint Surgeons® ischial spine signs. There was no significant difference in HHS between the crossover-positive and crossover-negative patient groups nor in the prominence of the ischial spine-positive and prominence of the ischial spine-negative patient groups (crossover sign, p = 0.68; prominence of the ischial spine sign, p = 0.54). Hips with femoral head deformity (25 of 99 hips [25%]) were more likely to have acetabular retroversion compared with hips without femoral-head deformity (crossover sign, p = 0.029, prominence of the ischial spine sign, p = 0.013). Conclusions Our results suggest that Salter innominate osteotomy does not consistently cause acetabular retroversion in adulthood. We propose that retroversion of the acetabulum is a result of intrinsic development of the pelvis in each patient. A longer-term followup study is needed to determine whether retroverted acetabulum after Slater innominate osteotomy is a true risk factor for early osteoarthritis. Femoral head deformity is a risk factor for subsequent acetabular retroversion. Level of Evidence Level III, therapeutic study.

The aim of this study was to find a new predictive indicator for acetabular growth of developmental dysplasia of the hip. Seventy-three hips that were diagnosed with developmental dysplasia of the hip and treated by conservative reduction were included in our study. In 30 hips with center-edge angle ≤ 10° at age 4, the center-edge of the acetabular limbus angle (CEALA) in the arthrogram was measured. On the basis of the results, CEALA was significantly smaller in the secondary acetabular dysplasia group than in the normal group at maturity. In conclusion, CEALA is a more reliable and accurate predictive indicator for acetabular development than center-edge angle or acetabular index.

The aim of this study was to investigate the ability of the joint fluid glucose level to detect septic arthritis. Thirty joints in 30 patients with suspected septic arthritis were evaluated. When glucose level was less than 40 mg/dl, we performed arthrotomy. Eleven patients had joint fluid glucose levels less than 40 mg/dl. All 11 (100%) had positive joint fluid cultures. Conversely, 19 patients had synovial glucose levels of at least 40 mg/dl. Six (31.6%) of these had positive joint fluid cultures. The remaining 13 were diagnosed with transient synovitis. Patients with joint fluid glucose levels less than 40 mg/dl should be suspected septic arthritis.

Background: A timely diagnosis of developmental dysplasia of the hip (DDH) is important for satisfactory clinical outcomes. Ultrasonography is a useful tool for DDH screening; however, it is technically demanding. We hypothesized that deep learning could assist in the diagnosis of DDH. In this study, several deep-learning models were assessed to diagnose DDH on ultrasonograms. This study aimed to evaluate the accuracy of diagnoses made by artificial intelligence (AI) using deep learning on ultrasound images of DDH. Methods: Infants who were up to 6 months old with suspected DDH were included. DDH diagnosis using ultrasonography was performed according to the Graf classification. Data on 60 infants (64 hips) with DDH and 131 healthy infants (262 hips) obtained from 2016 to 2021 were retrospectively reviewed. For deep learning, a MATLAB deep learning toolbox (MathWorks, Natick, MA, US) was used, and 80% of the images were used as training data, with the rest as validation data. Training images were augmented to increase data variation. In addition, 214 ultrasound images were used as test data to evaluate the AI’s accuracy. Pre-trained models (SqueezeNet, MobileNet_v2, and EfficientNet) were used for transfer learning. Model accuracy was evaluated using a confusion matrix. The region of interest of each model was visualized using gradient-weighted class activation mapping (Grad-CAM), occlusion sensitivity, and image LIME. Results: The best scores for accuracy, precision, recall, and F-measure were all 1.0 in each model. In DDH hips, the region of interest for deep learning models was the area lateral to the femoral head, including the labrum and joint capsule. However, for normal hips, the models highlighted the medial and proximal areas where the lower margin of the os ilium and the normal femoral head exist. Conclusions: Ultrasound imaging with deep learning can assess DDH with high accuracy. This system could be refined for a convenient and accurate diagnosis of DDH. Level of Evidence: Level—Ⅳ.