Pelvic obliquity is a common finding in adolescents
with cerebral palsy, however, there is little agreement on its measurement
or relationship with hip development at different gross motor function
classification system (GMFCS) levels. . The purpose of this investigation was to study these issues in
a large, population-based cohort of adolescents with cerebral palsy
at transition into adult services. . The cohort were a subset of a three year birth cohort (n = 98,
65M: 33F, with a mean age of 18.8 years (14.8 to 23.63) at their
last radiological review) with the common features of a migration
percentage greater than 30% and a history of adductor release surgery. . Different radiological methods of measuring pelvic obliquity
were investigated in 40 patients and the angle between the acetabular
tear drops (ITDL) and the horizontal reference frame of the radiograph
was found to be reliable, with good face validity. This was selected
for further study in all 98 patients. . The median pelvic obliquity was 4° (interquartile range 2° to
8°). There was a strong correlation between
We have tested the reliability of a recently reported classification system of
There is no consensus regarding optimum timing and frequency of ultrasound (US) for monitoring response to Pavlik harness (PH) treatment in developmental dysplasia of the hip (DDH). The purpose of our study was to determine if a limited-frequency hip US assessment had an adverse effect on treatment outcomes compared to traditional comprehensive US monitoring. This study was a single-centre noninferiority randomized controlled trial. Infants aged under six months whose hips were reduced and centred in the harness at initiation of treatment (stable dysplastic or subluxable), or initially decentred (subluxated or dislocated) but reduced and centred within four weeks of PH treatment, were randomized to our current standard US monitoring protocol (every clinic visit) or to a limited-frequency US protocol (US only at end of treatment). Groups were compared based on α angle and femoral head coverage at the end of PH treatment, acetabular indices, and International Hip Dysplasia Institute (IHDI) grade on one-year follow-up radiographs.Aims
Methods
The most important complication of treatment of developmental dysplasia of the hip (DDH) is avascular necrosis (AVN) of the femoral head, which can result in proximal femoral growth disturbances leading to pain, dysfunction, and eventually to early onset osteoarthritis. In this study, we aimed to identify morphological variants in hip joint development that are predictive of a poor outcome. We retrospectively reviewed all patients who developed AVN after DDH treatment, either by closed and/or open reduction, at a single institution between 1984 and 2007 with a minimal follow-up of eight years. Standard pelvis radiographs obtained at ages one, two, three, five, and eight years, and at latest follow-up were retrieved. The Bucholz-Ogden classification was used to determine the type of AVN on all radiographs. Poor outcome was defined by Severin classification grade 3 or above on the latest follow-up radiographs and/or the need for secondary surgery. With statistical shape modelling, we identified the different shape variants of the hip at each age. Logistic regression analysis was used to associate the different modes or shape variants with poor outcome.Aims
Methods
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. 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.Aims
Methods
