The purpose of this study was to develop a convolutional neural network (CNN) for fracture detection, classification, and identification of greater tuberosity displacement ≥ 1 cm, neck-shaft angle (NSA) ≤ 100°, shaft translation, and articular fracture involvement, on plain radiographs. The CNN was trained and tested on radiographs sourced from 11 hospitals in Australia and externally validated on radiographs from the Netherlands. Each radiograph was paired with corresponding CT scans to serve as the reference standard based on dual independent evaluation by trained researchers and attending orthopaedic surgeons. Presence of a fracture, classification (non- to minimally displaced; two-part, multipart, and glenohumeral dislocation), and four characteristics were determined on 2D and 3D CT scans and subsequently allocated to each series of radiographs. Fracture characteristics included greater tuberosity displacement ≥ 1 cm, NSA ≤ 100°, shaft translation (0% to < 75%, 75% to 95%, > 95%), and the extent of articular involvement (0% to < 15%, 15% to 35%, or > 35%).Aims
Methods
We undertook a trial on 60 patients with AO 31A2 fractures of the hip who were randomised after stabilisation of the fracture into two equal groups, one of which received post-operative treatment using a non-invasive interactive neurostimulation device and the other with a sham device. All other aspects of their rehabilitation were the same. The treatment was continued for ten days after operation. Outcome measurements included the use of a visual analogue scale for pain, the brief pain inventory and Ketorolac for post-operative control of pain, and an overall assessment of outcome by the surgeon. There were significantly better results for the patients receiving treatment by active electrical stimulation (repeated measures analysis of variance, p <
0.001). The findings of this pilot trial justify a larger study to determine if these results are more generally applicable.
We hypothesised that the anterior and posterior
walls of the body of the first sacral vertebra could be visualised with
two different angles of inlet view, owing to the conical shape of
the sacrum. Six dry male cadavers with complete pelvic rings and
eight dry sacrums with K-wires were used to study the effect of
canting (angling the C-arm) the fluoroscope towards the head in
5° increments from 10° to 55°. Fluoroscopic images were taken in
each position. Anterior and posterior angles of inclination were
measured between the upper sacrum and the vertical line on the lateral
view. Three authors separately selected the clearest image for overlapping
anterior cortices and the upper sacral canal in the cadaveric models.
The dry bone and K-wire models were scored by the authors, being
sure to check whether the
K-wire was in or out. In the dry bone models the mean score of the relevant inlet position
of the anterior or posterior inclination was 8.875 (standard deviation
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