Aims. The purpose of this study was to develop a convolutional neural network (CNN) for fracture
Artificial intelligence (AI) is, in essence, the concept of ‘computer thinking’, encompassing methods that train computers to perform and learn from executing certain tasks, called machine learning, and methods to build intricate computer models that both learn and adapt, called complex neural networks. Computer vision is a function of AI by which machine learning and complex neural networks can be applied to enable computers to capture, analyze, and interpret information from clinical images and visual inputs. This annotation summarizes key considerations and future perspectives concerning computer vision, questioning the need for this technology (the ‘why’), the current applications (the ‘what’), and the approach to unlocking its full potential (the ‘how’). Cite this article:
The aim of this study was to develop and evaluate a deep learning-based model for classification of hip fractures to enhance diagnostic accuracy. A retrospective study used 5,168 hip anteroposterior radiographs, with 4,493 radiographs from two institutes (internal dataset) for training and 675 radiographs from another institute for validation. A convolutional neural network (CNN)-based classification model was trained on four types of hip fractures (Displaced, Valgus-impacted, Stable, and Unstable), using DAMO-YOLO for data processing and augmentation. The model’s accuracy, sensitivity, specificity, Intersection over Union (IoU), and Dice coefficient were evaluated. Orthopaedic surgeons’ diagnoses served as the reference standard, with comparisons made before and after artificial intelligence assistance.Aims
Methods
Our aim was to determine if the
We studied the
Our aim was to determine the clinical value of MRI and CT arthrography in predicting the presence of loose bodies in the elbow. A series of 26 patients with mechanical symptoms in the elbow had plain radiography, MRI and CT arthrography, followed by routine arthroscopy of the elbow. The location and number of loose bodies determined by MRI and CT arthrography were recorded. Pre-operative plain radiography, MRI and CT arthrography were compared with arthroscopy. Both MRI and CT arthrography had excellent sensitivity (92% to 100%) but low to moderate specificity (15% to 77%) in identifying posteriorly-based loose bodies. Neither MRI nor CT arthrography was consistently sensitive (46% to 91%) or specific (13% to 73%) in predicting the presence or absence of loose bodies anteriorly. The overall sensitivity for the
Visualisation of periacetabular osteolysis by standard anteroposterior (AP) radiographs underestimates the extent of bone loss around a metal-backed acetabular component. We have assessed the effectiveness of standard radiological views in depicting periacetabular osteolysis, and recommend additional projections which make these lesions more visible. This was accomplished using a computerised simulation of radiological views and a radiological analysis of simulated defects placed at regular intervals around the perimeter of a cadaver acetabulum. The AP view alone showed only 38% of the defects over all of the surface of the cup and failed to depict a 3 mm lesion over 83% of the cup. When combined with the AP view, additional 45° obturator-oblique and iliac-oblique projections increased the depiction, showing 81% of the defects. The addition of the 60° obturator-oblique view further improved the visualisation of posterior defects, increasing the rate of
The aim of this study was to evaluate the ability of a machine-learning algorithm to diagnose prosthetic loosening from preoperative radiographs and to investigate the inputs that might improve its performance. A group of 697 patients underwent a first-time revision of a total hip (THA) or total knee arthroplasty (TKA) at our institution between 2012 and 2018. Preoperative anteroposterior (AP) and lateral radiographs, and historical and comorbidity information were collected from their electronic records. Each patient was defined as having loose or fixed components based on the operation notes. We trained a series of convolutional neural network (CNN) models to predict a diagnosis of loosening at the time of surgery from the preoperative radiographs. We then added historical data about the patients to the best performing model to create a final model and tested it on an independent dataset.Aims
Methods
The aim of this study was to establish the incidence of developmental dysplasia of the hip (DDH) diagnosed after one-year of age in England, stratified by age, gender, year, and region of diagnosis. A descriptive observational study was performed by linking primary and secondary care information from two independent national databases of routinely collected data: the United Kingdom Clinical Practice Research Datalink and Hospital Episode Statistics. The study examined all children from 1 January 1990 to 1 January 2016 who had a new first diagnostic code for DDH aged between one and eight years old.Aims
Patients and Methods
We have examined the accuracy of 143 consecutive ultrasound scans of patients who subsequently underwent shoulder arthroscopy for rotator-cuff disease. All the scans and subsequent surgery were performed by an orthopaedic surgeon using a portable ultrasound scanner in a one-stop clinic. There were 78 full thickness tears which we confirmed by surgery or MRI. Three moderate-size tears were assessed as partial-thickness at ultrasound scan (false negative) giving a sensitivity of 96.2%. One partially torn and two intact cuffs were over-diagnosed as small full-thickness tears by ultrasound scan (false positive) giving a specificity of 95.4%. This gave a positive predictive value of 96.2% and a negative predictive value of 95.4%. Estimation of tear size was more accurate for large and massive tears at 96.5% than for moderate (88.8%) and small tears (91.6%). These results are equivalent to those obtained by several studies undertaken by experienced radiologists. We conclude that ultrasound imaging of the shoulder performed by a sufficiently-trained orthopaedic surgeon is a reliable time-saving practice to identify rotator-cuff integrity.
We used MRI to examine the hips of 32 asymptomatic patients at 9 to 21 months after renal transplantation covered by high-dose corticosteroids. Five hips in three patients showed changes which indicate avascular necrosis, although radiographs, CT scans and isotope scans were normal. These patients had repeat MRI scans after another two years and three years. One patient with bilateral MRI changes developed symptoms and abnormal radiographs and CT and isotope scans in one hip nine months after the abnormal MRI. Intraosseous pressure was found to be raised in both hips, and core biopsies revealed necrotic bone on both sides. The other three hips have remained asymptomatic with unchanged MRI appearances three years after the initial MRI. It seems that idiopathic avascular necrosis does not always progress to bone collapse in the medium term.
We have assessed the effect of a variety of implants commonly used in fracture fixation and joint replacement on the activation of metal detectors at airport security gates. A volunteer with metal implants strapped on and patients with implants in situ walked through the device. Implants used in fixation do not activate it, except for Richards cannulated screws. An Austin-Moore prosthesis does set off the detector, but a single joint replacement does not. Three or four joint replacements activate the alarm and patients with these implants should be warned of this possibility.