AI-based detection and classification of distal radius fractures using low-effort data labeling: evaluation of applicability and effect of training set size.

Objectives: To evaluate the performance of a deep convolutional neural network (DCNN) in detecting and classifying distal radius fractures, metal, and cast on radiographs using labels based on radiology reports. The secondary aim was to evaluate the effect of the training set size on the algorithm's performance.

Methods: A total of 15,775 frontal and lateral radiographs, corresponding radiology reports, and a ResNet18 DCNN were used.

In vivo 3D tomography of the lumbar spine using a twin robotic X-ray system: quantitative and qualitative evaluation of the lumbar neural foramina in supine and upright position.

Objectives: Supine lumbar spine examinations underestimate body weight effects on neuroforaminal size. Therefore, our purpose was to evaluate size changes of the lumbar neuroforamina using supine and upright 3D tomography and to initially assess image quality compared with computed tomography (CT).

Methods: The lumbar spines were prospectively scanned in 48 patients in upright (3D tomographic twin robotic X-ray) and supine (30 with 3D tomography, 18 with CT) position.

Timed function tests, motor function measure, and quantitative thigh muscle MRI in ambulant children with Duchenne muscular dystrophy: A cross-sectional analysis.

The development of new therapeutic agents for the treatment of Duchenne muscular dystrophy has put a focus on defining outcome measures most sensitive to capture treatment effects. This cross-sectional analysis investigates the relation between validated clinical assessments such as the 6-minute walk test, motor function measure and quantitative muscle MRI of thigh muscles in ambulant Duchenne muscular dystrophy patients, aged 6.5 to 10.