AI Medical Compendium Journal:
Skeletal radiology

Showing 21 to 30 of 63 articles

Imaging of early-stage osteoarthritis: the needs and challenges for diagnosis and classification.

Skeletal radiology
In an effort to boost the development of new management strategies for OA, there is currently a shift in focus towards the diagnosis and treatment of early-stage OA. It is important to distinguish diagnosis from classification of early-stage OA. Diag...

A comparison of ChatGPT-generated articles with human-written articles.

Skeletal radiology
OBJECTIVE: ChatGPT (Generative Pre-trained Transformer) is an artificial intelligence language tool developed by OpenAI that utilises machine learning algorithms to generate text that closely mimics human language. It has recently taken the internet ...

Development and validation of a deep learning model using convolutional neural networks to identify femoral internal fixation device in radiographs.

Skeletal radiology
OBJECTIVE: The purpose of this study is to develop and validate a deep convolutional neural network (DCNN) model to automatically identify the manufacturer and model of hip internal fixation devices from anteroposterior (AP) radiographs.

Comparison of deep learning-based reconstruction of PROPELLER Shoulder MRI with conventional reconstruction.

Skeletal radiology
OBJECTIVE: To compare the image quality and agreement among conventional and accelerated periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI with both conventional reconstruction (CR) and deep learning-based r...

Deep learning applications in osteoarthritis imaging.

Skeletal radiology
Deep learning (DL) is one of the most exciting new areas in medical imaging. This article will provide a review of current applications of DL in osteoarthritis (OA) imaging, including methods used for cartilage lesion detection, OA diagnosis, cartila...

Original research: utilization of a convolutional neural network for automated detection of lytic spinal lesions on body CTs.

Skeletal radiology
OBJECTIVE: To develop, train, and test a convolutional neural network (CNN) for detection of spinal lytic lesions in chest, abdomen, and pelvis CT scans.

Rapid lumbar MRI protocol using 3D imaging and deep learning reconstruction.

Skeletal radiology
BACKGROUND AND PURPOSE: Three-dimensional (3D) imaging of the spine, augmented with AI-enabled image enhancement and denoising, has the potential to reduce imaging times without compromising image quality or diagnostic performance. This work evaluate...

Deep-learning-reconstructed high-resolution 3D cervical spine MRI for foraminal stenosis evaluation.

Skeletal radiology
OBJECTIVE: To compare standard-of-care two-dimensional MRI acquisitions of the cervical spine with those from a single three-dimensional MRI acquisition, reconstructed using a deep-learning-based reconstruction algorithm. We hypothesized that the imp...

Ultrafast lumbar spine MRI protocol using deep learning-based reconstruction: diagnostic equivalence to a conventional protocol.

Skeletal radiology
OBJECTIVE: To evaluate the diagnostic equivalency between an ultrafast (1 min 53 s) lumbar MRI protocol using deep learning-based reconstruction and a conventional lumbar MRI protocol (12 min 31 s).

A deep learning algorithm for detecting lytic bone lesions of multiple myeloma on CT.

Skeletal radiology
BACKGROUND: Whole-body low-dose CT is the recommended initial imaging modality to evaluate bone destruction as a result of multiple myeloma. Accurate interpretation of these scans to detect small lytic bone lesions is time intensive. A functional dee...