Artificial intelligence powered advancements in upper extremity joint MRI: A review

Abstract

Magnetic resonance imaging (MRI) is an indispensable medical imaging examination technique in musculoskeletal medicine. Modern MRI techniques achieve superior high-quality multiplanar imaging of soft tissue and skeletal pathologies without the harmful effects of ionizing radiation. Some current limitations of MRI include long acquisition times, artifacts, and noise. In addition, it is often challenging to distinguish abutting or closely applied soft tissue structures with similar signal characteristics. In the past decade, Artificial Intelligence (AI) has been widely employed in musculoskeletal MRI to help reduce the image acquisition time and improve image quality. Apart from being able to reduce medical costs, AI can assist clinicians in diagnosing diseases more accurately. This will effectively help formulate appropriate treatment plans and ultimately improve patient care. This review article intends to summarize AI's current research and application in musculoskeletal MRI, particularly the advancement of DL in identifying the structure and lesions of upper extremity joints in MRI images.

Keywords: Artificial intelligence; Convolution neural network; Deep learning; Magnetic resonance imaging; Upper extremity.

Fig. 1

A schematic diagram of the 2D and 3D CNN models. For 2D CNN, a single image slice was the input, while 2D convolution layers were utilized to extract image features and then fed into a classifier, from which the output realized RC recognition or RCT diagnosis. For the 3D CNN model, several image slices from different views (transverse, coronal, and sagittal) or the reconstructed 3D blocks were the input, and 3D convolution layers were utilized to extract image features and then fed into a classifier, from which the output realized RC recognition or RCT diagnosis.

Fig. 2

Process of TFCC 3D reconstruction. The DL image analysis method for the anatomical structure of the wrist in MRI realizes the intelligent processing of MRI images and the intelligent reconstruction of the TFCC.

Fig. 3

An overview of AI application in shoulder and wrist MRI. The middle lower part contains the deep learning image reconstruction (DLR) technique or algorithm, and the upper part contains the conventional image acceleration or denoising technique. The combination or independent application of the two techniques obtain a super-resolution MRI image and/or reduces the acquisition time. The side parts are the application of the DL network in MRI image segmentation or disease diagnosis.