Artificial intelligence, machine learning and deep learning in musculoskeletal imaging: Current applications

Abstract

Artificial intelligence is rapidly expanding in all technological fields. The medical field, and especially diagnostic imaging, has been showing the highest developmental potential. Artificial intelligence aims at human intelligence simulation through the management of complex problems. This review describes the technical background of artificial intelligence, machine learning, and deep learning. The first section illustrates the general potential of artificial intelligence applications in the context of request management, data acquisition, image reconstruction, archiving, and communication systems. In the second section, the prospective of dedicated tools for segmentation, lesion detection, automatic diagnosis, and classification of musculoskeletal disorders is discussed.

Sound of synergy: ultrasound and artificial intelligence in sports medicine

“…Integrating AI in musculoskeletal imaging shows promising results, with various applications ranging from image interpretation to disease progression analysis 7. Convolutional neural networks have been used to automatically detect and classify rotator cuff tears from ultrasound images, improving clinical accuracy and timely diagnoses 8.…”

“…Convolutional neural networks have been used to automatically detect and classify rotator cuff tears from ultrasound images, improving clinical accuracy and timely diagnoses 8. AI-driven tools employing support vector machines also have been used to improve injury detection in stress fractures, enabling personalised treatments and enhanced patient outcomes 7…”

Artificial Intelligence in Cardiovascular CT and MR Imaging

“…In particular, machine learning (ML) algorithms have the ability to learn from data, improve with experience, and make predictions [ 2 ]. Deep learning (DL) is a subtype of machine learning that does not require manual data input and generates artificial neural networks, capable of learning data and creating features [ 3 , 4 ]. In cardiovascular imaging, the use of AI can aid the radiologists’ workflow, reducing acquisition and post-processing time, improving image quality and exam accuracy.…”

Classification of racehorse limb radiographs using deep convolutional neural networks

“…Deep convolutional neural networks (DCNN) are a class of machine learning (ML) methods with the potential to augment radiological diagnosis and research. 1,2 ML has been used in human [3][4][5][6] and veterinary [7][8][9][10] medicine for a few decades. Until the early 2010s, application of ML to radiographs required experts to define a set of features (called radiomics 11,12 ); the features were used to create models using methods like linear regression, decision trees and support vector machines.…”