Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review

Abstract

Machine-learning (ML) and deep-learning (DL) algorithms are part of a group of modeling algorithms that grasp the hidden patterns in data based on a training process, enabling them to extract complex information from the input data. In the past decade, these algorithms have been increasingly used for image processing, specifically in the medical domain. Cardiothoracic imaging is one of the early adopters of ML/DL research, and the COVID-19 pandemic resulted in more research focus on the feasibility and applications of ML/DL in cardiothoracic imaging. In this scoping review, we systematically searched available peer-reviewed medical literature on cardiothoracic imaging and quantitatively extracted key data elements in order to get a big picture of how ML/DL have been used in the rapidly evolving cardiothoracic imaging field. During this report, we provide insights on different applications of ML/DL and some nuances pertaining to this specific field of research. Finally, we provide general suggestions on how researchers can make their research more than just a proof-of-concept and move toward clinical adoption.

The Feasibility and Performance of Total Hip Replacement Prediction Deep Learning Algorithm with Real World Data

“…The use of deep learning models in medical imaging has potential to improve the accuracy and reduce the time and cost of medical imaging analysis [ 19 , 20 ]. It can also be used to identify and classify lesions, detect signs of disease, and predict patient prognosis [ 21 , 22 , 23 , 24 , 25 ]. For the musculoskeletal system, there are several investigative tools such as computed tomography, magnetic resonance imaging, and plain radiographic films.…”