Chalian, Hamid

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.

Abstract Chest computed tomography (CT) is one of the most frequently performed imaging studies. Incidental osseous and articular findings are commonly encountered in chest CTs in daily practice. The spectrum of findings is broad, varying from benign to malignant, and interpretation of these incidental musculoskeletal findings could be challenging for radiologists. In this review, we provide a systematic algorithmic approach for the diagnosis of common articular findings seen on chest CT with recommendations for appropriate follow-up evaluation.