TY - JOUR
T1 - Machine Learning and Deep Learning in Cardiothoracic Imaging
T2 - A Scoping Review
AU - Khosravi, Bardia
AU - Rouzrokh, Pouria
AU - Faghani, Shahriar
AU - Moassefi, Mana
AU - Vahdati, Sanaz
AU - Mahmoudi, Elham
AU - Chalian, Hamid
AU - Erickson, Bradley J.
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/10
Y1 - 2022/10
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - cardiothoracic imaging
KW - deep learning
KW - machine learning
KW - radiology
KW - scoping review
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U2 - 10.3390/diagnostics12102512
DO - 10.3390/diagnostics12102512
M3 - Review article
AN - SCOPUS:85140635380
SN - 2075-4418
VL - 12
JO - Diagnostics
JF - Diagnostics
IS - 10
M1 - 2512
ER -