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

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 fo...
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Gespeichert in:

Autor*in:	Bardia Khosravi [verfasserIn] Pouria Rouzrokh [verfasserIn] Shahriar Faghani [verfasserIn] Mana Moassefi [verfasserIn] Sanaz Vahdati [verfasserIn] Elham Mahmoudi [verfasserIn] Hamid Chalian [verfasserIn] Bradley J. Erickson [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	artificial intelligence machine learning deep learning cardiothoracic imaging scoping review radiology

Übergeordnetes Werk:	In: Diagnostics - MDPI AG, 2012, 12(2022), 10, p 2512
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:10, p 2512

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/diagnostics12102512

Katalog-ID:	DOAJ083932003

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language	English
source	In Diagnostics 12(2022), 10, p 2512 volume:12 year:2022 number:10, p 2512
sourceStr	In Diagnostics 12(2022), 10, p 2512 volume:12 year:2022 number:10, p 2512
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topic_title	R5-920 Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review artificial intelligence machine learning deep learning cardiothoracic imaging scoping review radiology
topic	misc R5-920 misc artificial intelligence misc machine learning misc deep learning misc cardiothoracic imaging misc scoping review misc radiology misc Medicine (General)
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title	Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review
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title_sort	machine learning and deep learning in cardiothoracic imaging: a scoping review
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title_auth	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.
abstractGer	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_unstemmed	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.
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title_short	Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review
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Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review

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