A Review of Artificial Intelligence in Breast Imaging.


Journal

Tomography (Ann Arbor, Mich.)
ISSN: 2379-139X
Titre abrégé: Tomography
Pays: Switzerland
ID NLM: 101671170

Informations de publication

Date de publication:
09 May 2024
Historique:
received: 05 03 2024
revised: 14 04 2024
accepted: 06 05 2024
medline: 24 5 2024
pubmed: 24 5 2024
entrez: 24 5 2024
Statut: epublish

Résumé

With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women's physical and mental health. Early breast cancer screening-through mammography, ultrasound, or magnetic resonance imaging (MRI)-can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.

Identifiants

pubmed: 38787015
pii: tomography10050055
doi: 10.3390/tomography10050055
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

705-726

Auteurs

Dhurgham Al-Karawi (D)

Medical Analytica Ltd., 26a Castle Park Industrial Park, Flint CH6 5XA, UK.

Shakir Al-Zaidi (S)

Medical Analytica Ltd., 26a Castle Park Industrial Park, Flint CH6 5XA, UK.

Khaled Ahmad Helael (KA)

Royal Medical Services, King Hussein Medical Hospital, King Abdullah II Ben Al-Hussein Street, Amman 11855, Jordan.

Naser Obeidat (N)

Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan.

Abdulmajeed Mounzer Mouhsen (AM)

Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan.

Tarek Ajam (T)

Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan.

Bashar A Alshalabi (BA)

Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan.

Mohamed Salman (M)

Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan.

Mohammed H Ahmed (MH)

School of Computing, Coventry University, 3 Gulson Road, Coventry CV1 5FB, UK.

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Classifications MeSH