A Review of Artificial Intelligence in Breast Imaging.
Dhurgham Al-KarawiShakir Al-ZaidiKhaled Ahmad HelaelNaser ObeidatAbdulmajeed Mounzer MouhsenTarek AjamBashar A AlshalabiMohamed SalmanMohammed H AhmedPublished in: Tomography (Ann Arbor, Mich.) (2024)
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.
Keyphrases
- artificial intelligence
- magnetic resonance imaging
- deep learning
- machine learning
- big data
- breast cancer risk
- high resolution
- mental health
- image quality
- contrast enhanced
- healthcare
- computed tomography
- type diabetes
- early breast cancer
- mass spectrometry
- systematic review
- polycystic ovary syndrome
- fluorescence imaging
- randomized controlled trial
- young adults
- photodynamic therapy
- drug induced
- pregnancy outcomes
- childhood cancer