Artificial intelligence in breast ultrasound: application in clinical practice Review
| Authors: | Fruchtman Brot, H.; Mango, V. L. |
| Review Title: | Artificial intelligence in breast ultrasound: application in clinical practice |
| Abstract: | Ultrasound (US) is a widely accessible and extensively used tool for breast imaging. It is commonly used as an additional screening tool, especially for women with dense breast tissue. Advances in artificial intelligence (AI) have led to the development of various AI systems that assist radiologists in identifying and diagnosing breast lesions using US. This article provides an overview of the background and supporting evidence for the use of AI in hand held breast US. It discusses the impact of AI on clinical workflow, covering breast cancer detection, diagnosis, prediction of molecular subtypes, evaluation of axillary lymph node status, and response to neoadjuvant chemotherapy. Additionally, the article highlights the potential significance of AI in breast US for low and middle income countries. © 2024 Korean Society of Ultrasound in Medicine (KSUM). |
| Keywords: | review; lymph node metastasis; prospective study; sensitivity and specificity; sentinel lymph node biopsy; clinical practice; breast cancer; echomammography; breast neoplasms; prediction; ultrasound; axillary lymph node; training; algorithm; computer assisted diagnosis; artificial intelligence; breast tumor; neoadjuvant chemotherapy; predictive value; breast lesion; receiver operating characteristic; artificial neural network; interrater reliability; computer-aided diagnosis; computer-aided detection; machine learning; workflow; image guided biopsy; breast imaging reporting and data system; human; female; shear wave elastography; deep learning; clinical workflow; breast cancer molecular subtype; resource limited setting; axillary nodal metastasis; deep learning algorithm |
| Journal Title: | Ultrasonography |
| Volume: | 43 |
| Issue: | 1 |
| ISSN: | 2288-5919 |
| Publisher: | Korean Soc Ultrasound Medicine |
| Date Published: | 2024-01-01 |
| Start Page: | 3 |
| End Page: | 14 |
| Language: | English |
| DOI: | 10.14366/usg.23116 |
| PROVIDER: | scopus |
| PMCID: | PMC10766882 |
| PUBMED: | 38109894 |
| DOI/URL: | |
| Notes: | Source: Scopus |
