Bibliographic Details
| Title: |
Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis—A Rapid Review |
| Authors: |
Nicole Brunetti, Massimo Calabrese, Carlo Martinoli, Alberto Stefano Tagliafico |
| Source: |
Diagnostics, Vol 13, Iss 1, p 58 (2022) |
| Publisher Information: |
MDPI AG, 2022. |
| Publication Year: |
2022 |
| Collection: |
LCC:Medicine (General) |
| Subject Terms: |
artificial intelligence, breast cancer, ultrasound, deep learning, machine learning, Medicine (General), R5-920 |
| More Details: |
Background: Ultrasound (US) is a fundamental diagnostic tool in breast imaging. However, US remains an operator-dependent examination. Research into and the application of artificial intelligence (AI) in breast US are increasing. The aim of this rapid review was to assess the current development of US-based artificial intelligence in the field of breast cancer. Methods: Two investigators with experience in medical research performed literature searching and data extraction on PubMed. The studies included in this rapid review evaluated the role of artificial intelligence concerning BC diagnosis, prognosis, molecular subtypes of breast cancer, axillary lymph node status, and the response to neoadjuvant chemotherapy. The mean values of sensitivity, specificity, and AUC were calculated for the main study categories with a meta-analytical approach. Results: A total of 58 main studies, all published after 2017, were included. Only 9/58 studies were prospective (15.5%); 13/58 studies (22.4%) used an ML approach. The vast majority (77.6%) used DL systems. Most studies were conducted for the diagnosis or classification of BC (55.1%). At present, all the included studies showed that AI has excellent performance in breast cancer diagnosis, prognosis, and treatment strategy. Conclusions: US-based AI has great potential and research value in the field of breast cancer diagnosis, treatment, and prognosis. More prospective and multicenter studies are needed to assess the potential impact of AI in breast ultrasound. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2075-4418 |
| Relation: |
https://www.mdpi.com/2075-4418/13/1/58; https://doaj.org/toc/2075-4418 |
| DOI: |
10.3390/diagnostics13010058 |
| Access URL: |
https://doaj.org/article/1f614eaa10af4c96ad29388774d33b6d |
| Accession Number: |
edsdoj.1f614eaa10af4c96ad29388774d33b6d |
| Database: |
Directory of Open Access Journals |
