Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications.
Background: In recent years, the integration of artificial intelligence (AI) techniques into medical imaging has shown great potential to transform the diagnostic process. This review aims to provide a comprehensive overview of current state-of-the-art applications for AI in abdominal and pelvic ult...
| Publicado en: | Abdominal Radiology Vol. 50; no. 4; pp. 1775 - 1790 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184038984&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184038984 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Apr2025 vid: 50 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184038984 180581309 10.1007/s00261-024-04640-x 184038984 ppf: 1775 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications. aug: au: Cai, Lie Pfob, André affil: https://ror.org/013czdx64 Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany sug: ab: Background: In recent years, the integration of artificial intelligence (AI) techniques into medical imaging has shown great potential to transform the diagnostic process. This review aims to provide a comprehensive overview of current state-of-the-art applications for AI in abdominal and pelvic ultrasound imaging. Methods: We searched the PubMed, FDA, and ClinicalTrials.gov databases for applications of AI in abdominal and pelvic ultrasound imaging. Results: A total of 128 titles were identified from the database search and were eligible for screening. After screening, 57 manuscripts were included in the final review. The main anatomical applications included multi-organ detection (n = 16, 28%), gynecology (n = 15, 26%), hepatobiliary system (n = 13, 23%), and musculoskeletal (n = 8, 14%). The main methodological applications included deep learning (n = 37, 65%), machine learning (n = 13, 23%), natural language processing (n = 5, 9%), and robots (n = 2, 4%). The majority of the studies were single-center (n = 43, 75%) and retrospective (n = 56, 98%). We identified 17 FDA approved AI ultrasound devices, with only a few being specifically used for abdominal/pelvic imaging (infertility monitoring and follicle development). Conclusion: The application of AI in abdominal/pelvic ultrasound shows promising early results for disease diagnosis, monitoring, and report refinement. However, the risk of bias remains high because very few of these applications have been prospectively validated (in multi-center studies) or have received FDA clearance. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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