A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence-Powered Ultrasound for Improving Clinical Workflow.
Ultrasound is the most commonly used imaging modality in clinical practice because it is a nonionizing, low-cost, and portable point-of-care imaging tool that provides real-time images. Artificial intelligence (AI)-powered ultrasound is becoming more mature and getting closer to routine clinical app...
| Publicado en: | Journal of the American College of Radiology Vol. 16; no. 9; pp. 1318 - 1329 |
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| Autores principales: | , , , , , , |
| Formato: | review Journal Article |
| Publicado: |
Elsevier B.V.
Sep2019:Part A
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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=138569277&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138569277 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15461440 EAO jtl: Journal of the American College of Radiology issn: 15461440 maglogo: N pubinfo: dt: Sep2019:Part A vid: 16 iid: 9 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 138569277 138569277 NLM31492410 138569277 10.1016/j.jacr.2019.06.004 NLM31492410 138569277 ppf: 1318 ppct: 11 formats: tig: atl: A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence-Powered Ultrasound for Improving Clinical Workflow. aug: au: Akkus, Zeynettin Cai, Jason Boonrod, Arunnit Zeinoddini, Atefeh Weston, Alexander D Philbrick, Kenneth A Erickson, Bradley J affil: Radiology Informatics Lab, Department of Radiology, Mayo Clinic, Rochester, Minnesota sug: subj: Ultrasonography, Doppler, Color Methods Quality Improvement Systems Analysis Male Thyroid Neoplasms Artificial Intelligence Breast Neoplasms United States Female Algorithms Liver Neoplasms Forecasting Ferrans and Powers Quality of Life Index Scales Male Female ab: Ultrasound is the most commonly used imaging modality in clinical practice because it is a nonionizing, low-cost, and portable point-of-care imaging tool that provides real-time images. Artificial intelligence (AI)-powered ultrasound is becoming more mature and getting closer to routine clinical applications in recent times because of an increased need for efficient and objective acquisition and evaluation of ultrasound images. Because ultrasound images involve operator-, patient-, and scanner-dependent variations, the adaptation of classical machine learning methods to clinical applications becomes challenging. With their self-learning ability, deep-learning (DL) methods are able to harness exponentially growing graphics processing unit computing power to identify abstract and complex imaging features. This has given rise to tremendous opportunities such as providing robust and generalizable AI models for improving image acquisition, real-time assessment of image quality, objective diagnosis and detection of diseases, and optimizing ultrasound clinical workflow. In this report, the authors review current DL approaches and research directions in rapidly advancing ultrasound technology and present their outlook on future directions and trends for DL techniques to further improve diagnosis, reduce health care cost, and optimize ultrasound clinical workflow. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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