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Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning

dc.contributor.authorTenajas, Rebeca
dc.contributor.authorMiraut, David
dc.contributor.authorIllana, Carlos I.
dc.contributor.authorAlonso González, Rodrigo
dc.contributor.authorArias Valcayo, Fernando
dc.contributor.authorLópez Herraiz, Joaquín
dc.date.accessioned2023-06-22T11:22:12Z
dc.date.available2023-06-22T11:22:12Z
dc.date.issued2023-03
dc.descriptionFunded by the Spanish Ministry of Economic Affairs and Digital Transformation (Project MIA.2021.M02.0005 TARTAGLIA, from the Recovery, Resilience, and Transformation Plan financed by the European Union through Next Generation EU funds). TARTAGLIA takes place under the R&D Missions in Artificial Intelligence program, which is part of the Spain Digital 2025 Agenda and the Spanish National Artificial Intelligence Strategy.
dc.description.abstractUltrasound (US) is a flexible imaging modality used globally as a first-line medical exam procedure in many different clinical cases. It benefits from the continued evolution of ultrasonic technologies and a well-established US-based digital health system. Nevertheless, its diagnostic performance still presents challenges due to the inherent characteristics of US imaging, such as manual operation and significant operator dependence. Artificial intelligence (AI) has proven to recognize complicated scan patterns and provide quantitative assessments for imaging data. Therefore, AI technology has the potential to help physicians get more accurate and repeatable outcomes in the US. In this article, we review the recent advances in AI-assisted US scanning. We have identified the main areas where AI is being used to facilitate US scanning, such as standard plane recognition and organ identification, the extraction of standard clinical planes from 3D US volumes, and the scanning guidance of US acquisitions performed by humans or robots. In general, the lack of standardization and reference datasets in this field makes it difficult to perform comparative studies among the different proposed methods. More open-access repositories of large US datasets with detailed information about the acquisition are needed to facilitate the development of this very active research field, which is expected to have a very positive impact on US imaging.
dc.description.departmentDepto. de Estructura de la Materia, Física Térmica y Electrónica
dc.description.facultyFac. de Ciencias Físicas
dc.description.refereedTRUE
dc.description.sponsorshipMinistry of Economic Affairs and Digital Transformation from the Recovery, Resilience, and Transformation Plan
dc.description.sponsorshipNext Generation EU funds
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/78530
dc.identifier.doi10.3390/app13063693
dc.identifier.issn2076-3417
dc.identifier.officialurlhttp://dx.doi.org/10.3390/app13063693
dc.identifier.relatedurlhttps://www.mdpi.com/
dc.identifier.urihttps://hdl.handle.net/20.500.14352/72357
dc.issue.number6
dc.journal.titleApplied sciences-basel
dc.language.isoeng
dc.publisherMDPI
dc.relation.projectIDMIA.2021.M02.0005 TARTAGLIA
dc.rightsAtribución 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/es/
dc.subject.cdu539.1
dc.subject.keywordStandard plane localization
dc.subject.keywordDeep
dc.subject.keywordClassification
dc.subject.ucmFísica nuclear
dc.subject.unesco2207 Física Atómica y Nuclear
dc.titleRecent Advances in Artificial Intelligence-Assisted Ultrasound Scanning
dc.typejournal article
dc.volume.number13
dspace.entity.typePublication
relation.isAuthorOfPublication804c568d-8b53-4973-88db-74e1c3be3c41
relation.isAuthorOfPublicationff1ea731-78c3-4e37-a602-13cc8037ae8e
relation.isAuthorOfPublication.latestForDiscovery804c568d-8b53-4973-88db-74e1c3be3c41

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