An AI-powered navigation framework to achieve an automated acquisition of cardiac ultrasound images

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dc.contributor.authorSoemantoro, Raska
dc.contributor.authorKardos, Attila
dc.contributor.authorTang, Gilbert
dc.contributor.authorZhao, Yifan
dc.date.accessioned2023-09-18T12:20:55Z
dc.date.available2023-09-18T12:20:55Z
dc.date.issued2023-09-11
dc.description.abstractEchocardiography is an effective tool for diagnosing cardiovascular disease. However, numerous challenges affect its accessibility, including skill requirements, workforce shortage, and sonographer strain. We introduce a navigation framework for the automated acquisition of echocardiography images, consisting of 3 modules: perception, intelligence, and control. The perception module contains an ultrasound probe, a probe actuator, and a locator camera. Information from this module is sent to the intelligence module, which grades the quality of an ultrasound image for different echocardiography views. The window search algorithm in the control module governs the decision-making process in probe movement, finding the best location based on known probe traversal positions and image quality. We conducted a series of simulations using the HeartWorks simulator to assess the proposed framework. This study achieved an accuracy of 99% for the image quality model, 96% for the probe locator model, and 99% for the view classification model, trained on an 80/20 training and testing split. We found that the best search area corresponds with general guidelines: at the anatomical left of the sternum between the 2nd and 5th intercostal space. Additionally, the likelihood of successful acquisition is also driven by how long it stores past coordinates and how much it corrects itself. Results suggest that achieving an automated echocardiography system is feasible using the proposed framework. The long-term vision is of a widely accessible and accurate heart imaging capability within hospitals and community-based settings that enables timely diagnosis of early-stage heart disease.en_UK
dc.identifier.citationSoemantoro R, Kardos A, Tang G, Zhao Y. (2023) An AI-powered navigation framework to achieve an automated acquisition of cardiac ultrasound images, Scientific Reports, Volume 13, September 2023, Article Number 15008en_UK
dc.identifier.issn2045-2322
dc.identifier.urihttps://doi.org/10.1038/s41598-023-42263-2
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20232
dc.language.isoenen_UK
dc.publisherSpringer Natureen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleAn AI-powered navigation framework to achieve an automated acquisition of cardiac ultrasound imagesen_UK
dc.typeArticleen_UK

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