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dc.contributor.authorDivya M.O-
dc.contributor.authorVijaya M.S-
dc.date.accessioned2023-08-17T10:07:02Z-
dc.date.available2023-08-17T10:07:02Z-
dc.date.issued2023-01-01-
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-981-19-2358-6_18-
dc.description.abstractThe foetal anomaly scanning is one of the most challenging areas where accuracy of diagnosis much fluctuating with respect to the expertise of the radiologist and the mental equilibrium of the radiologist at the time of scanning. Amongst the various anomalies, foetal heart anomaly diagnosis expects precise and sensitive intellectual presence since perilous congenital heart diseases are one of the common causes resulting in the major population of infant mortality or into permanent natal faults. The accuracy of manual diagnosis of foetal cardiac abnormalities from the ultrasound scan images vary based on the human expertise and the presence of mind. Therefore, the scope of computer-assisted judgement can produce accurate diagnosis irrespective of the operator’s profile. Numerous researches are going on to explore the scope of computer-assisted judgement of abnormalities using ultrasound imaging technique (USIT), specifically using machine learning and deep learning models. This work exploits the opportunities of computer-assisted diagnosis in foetal cardiac anomaly diagnosis as this is one of the most sensitive areas where appropriate diagnosis can save a life and a wrong diagnosis may lose a life unnecessarily.en_US
dc.language.isoen_USen_US
dc.publisherSpringer Linken_US
dc.subjectCongenital heart diseaseen_US
dc.subjectEarly diagnosisen_US
dc.subjectUltrasound imagesen_US
dc.subjectImage processingen_US
dc.subjectMachine learningen_US
dc.subjectDeep learningen_US
dc.subjectFoetal cardiac anomalyen_US
dc.titleARTIFICIAL INTELLIGENT MODELS FOR AUTOMATIC DIAGNOSIS OF FOETAL CARDIAC ANOMALIES: A META-ANALYSISen_US
dc.typeArticleen_US
Appears in Collections:International Conference

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