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Utilizing artificial intelligence to detect cardiac amyloidosis in patients with severe aortic stenosis: A step forward to diagnose the underdiagnosed.

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Title: Utilizing artificial intelligence to detect cardiac amyloidosis in patients with severe aortic stenosis: A step forward to diagnose the underdiagnosed.
Authors: Muller, Steven A1,2,3 (AUTHOR) s.a.muller-5@umcutrecht.nl, Hauptmann, Laurenz4 (AUTHOR), Nitsche, Christian4 (AUTHOR), Oerlemans, Marish IFJ1,3 (AUTHOR)
Source: European Journal of Nuclear Medicine & Molecular Imaging. Jan2025, Vol. 52 Issue 2, p482-484. 3p.
Database: Academic Search Ultimate
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  Data: Utilizing artificial intelligence to detect cardiac amyloidosis in patients with severe aortic stenosis: A step forward to diagnose the underdiagnosed.
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  Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Nuclear+Medicine+%26+Molecular+Imaging%22">European Journal of Nuclear Medicine & Molecular Imaging</searchLink>. Jan2025, Vol. 52 Issue 2, p482-484. 3p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=182239525
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        Value: 10.1007/s00259-024-06928-y
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      – TitleFull: Utilizing artificial intelligence to detect cardiac amyloidosis in patients with severe aortic stenosis: A step forward to diagnose the underdiagnosed.
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              Text: Jan2025
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