Výsledky vyhledávání - "Slavotinek, A."

Akademický článek

Diagnostic yield after next-generation sequencing in pediatric cardiovascular disease

Autor: Anne M. Slavotinek, Michelle L. Thompson, Lisa J. Martin, Bruce D. Gelb

Publikováno v: HGG Advances, Vol 5, Iss 3, Pp 100286- (2024)

Summary: Genetic testing with exome sequencing and genome sequencing is increasingly offered to infants and children with cardiovascular diseases. However, the rates of positive diagnoses after genetic testing within the different categories of cardi

Externí odkaz: https://doaj.org/article/6c06b15aec4143bfb132259c13f4e3e8

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Akademický článek

Satisfaction with mode of delivery of genomic sequencing results in a diverse national sample of research participants through the Clinical Sequencing Evidence-Generating Research Consortium

Autor: Scollon, Sarah, Robinson, Jill O., Jo, Eunji, Suckiel, Sabrina A., Amendola, Laura M., Foreman, Ann Katherine M., Jarvik, Gail P., Rini, Christine, Wang, Tao, Slavotinek, Anne

Publikováno v: In Genetics in Medicine September 2024 26(9)

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Akademický článek

Genetic ancestry and diagnostic yield of exome sequencing in a diverse population

Autor: Yusuph Mavura, Nuriye Sahin-Hodoglugil, Ugur Hodoglugil, Mark Kvale, Pierre-Marie Martin, Jessica Van Ziffle, W. Patrick Devine, Sara L. Ackerman, Barbara A. Koenig, Pui-Yan Kwok, Mary E. Norton, Anne Slavotinek, Neil Risch

Publikováno v: npj Genomic Medicine, Vol 9, Iss 1, Pp 1-14 (2024)

Abstract It has been suggested that diagnostic yield (DY) from Exome Sequencing (ES) may be lower among patients with non-European ancestries than those with European ancestry. We examined the association of DY with estimated continental/subcontinent

Externí odkaz: https://doaj.org/article/e1e6ad4fde6842039e6b02089d668270

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Akademický článek

Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA

Autor: Adrian P. Brady, Bibb Allen, Jaron Chong, Elmar Kotter, Nina Kottler, John Mongan, Lauren Oakden-Rayner, Daniel Pinto dos Santos, An Tang, Christoph Wald, John Slavotinek

Publikováno v: Insights into Imaging, Vol 15, Iss 1, Pp 1-19 (2024)

Abstract Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by a

Externí odkaz: https://doaj.org/article/a0270d79cf654afb80e58b7ac7183bff

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