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Building a machine learning-assisted echocardiography prediction tool for children at risk for cancer therapy-related cardiomyopathy

Abstract Background Despite routine echocardiographic surveillance for childhood cancer survivors, the ability to predict cardiomyopathy risk in individual patients is limited. We explored the feasibility and optimal processes for machine learning-enhanced cardiomyopathy prediction in survivors usin...

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Tallennettuna:
Päätekijät: Lindsay A. Edwards, Christina Yang, Surbhi Sharma, Zih-Hua Chen, Lahari Gorantla, Sanika A. Joshi, Nicolas J. Longhi, Nahom Worku, Jamie S. Yang, Brandy Martinez Di Pietro, Saro Armenian, Aarti Bhat, William Border, Sujatha Buddhe, Nancy Blythe, Kayla Stratton, Kasey J. Leger, Wendy M. Leisenring, Lillian R. Meacham, Paul C. Nathan, Shanti Narasimhan, Ritu Sachdeva, Karim Sadak, Eric J. Chow, Patrick M. Boyle
Aineistotyyppi: Artigo
Kieli:Inglês
Julkaistu: BMC 2024-10-01
Sarja:Cardio-Oncology
Aiheet:
Machine learning
Cardiomyopathy
Cancer survivorship
Echocardiography
Linkit:https://doi.org/10.1186/s40959-024-00268-4
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