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pro vyhledávání: '"H. Asher Ai"'
Autor:
Farzan Vahedifard, Xuchu Liu, Kranthi K. Marathu, H. Asher Ai, Mark P. Supanich, Mehmet Kocak, Seth Adler, Shehbaz M. Ansari, Melih Akyuz, Jubril O. Adepoju, Sharon Byrd
Publikováno v:
Reproductive Medicine, Vol 5, Iss 3, Pp 113-135 (2024)
Accurately predicting a fetus’s gestational age (GA) is crucial in prenatal care. This study aimed to develop an artificial intelligence (AI) model to predict GA using biometric measurements from fetal brain magnetic resonance imaging (MRI). We ass
Externí odkaz:
https://doaj.org/article/3e1d07c5d2b0442985cd9963410425f1
Autor:
Farzan Vahedifard, H. Asher Ai, Mark P. Supanich, Kranthi K. Marathu, Xuchu Liu, Mehmet Kocak, Shehbaz M. Ansari, Melih Akyuz, Jubril O. Adepoju, Seth Adler, Sharon Byrd
Publikováno v:
Diagnostics, Vol 13, Iss 14, p 2355 (2023)
In this study, we developed an automated workflow using a deep learning model (DL) to measure the lateral ventricle linearly in fetal brain MRI, which are subsequently classified into normal or ventriculomegaly, defined as a diameter wider than 10 mm
Externí odkaz:
https://doaj.org/article/b93b3861f83d40e39c4cec8f1f2a3f38
Autor:
H. Asher Ai, Charles E. Willis, Ehsan Samei, Jered R. Wells, Joshua M. Wilson, Thomas K. Nishino
Publikováno v:
Medical Physics. 45:4377-4391
Purpose The purpose of this study was to determine whether a proposed suite of objective image quality metrics for digital chest radiographs is useful for monitoring image quality in a clinical setting unique from the one where the metrics were devel
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