Assessment of germinal matrix hemorrhage on head ultrasound with deep learning algorithms.

Journal: Pediatric radiology

Published Date: Jan 22, 2022

Abstract

BACKGROUND: Germinal matrix hemorrhage-intraventricular hemorrhage is among the most common intracranial complications in premature infants. Early detection is important to guide clinical management for improved patient prognosis.

Authors

Kevin Y Kim

Department of Radiology, Loma Linda University Medical Center, Loma Linda, CA, USA.
Rajeev Nowrangi

Department of Radiology, Loma Linda University Medical Center, Loma Linda, CA, USA.
Arianna McGehee

Loma Linda University School of Medicine, Loma Linda, CA, USA.
Neil Joshi

Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland, United States of America.
Patricia T Acharya

Loma Linda University School of Medicine, Loma Linda, CA, USA. pacharya@chla.usc.edu.

Keywords

Algorithms Deep Learning Humans Infant Infant, Newborn Neural Networks, Computer ROC Curve Ultrasonography

External Resources

View on PubMed Access via DOI PubMed (35064324)

Assessment of germinal matrix hemorrhage on head ultrasound with deep learning algorithms.

Abstract

Authors

Keywords

External Resources

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