Importance: An accurate and robust artificial intelligence (AI) algorithm for detecting cancer in digital breast tomosynthesis (DBT) could significantly improve detection accuracy and reduce health care costs worldwide.
Objectives: To make training and evaluation data for the development of AI algorithms for DBT analysis available, to develop well-defined benchmarks, and to create publicly available code for existing methods.
Design, Setting, And Participants: This diagnostic study is based on a multi-institutional international grand challenge in which research teams developed algorithms to detect lesions in DBT.
Background Digital breast tomosynthesis (DBT) has higher diagnostic accuracy than digital mammography, but interpretation time is substantially longer. Artificial intelligence (AI) could improve reading efficiency. Purpose To evaluate the use of AI to reduce workload by filtering out normal DBT screens.
View Article and Find Full Text PDFObjective: To quantify changes in respiratory and glycemic control outcomes following antenatal corticosteroids (ANCS) exposure in late preterm neonates.
Design/methods: The study included 500 neonates born between 34 0/7 and 36 6/7 weeks of gestation. Study population was divided into two groups: an immature group (34 0/7-35 6/7 weeks) and a mature group (36 0/7-36 6/7 weeks).
Background: In the developing brain, the death of immature oligodendrocytes (OLs) has been proposed to explain a developmental window for vulnerability to white matter injury (WMI). However, in neonatal mice, chronic sublethal intermittent hypoxia (IH) recapitulates the phenotype of diffuse WMI without affecting cellular viability. This work determines whether, in neonatal mice, a developmental window of WMI vulnerability exists in the absence of OLs lineage cellular death.
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