Publications by authors named "A Melbourne"
Acoustic noise can have profound effects on wellbeing, impacting the health of the pregnant mother and the development of the fetus. Mounting evidence suggests neural memory traces are formed by auditory learning in utero. A better understanding of the fetal auditory environment is therefore critical to avoid exposure to damaging noise levels.
View Article and Find Full Text PDFIncreasing placental perfusion (PP) could improve outcomes of growth-restricted fetuses. One way of increasing PP may be by using phosphodiesterase (PDE)-5 inhibitors, which induce vasodilatation of vascular beds. We used a combination of clinically relevant magnetic resonance imaging (MRI) techniques to characterize the impact that tadalafil infusion has on maternal, placental and fetal circulations.
View Article and Find Full Text PDFAlthough the central role of adequate blood flow and oxygen delivery is known, the lack of optimized imaging modalities to study placental structure has impeded our understanding of its vascular function. Magnetic resonance imaging is increasingly being applied in this field, but gaps in knowledge remain, and further methodological developments are needed. In particular, the ability to distinguish maternal from fetal placental perfusion and the understanding of how individual placental lobules are functioning are lacking.
View Article and Find Full Text PDFStatic and dynamic responses to hyperoxia of normal placenta across gestation with T2*-weighted MRI sequences.
Ultrasound Obstet Gynecol
August 2024
Objectives: T2*-weighted magnetic resonance imaging (MRI) sequences have been identified as non-invasive tools with which to study placental oxygenation in vivo. This study aimed to use these to investigate both static and dynamic responses to hyperoxia of the normal placenta across gestation.
Methods: We conducted a single-center prospective study including 52 uncomplicated pregnancies.
A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation.
IEEE Trans Pattern Anal Mach Intell
May 2024
Article Synopsis
- Deep learning models used for looking at medical images can make big mistakes, especially when they see images that are different from what they were trained on.
- Because of these mistakes, doctors might find it hard to trust these models, so it's really important to have ways to find and fix problems with them.
- The researchers created a new system that makes sure the AI model is more reliable by checking its predictions against expert knowledge and using a backup method if needed, proving it works well with a large set of brain images from babies.