Fetal heart segmentation in a virtual reality environment.
Marcela Castro GiffoniJorge LopesGerson Luiz Ulema RibeiroAntonio Fernandes MoronHeron Werner JuniorPublished in: The international journal of cardiovascular imaging (2024)
This study presents the initial results of a pilot project using the Elucis Virtual Reality (VR) platform for fetal heart segmentation. Twelve fetal heart cases, ranging in gestational age from 24 to 30 weeks, including various cardiac conditions, were reconstructed using 3D models facilitated by the Elucis platform's integration of automated algorithms and manual adjustments. The models, which were evaluated by four experts in virtual and 3D printed formats, were of high quality and offered improved visuospatial visualization and detailed anatomical insights. This research highlights the potential of VR technology to improve prenatal diagnosis and planning for complex cardiac conditions, suggesting significant implications for continuing medical education and clinical practice in fetal cardiology.
Keyphrases
- virtual reality
- gestational age
- deep learning
- medical education
- heart failure
- machine learning
- high throughput
- clinical practice
- birth weight
- convolutional neural network
- left ventricular
- preterm birth
- atrial fibrillation
- working memory
- cardiac surgery
- body mass index
- quality improvement
- clinical trial
- acute kidney injury
- electron microscopy