Advancements in Maternal and Fetal Health Monitoring

The field of maternal and fetal health monitoring is rapidly evolving with the integration of machine learning and digital technologies. Recent developments have focused on improving the accuracy and accessibility of health monitoring tools, particularly in low-resource settings. Researchers are exploring the use of 3D body scans, ultrasound videos, and carotid sonography to predict adverse pregnancy outcomes and detect cardiovascular risk. Automated tools and algorithms are being developed to assist sonographers and healthcare professionals in diagnosing and monitoring fetal health. These innovations have the potential to enhance prenatal care, streamline standard plane acquisition, and improve health outcomes for mothers and babies. Noteworthy papers include:

  • A study on using machine learning on optical 3D body scans to predict adverse pregnancy outcomes, which achieved high prediction accuracies exceeding 88%.
  • A paper introducing the Multi-Tier Class-Aware Token Transformer (MCAT) method for localizing standard anatomical clips in fetal ultrasound videos, which outperformed state-of-the-art methods by 10% and 13% mIoU.

Sources

Scope of Online Maternal Health Information in Kinyarwanda and Opportunities for Digital Health Developers

Maternal and Fetal Health Status Assessment by Using Machine Learning on Optical 3D Body Scans

MCAT: Visual Query-Based Localization of Standard Anatomical Clips in Fetal Ultrasound Videos Using Multi-Tier Class-Aware Token Transformer

Deep Learning for Cardiovascular Risk Assessment: Proxy Features from Carotid Sonography as Predictors of Arterial Damage

Determining Fetal Orientations From Blind Sweep Ultrasound Video

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