PUBLISHER: Grand View Research | PRODUCT CODE: 1587855
PUBLISHER: Grand View Research | PRODUCT CODE: 1587855
The global fetal monitoring equipment market size is anticipated to reach USD 6.16 billion by 2030 and is anticipated to expand at a CAGR of 9.02% during the forecast period, according to a new report by Grand View Research, Inc. The market is driven by the increasing government initiatives that are aimed at reducing infant mortality rates. Governments are investing heavily in improving healthcare infrastructure, particularly in maternal and neonatal care. Initiatives such as the United Nations Sustainable Development Goals (UNSDGs), specifically targeting a reduction in infant mortality, have prompted government investments in fetal monitoring devices. These programs emphasize improving access to maternal healthcare and advanced medical technologies. In addition, many nations have increased funding for hospitals, clinics, and maternal health centers, often requiring fetal monitoring equipment as a standard feature in obstetric wards. This demand for improved neonatal care, induced by government-backed funding programs.
Moreover, several innovations have emerged, and initiatives are being undertaken, highlighting the potential for more effective monitoring of fetal health. For instance, in September 2023, the RADx Tech Fetal Monitoring Equipment Challenge was launched by the National Institutes of Health (NIH) in partnership with the Bill & Melinda Gates Foundation. The challenge's goal was to speed up the development of diagnostic and monitoring technologies. More than 40 entries was submitted by different innovators, focusing on creating affordable and accessible solutions for monitoring fetal health, especially in low-resource environments.
Furthermore, AI and machine learning are being integrated into fetal monitoring systems to improve data processing and interpretation. These technologies can help in noise suppression, feature detection, & fetal state classification, thereby reducing false positives and enhancing the reliability of monitoring outcomes. For instance, the Lullaby algorithm, developed by researchers at UC Irvine in 2022, effectively distinguishes fetal heartbeats from maternal signals, improving the accuracy of readings.