AI-Powered Lung Maturity Test Identifies Newborns at Higher Risk of Respiratory Distress

Each year, approximately 300,000 babies in the United States are born between 32 and 36 weeks' gestation, according to national health data. This group is at an elevated risk for respiratory distress, a major contributor to neonatal morbidity and prolonged hospitalizations. Conventional clinical approaches often rely on a "wait and see" method to determine which infants require intervention. Unfortunately, this approach can lead to higher NICU admissions, delayed treatments, increased healthcare expenses, and added stress for families and infants. Now, an artificial intelligence (AI)-driven lung maturity test (LMT) is poised to close this gap by providing an objective screening tool to help identify newborns who are at risk more promptly.

SIME Diagnostics (London, UK) has developed an innovative platform that includes a point-of-care device and a single-use cartridge designed to quickly analyze routinely collected samples without the need for reagents. The device enables the real-time evaluation of lung biochemistry by measuring key biomarkers—lecithin and sphingomyelin. These biomarkers' ratio reflects surfactant levels, which are crucial for efficient oxygen exchange in developing lungs. By offering clinicians valuable data to guide respiratory support decisions within the first hour of life, the AI platform aids in the early identification of infants needing intervention, reducing unnecessary treatments and minimizing delays. This platform is specifically designed for use in intensive care settings, helping healthcare professionals provide timely and focused respiratory care.

Image: The POC device rapidly predicts neonatal respiratory disease at birth in the NICU (Photo courtesy of SIME Diagnostics)

In a recent study involving 207 late-preterm infants (those born at more than 30 weeks' gestation), researchers tested gastric aspirate samples using the point-of-care device from SIME Diagnostics. The results indicated that the LMT successfully assessed biomarkers related to lung maturity, facilitating early identification of infants at risk for respiratory distress. The study concluded that the device accurately predicted which infants would require extended respiratory support and identified those whose need for such support would resolve within six hours. Ongoing studies and clinical assessments aim to refine the device's applications further, potentially extending its use across various hospital settings.

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