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Clinical Trial Summary

Investigators hypothesize that premature newborns with poor cardiopulmonary performance have higher morbidities and poorer physical and cognitive developmental outcomes. Investigators further hypothesize that audible sounds combined with novel inaudible vibrations above and below human perception interpreted with transparent and auditable AI algorithms can detect and identify early gas and fluid movement anomalies not uncovered by conventional tools in an non-invasive, easy, fast, and low cost examination.


Clinical Trial Description

Worldwide preterm birth (<37 weeks of gestation) affects approximately 10% of live births and is the leading cause of death in children less than 5 years of age. Preterm birth disrupts normal lung development leading to several respiratory complications in the neonatal period and later in life. Consequently, factors that negatively affect prenatal and early life respiratory growth can compromise the achievement of "personal-best lung function". This novel study will generate normative, audible/inaudible frequencies, visible/invisible frequencies, and perceptible/imperceptible energies, termed vibrome biosignatures, of cardiopulmonary development and function during early postnatal development. Once baseline patterns are established, future studies will be designed to characterize vibrome biosignature differences across acute neonatal respiratory problems, such as respiratory distress syndrome, meconium aspiration, sepsis, persistent pulmonary hypertension, and congenital heart disease. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT05827250
Study type Observational
Source Level 42 AI, Inc.
Contact Pinaki Panigrahi, MD, PhD
Phone (2020) 444-5553
Email pinaki.panigrahi@georgetown.edu
Status Recruiting
Phase
Start date February 13, 2023
Completion date February 28, 2025

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