Research Lab Content Update

Summary:

Introduction: Preterm birth, defined as birth occurring before 37 weeks gestation, affects over 12% of births in the United States, leading to significant infant mortality and an economic burden of 26 billion dollars annually. This study proposes a low-cost solution to analyze the biomechanical and electrical properties of cervical tissue during pregnancy to predict preterm birth.

Objective: The primary objective of the research is to develop the CerviCheck Probe, a device designed to predict the timing of preterm labor in pregnant individuals. This prediction is based on the analysis of cervical tissue using impedance spectroscopy and mechanical strain measurements.

Methodology: The impedance detection system employs the AD5933 chip to perform frequency sweeps and measure impedance with high accuracy, controlled by an Arduino platform. The system includes an MCP4725 digital-to-analog converter (DAC) to regulate pressure and a multiplexer to select different testing channels. For mechanical strain analysis, vacuum pressure is applied to the cervical tissue, stretching it over an array of electrodes. This process allows for the examination of mechanical properties through stress/strain curves. Data collection is designed to be rapid, minimizing contact time, and uses Fast Fourier Transform (FFT) for frequency analysis and noise reduction. The system also plans to incorporate machine learning to optimize data collection and prediction accuracy.

Components: The device components include an Arduino-controlled impedance analyzer, a pressure regulator, a vacuum system, and a multiplexer. The probe tip, which is disposable and made of medical-grade plastic, contains a flex PCB and a camera. The system is calibrated with a reference resistor to ensure accurate impedance calculations.

Conclusion: Early testing is currently underway to verify the functionality of the device. Future plans include expanding testing through crowd-sourcing and publishing the findings. The research aims to create a device that collects sufficient data within a short contact time by integrating machine learning algorithms to enhance prediction accuracy.