Deep Learning and Signal Processing for Low-Cost Doppler Fetal Assessment

Project Overview

A field-deployable fetal monitoring system built around a low-cost (~$15) handheld Doppler connected to a smartphone, where we have developed a full on-device pipeline that converts raw one-dimensional audio into clinically meaningful information in real time. The pipeline combines signal processing (filtering, normalization, artifact suppression, and signal-saturation detection) with deep learning models for fetal heart rate tracking, signal quality indices that guide probe positioning, and gestational-age estimation using hierarchical sequence modeling; a mobile computer-vision component uses real-time object detection and digit recognition to transcribe bedside blood-pressure displays into structured data. All inference runs inside an Android application with lightweight, quantized models to deliver sub-second, on-screen feedback that reduces unusable windows, stabilizes fetal heart rate and gestational-age estimates in noisy, real-world conditions, and packages results for seamless handoff to clinical workflows—bringing reliable point-of-care analytics to low-resource settings.

Related Publications

• Self-Supervised Learning for Gestational Age Estimation from Low-Cost Doppler Ultrasound in Low-Resource Settings

  M Motie-Shirazi, E Ramos, S Chandrasekaran, R Sameni, P Rohloff, GD Clifford, N Katebi

  TS4H NeurIPS 2025

• Next-Generation Fetal Heart Monitoring: Leveraging Neural Sequential Modeling for Ultrasound Analysis

  A Rafiei, M Motie-Shirazi, R Sameni, GD Clifford, N Katebi

  IEEE Transactions on Biomedical Engineering

• Edge AI for Real-time Fetal Assessment in Rural Guatemala

  N Katebi, M Ahmad, M Motie-Shirazi, D Phan, E Kolesnikova, S Nikookar, ...

  Demo Track, ML4H

• Point-of-Care Real-Time Signal Quality for Fetal Doppler Ultrasound Using a Deep Learning Approach

  M Motie-Shirazi, R Sameni, P Rohloff, N Katebi, GD Clifford

  Findings Track, ML4H

• Real-Time Quality Feedback on Doppler Data for Community Midwives using Edge-AI

  M Motie-Shirazi, S Nikookar, M Ahmad, A Rafiei, R Sameni, P Rohloff, ...

  Machine Learning: Health