Abstract:
Fetal compromise is a significant global health issue that can result in severe long-term disability and mortality. Current fetal monitoring is predominantly performed using cardiotocography (CTG) machines in healthcare settings, limiting clinical oversight to routine hospital visits or during labor. CTG monitoring typically relies on intermittent visual interpretation, sometimes resulting in inconsistent clinical judgement and delayed intervention. Artificial intelligence (AI) has been proposed to assist in CTG interpretation, but conventional AI models require substantial computational resources, making them impractical for wearable, battery-powered devices designed for continuous monitoring. In this study, we propose a quantized Edge AI model for detecting fetal compromise (pH < 7.05) in a resource-constrained setting. Utilizing the low-power MAX78002 AI microcontroller, we demonstrate real-time fetal compromise detection with a deep learning model optimized for power efficiency. Our final model, trained using knowledge distillation combined with quantization-aware training on an internal dataset of 9,887 CTG recordings and tested on 552 CTG recordings from the public CTU-UHB dataset, achieves an AUC of 0.81 while consuming only 2 mJ of energy per 60 minutes of inference data. This represents comparable performance to a GPU-based model on the same dataset, while achieving a 94% reduction in energy consumption. This work demonstrates that deep learning models for fetal compromise detection can be quantized and deployed on low-power Edge AI hardware, paving the way for wearable devices capable of continuous, real-time monitoring.
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