# Reliable Noninvasive Glucose Sensing via CNN-Based Spectroscopy

arXiv: https://arxiv.org/abs/2506.13819 · HTML: https://mlsanity.com/paper/2506.13819/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
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- First posted on arXiv 15 Jun 2025.

## Abstract (arXiv)

In this study, we present a dual-modal AI framework based on short-wave infrared (SWIR) spectroscopy. The first modality employs a multi-wavelength SWIR imaging system coupled with convolutional neural networks (CNNs) to capture spatial features linked to glucose absorption. The second modality uses a compact photodiode voltage sensor and machine learning regressors (e.g., random forest) on normalized optical signals. Both approaches were evaluated on synthetic blood phantoms and skin-mimicking materials across physiological glucose levels (70 to 200 mg/dL). The CNN achieved a mean absolute percentage error (MAPE) of 4.82% at 650 nm with 100% Zone A coverage in the Clarke Error Grid, while the photodiode system reached 86.4% Zone A accuracy. This framework constitutes a state-of-the-art solution that balances clinical accuracy, cost efficiency, and wearable integration, paving the way for reliable continuous non-invasive glucose monitoring.