用近红外光谱+AI实现无创血糖监测，精度达临床可用水平。
Reliable Noninvasive Glucose Sensing via CNN-Based Spectroscopy
- 双模态AI框架：图像特征与光电信号融合分析
- CNN模型在650nm下误差仅4.82%，90%以上数据落在安全区
- 兼顾成本与可穿戴性，适合糖尿病日常监测
本研究提出一种基于短波红外（SWIR）光谱的双模态AI框架。第一模态采用多波长SWIR成像系统结合卷积神经网络（CNN），捕捉与葡萄糖吸收相关的空间特征；第二模态使用紧凑型光电二极管电压传感器，结合机器学习回归器（如随机森林）对归一化光信号进行分析。两种方法在模拟血液样本和皮肤替代材料上评估，覆盖生理血糖范围（70至200 mg/dL）。CNN模型在650 nm波长下实现4.82%的平均绝对百分比误差（MAPE），Clarke误差图中达到100%的Zone A覆盖率；光电二极管系统则获得86.4%的Zone A准确率。该框架在临床准确性、成本效益与可穿戴集成之间取得平衡，为可靠的连续无创血糖监测提供了前沿解决方案。
原文摘要 · Abstract (English)
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.
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