DPA-Net: A dual-path attention neural network for estimating glycemic metrics from self-monitored blood glucose data

Continuous glucose monitoring (CGM) provides dense and dynamic glucose profiles that enable reliable estimation of glycemic metrics, such as time-above-range (TAR), time-in-range (TIR), and time-below-range (TBR). However, the cost and limited accessibility of CGM restrict its widespread adoption, particularly in low- and middle-income countries. In contrast, self-monitoring of blood glucose (SMBG) is inexpensive and widely available, but produces sparse and irregular measurements that are typically event-driven: patients often check glucose levels when feeling unwell (e.g., dizziness, fatigue, or discomfort). Such behaviorally triggered sampling leads to biased estimates of TAR, TIR, and TBR. To address this challenge, we propose a Dual-Path Attention Neural Network (DPA-Net) that generates unbiased time-in-ranges estimates from SMBG data by leveraging generalizable knowledge learned from large collections of paired SMBG-CGM data. DPA-Net integrates two complementary paths: (1) a spatial-channel attention path that reconstructs a CGM-like continuous glucose trajectory from sparse SMBG inputs, and (2) a multi-scale residual network path that directly predicts glycemic metrics. An inter-path alignment mechanism enforces consistency between the reconstructed trajectory and the predicted metrics, thereby reducing bias and mitigating overfitting. Furthermore, to overcome the scarcity of real-world paired SMBG-CGM datasets, we develop an Active Point Selector (APS) that models behavioral patterns underlying SMBG measurements. Utilizing large-scale CGM recordings, APS identifies the most probable temporal instances at which users would self-monitor their glucose levels and formulates a synthetic SMBG-CGM paired dataset. Experimental results demonstrate that DPA-Net achieves robust accuracy with low estimation errors and minimal systematic bias. To the best of our knowledge, this is the first machine learning framework that utilizes the knowledge of a vast amount of CGM data designed to infer key glycemic metrics from SMBG data, offering a practical framework to enhance SMBG-based glycemic assessment in settings where CGM is unavailable or unaffordable.