A Holographic Sensor-Integrated Deep Learning Framework for Noninvasive Assessment of Stored Red Blood Cell Quality

Abstract

Prolonged storage of red blood cells (RBCs) induces morphological degradation that can compromise transfusion efficacy. Traditional quality assessment methods are often labor-intensive and time-consuming, limiting their utility in real-time settings. Although deep learning has been applied to RBC imaging, most approaches require large datasets and complex architectures, making them impractical for efficient deployment. This study introduces a holographic sensor-integrated deep learning framework for noninvasive RBC quality assessment using small datasets. A diffusion model is employed to synthetically generate phase images and segmentation masks, augmenting limited data. Self-supervised learning with pre-trained models further enhances classification performance while maintaining a streamlined model architecture. Compared to conventional segmentation methods, the proposed framework achieves higher accuracy and significantly faster inference. It also enables reliable detection of storage-induced morphological changes, providing proportional indicators of transfusion viability. Experimental results validate its effectiveness as a practical tool for real-time, sensor-driven monitoring of RBC quality.