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<p>Identifying blood groups accurately is critical for safe medical practices, especially in emergencies, surgeries, and prenatal care. Conventional methods often depend on visual inspection of agglutination reactions, which can be error-prone, particularly when using small sample volumes. In this work, we introduce an efficient and intelligent system for blood type detection that combines digital microfluidics with advanced deep learning and antigen-based decision logic. A Vision Transformer (ViT) model was trained to recognize agglutination patterns in droplet-based blood images, followed by an automated mapping of standard ABO/Rh rules to assign the corresponding blood group based on antigen presence. The proposed system achieved 100% accuracy specifically in detecting antigen–antibody agglutination reactions, with perfect scores across precision, recall, specificity, and F1-score. Our method reduces the need for large reagent volumes and minimizes testing cost, while also improving reliability. In future work, our aim is to embed this system into a compact, microfluidics paper-based device, enabling low-cost, AI-assisted blood typing in portable point of care settings.</p>

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Automated blood group classification using a digital microfluidics chip and vision transformer-based image analysis

  • 期刊论文

  • 发表日期：

    2026年5月20日

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  • Syeda Sana Bukhari,
  • Aleena Khan,
  • Khansa,
  • Hammas Ullah,
  • Shahab U. Ansari,
  • Ali Turab Jafry

摘要

Identifying blood groups accurately is critical for safe medical practices, especially in emergencies, surgeries, and prenatal care. Conventional methods often depend on visual inspection of agglutination reactions, which can be error-prone, particularly when using small sample volumes. In this work, we introduce an efficient and intelligent system for blood type detection that combines digital microfluidics with advanced deep learning and antigen-based decision logic. A Vision Transformer (ViT) model was trained to recognize agglutination patterns in droplet-based blood images, followed by an automated mapping of standard ABO/Rh rules to assign the corresponding blood group based on antigen presence. The proposed system achieved 100% accuracy specifically in detecting antigen–antibody agglutination reactions, with perfect scores across precision, recall, specificity, and F1-score. Our method reduces the need for large reagent volumes and minimizes testing cost, while also improving reliability. In future work, our aim is to embed this system into a compact, microfluidics paper-based device, enabling low-cost, AI-assisted blood typing in portable point of care settings.