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<p>Invasive venous blood draws remain the clinical standard for hematology, yet they are invasive, time-consuming, and costly. We introduce Video-to-Vessels, a computer-vision pipeline that converts high-magnification videos of bulbar conjunctiva capillaries into low-dimensional spatiotemporal vessel representations, reducing video dimensionality by ~200-fold while preserving hemodynamic information. These representations feed VesselNet, a multi-instance regression network that encodes each vessel with a modified ConvNeXt backbone, fuses vessel-specific thickness via cross-attention, and predicts blood biomarkers from concatenated embeddings. On a cohort of 224 participants with paired laboratory counts, VesselNet achieves a hemoglobin-based anemia ROC-AUC of 82.8% and a Spearman’s <i>ρ</i> of 0.47, while attaining a <i>ρ</i> of 0.46 for red-blood-cell (RBC) count regression. Removing local stabilization and segmentation-denoising lowers <i>ρ</i> by 38% for hemoglobin and 19% for RBC, underscoring their contributions. Our results mark a step toward a fully noninvasive complete blood count, coupling representation learning with ocular imaging.</p>

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Towards noninvasive blood count using a deep learning pipeline from bulbar conjunctiva videos

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月8日

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  • Tamir Denis,
  • Ifat Sher,
  • Emily Praisman,
  • Marian Haiadry,
  • Amir Zag,
  • Ohad Benjamini,
  • Abraham Avigdor,
  • Keren Asraf,
  • Ram Doolman,
  • Lior Wolf,
  • Haim Suchowski,
  • Ygal Rotenstreich

摘要

Invasive venous blood draws remain the clinical standard for hematology, yet they are invasive, time-consuming, and costly. We introduce Video-to-Vessels, a computer-vision pipeline that converts high-magnification videos of bulbar conjunctiva capillaries into low-dimensional spatiotemporal vessel representations, reducing video dimensionality by ~200-fold while preserving hemodynamic information. These representations feed VesselNet, a multi-instance regression network that encodes each vessel with a modified ConvNeXt backbone, fuses vessel-specific thickness via cross-attention, and predicts blood biomarkers from concatenated embeddings. On a cohort of 224 participants with paired laboratory counts, VesselNet achieves a hemoglobin-based anemia ROC-AUC of 82.8% and a Spearman’s ρ of 0.47, while attaining a ρ of 0.46 for red-blood-cell (RBC) count regression. Removing local stabilization and segmentation-denoising lowers ρ by 38% for hemoglobin and 19% for RBC, underscoring their contributions. Our results mark a step toward a fully noninvasive complete blood count, coupling representation learning with ocular imaging.