Authors
Tamir Denis, Ifat Sher, Emily Praisman, Marian Haiadry, Amir Zag, Ohad Benjamini, Abraham Avigdor, Keren Asraf, Ram Doolman, Lior Wolf, Haim Suchowski, Ygal Rotenstreich
Published in
NPJ digital medicine. Apr 08, 2026. Epub Apr 08, 2026.
Abstract
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.
PMID:
41951905
Bibliographic data and abstract were imported from PubMed on 09 Apr 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 62
- Comments 0