50+ Detectable Cell Anomalies

BloodWorX AI's deep learning model was trained on over one million cells. Every anomaly below is detected automatically in your darkfield images — labelled with a bounding box, a confidence score, and a severity grade from G1 (mild) to G5 (critical).

Red Blood Cells (Erythrocytes)

Acanthocyte Echinocyte Ovalocyte Spherocyte Stomatocyte Target Cell Schistocyte Rouleaux Ring RBC Colloid Bodies Ghost Cell Elliptocyte

Aggregations & Formations

Platelet Aggregation Platelet Aggregation Fermentation Early Platelet Aggregation Fermentation Cloud Pattern Complete Protein Linkage RBC Protein Linkage Pre-Rouleaux

White Blood Cells (Leukocytes)

Eosinophil Lymphocyte Monocyte Neutrophil Band Neutrophil Degenerated Neutrophil Cohesive Neutrophil Neutrophil, Phagocytosed Ghost Cell Keratocyte

Microorganisms & Parasites

Symprotit Phase Parasitised RBC Chondrit

Crystals, Structures & Other

Fat Cluster Fat Microdroplet Fibrin Fibrin Nest Metal Protoplast Pteroharpen Platelets Poikilocytes Membrane Protrusion Fermentation

How Severity Grading Works

G1–G5 Scale

Every detected anomaly is graded from G1 (mild) to G5 (critical), so findings are comparable across images, sessions, and clients — no subjective estimation.

Confidence Scores

Each detection carries a confidence percentage, so you always know how certain the model is about a classification before you interpret it.

Implication Mapping

Detected anomalies are cross-referenced against a knowledge base and combined into up to 30+ ranked health implications — see how it works.

Important: BloodWorX AI is designed to support trained practitioners with darkfield microscopy analysis. It does not provide medical diagnoses, treatment prescriptions, or replace the judgement of a qualified healthcare professional. All AI-generated findings are for educational and informational purposes only and must be interpreted by a trained practitioner.

See every one of these on your own samples.

Upload a darkfield image and watch the AI label all of it — cell by cell, grade by grade.

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