Manual microscopic examination of peripheral blood smears remains one of medicine’s most tedious and error-prone diagnostic procedures, causing diagnostic backlogs in clinical laboratories worldwide. Kazakhstani biomedical scientist and entrepreneur Aray Sabitova engineered Hemo.Labs, an AI-powered diagnostic platform that automates blood cell morphology analysis in under two minutes.
Automated Hematology Morphology via Computer Vision
Hemo.Labs connects standard digital laboratory microscopes to a proprietary convolutional neural network cloud service:
- High-Speed Cell Classification: The system automatically detects, segments, and classifies thousands of leukocytes, erythrocytes, and platelets in less than 120 seconds;
- Early Anomaly Detection: Algorithms flag abnormal morphological shapes associated with leukemia, anemia, and rare hematological disorders with 98%+ clinical accuracy;
- Laboratory Throughput Multiplier: By automating routine visual counting, laboratory pathologists can process 5x more clinical samples per shift without eye fatigue.
Silicon Valley Validation: Draper University and TechCrunch Disrupt
The venture demonstrated swift international traction, earning selection into Silicon Valley’s prestigious Draper University accelerator backed by venture capitalist Tim Draper. Hemo.Labs also represented Kazakhstan’s BioTech delegation at TechCrunch Disrupt in San Francisco, winning seed funding and international pilot clinical partnerships.
