Better Medicine
Better Analysis, Better Diagnostic.

Automating Diagnostics

Diagnostic test results, including blood tests, inform approximately 70 percent of medical decisions. The National Institutes of Health (NIH) estimates 12 million diagnostic errors annually in U.S. outpatients. This represents 1 in 20 adults. Manual microscopic analysis is currently the gold standard for analyzing blood. Yet manual microscopy of blood is more than 20% less accurate than a standardized and repeatable automated approach. Even larger deficiencies in manual microscopy exist for disease quantification in patients.


Innovations in Diagnostics with Artificial Intelligence

PAEAN is bringing innovation to aspects of manual microscopy using convolutional neural networks with deep learning artificial intelligence, machine learning, and robotics. Our imaging sensors bundled into our unique solution captures one (1) micron per pixel that allows us to perform cellular analysis of blood smears. This approach utilizes the very latest technology to find, measure and quantify blood cells. Medical imaging presents organizations with reproducible results as it preserves the cells and enables second opinions through telemedicine..


Modern Medicine

We have prototyped a miniaturized robotics scope device that is about the size of a small printer. This robot is an all in one device capable of capturing high-resolution medical cell images of blood smears. The robot can be adapted to capture high-resolution video as well.

Our technology Classify, Quantify, and Statistically Analyze Cells. Find Deviations in Size, Structure, Appearance, Cytoplasmic Characteristics and Morphology. Convolutional Artificial Intelligence Models Trained for Exact Results, Standardized and Repeatable Analysis.


Unique Approach

Our combination of definitive cellular parameters and artificial intelligence prediction models is unique. The team at PAEAN has conducted medical imaging research that has led to breakthroughs for Malaria (, Cancerous anaplastic cells ( and Childhood Cancers (