MENLO PARK, Calif.--(BUSINESS WIRE)--Deepcell, a life science company pioneering AI-powered cell classification and isolation for cell biology and translational research, today announced that it has surpassed the milestone of 1 billion cell images in its Cell Morphology Atlas.
With an unprecedented ability to capture high resolution cell images, Deepcell is building the industry’s largest repository of artificial intelligence-defined cellular visual features. The insights derived from this atlas on the Deepcell platform will be used to continuously improve AI models for cell characterization and isolation, enabling them to identify patterns that are invisible to the human eye.
“Deepcell’s Cell Morphology Atlas is part of a continued effort to create a repository of knowledge with the goal of advancing the field of cell biology,” said Maddison Masaeli, CEO of Deepcell. “Reaching the 1 billion cell images mark is just the beginning of our work to apply our unique AI-powered technology to a wide variety of samples at scale. We intend to continue building the world’s largest cell morphology atlas as an ongoing effort.”
Deepcell was spun out of Stanford to re-invent single cell analysis by creating a new quantitative dimension of cell morphology. Since then, the company has developed its AI-powered technology to characterize, identify, and sort cells without perturbation. Simultaneously, Deepcell is working with leading researchers and medical experts to develop AI models for specific applications.
The company’s Cell Morphology Atlas is intended to help unlock deeper biological insights that will be relevant for basic and translational research as well as potential clinical applications. It will serve as a resource for the company’s microfluidic and imaging system and artificial intelligence models to enable the profiling and enrichment of target cells, as well as to unlock new discoveries in cell biology.
“Our Cell Morphology Atlas complements our proprietary imaging and sorting system, as well as our AI models for cell characterization using morphology alone,” said Mahyar Salek, co-founder and CTO of Deepcell. “The results we have generated so far demonstrate the viability and scalability of the Deepcell platform, and the ability of our AI algorithms to continuously improve by using increasingly large volumes of cell morphology data.”
Deepcell’s technology combines advances in AI, cell capture, and single-cell analysis to identify and sort cells based on detailed visual features. Unlike other approaches that rely on antibody staining or similar methods, Deepcell’s technology was developed to isolate and collect label-free cells of any type, keeping the cell intact for downstream biological characterization.
For more information about Deepcell, go to www.deepcell.com.
Deepcell is helping to advance precision medicine by combining advances in AI, cell classification and capture, and single-cell analysis to deliver novel insights through an unprecedented view of cell biology. Spun out of Stanford University in 2017, the company has created unique, microfluidics-based technology that uses continuously learning AI to classify cells based on detailed visual features and sort them without inherent bias. The Deepcell platform maintains cell viability for downstream single-cell analysis and can be used to isolate virtually any type of cell, even those occurring at frequencies as low as one in a billion. The technology will initially be available as a service for use in translational research as well as diagnostics and therapeutic development. Deepcell is privately held and based in Menlo Park, CA. For more information, please visit deepcellbio.com.