Using AI and machine learning, Nucleai examines pathology images and spatial data at both cellular and tissue levels. Its exclusive technology identifies intricate patterns and characteristics from medical images, providing valuable insights into the tumor microenvironment, cellular structure, and spatial connections among various cell types.
Nucleai, a spatial AI biomarker company that deciphers cellular communications and maps cellular interactions within tissue samples to forecast therapeutic outcomes, disclosed on Wednesday that it has secured a $14 million investment. Leading the round is M Ventures, the corporate venture capital division of Merck KGaA, Darmstadt, Germany, with support from existing investors, totaling funding to $60 million. Notable existing investors include Section 32, a VC fund established by former Google Ventures CEO Bill Maris, Sanofi Ventures, Debiopharm, Fosun RZ Capital, Vertex Ventures Israel, and Grove Ventures.
The Israeli startup intends to utilize the funding to further implement its AI algorithms for the prospective recruitment of patients in clinical trials, a groundbreaking endeavor in personalized solutions tailored to individual patient needs.
Established in 2018, Nucleai is helmed by Avi Veidman, Eliron Amir, Lotan Chorev, and Albert Achtenberg, who spearheaded the AI computer vision department in the Israeli military’s Intelligence Corps. Nucleai’s spatial analysis technology equips pathologists with the intelligence to anticipate and navigate complex diseases like cancer. The company asserts collaborations with over 60% of the top 20 biopharmaceutical companies.
The application of AI and ML enables Nucleai to scrutinize pathology images and spatial data with precision. Its unique technology discerns intricate patterns and features from medical images, thereby uncovering insights into the tumor microenvironment, cellular morphology, and spatial relationships among diverse cell types. These capabilities facilitate drug development, refine biomarker discovery, and enhance the precision of therapeutic targeting, ultimately leading to more effective and personalized treatment options for patients.
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