Two of the largest drugmakers in the country are investing in a startup applying artificial intelligence in pathology.
Boston-based PathAI said Monday that it had closed its $75 million Series B financing round with funding from New York-based Bristol-Myers Squibb and the Merck Global Health Innovation Fund, part of Kenilworth, New Jersey-based Merck & Co. PathAI said it would use the money to bolster its clinical development capabilities. PathAI had announced its $60 million Series B financing its April, led by venture capital firms General Atlantic and General Catalyst, with participation from LabCorp, which the company said the latest investment follows and extends.
“Merck, BMS and other strategic partners understand that having these best possible insight into the data is incredibly important for driving continued advancements in the rapidly moving field of immuno-oncology, ultimately helping to bring new and better treatments to patients faster,” PathAI CEO Andy Beck said in a statement.
According to the company’s website, its technology is designed to help pathologists make rapid diagnoses for patients. Merck and BMS respectively make the immuno-oncology drugs Keytruda (pembrolizumab) and Opdivo (nivolumab), both PD-1 immune checkpoint inhibitors approved for multiple tumor types.
PathAI is one of several companies that have entered into the digital pathology space in recent years. In October of last year, for example, Columbus, Ohio-based Deep Lens emerged from stealth mode with $3.2 million of seed funding, led by Sierra Ventures and with participation from Rev1 Ventures and Tamarind-Hill Fund. Another company, Tel Aviv, Israel-based Nucleai, is also active in the area.
In a paper published in 2016 in the journal Medical Image Analysis, researchers at Case Western Reserve University stated that on the one hand, digital pathology has substantial implications for telepathology, second opinions and education, as well as providing research opportunities in image computing, prognostic data and the ability to pick up features that may not be visible to pathologists.
“However, the compelling opportunities in precision medicine offered by big digital pathology data come with their own set of computational challenges,” the researchers wrote. “Image analysis and computer assisted detection and diagnosis tools previously developed in the context of radiographic images are woefully inadequate to deal with the data density in high resolution digitized whole slide images.”
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