Paige.AI gets breakthrough status for cancer diagnosis solution

11 March 2019 (Last Updated December 23rd, 2019 10:23)

Paige.AI has received breakthrough device designation from the US Food and Drug Administration (FDA) for a cancer diagnosis solution based on artificial intelligence (AI).

Paige.AI has received breakthrough device designation from the US Food and Drug Administration (FDA) for a cancer diagnosis solution based on artificial intelligence (AI).

The company's pathology tools build on technology developed by co-founder Thomas Fuchs and are developed under a licensing agreement with the Memorial Sloan Kettering Cancer Center (MSK).

Under the agreement, Paige.AI receives de-identified images of more than one million pathology slides. MSK has been digitising the slides for four years and is now processing another four million.

"Paige.AI is focused on providing AI tools to pathologists that will enable them to become faster and more accurate in their diagnosis."

This dataset is being used to create a comprehensive portfolio of AI products across cancer subtypes to support pathologists worldwide.

Paige.AI CEO Leo Grady said: “Paige.AI is focused on providing AI tools to pathologists that will enable them to become faster and more accurate in their diagnosis and treatment recommendations for the care of cancer patients.

“We are thrilled to receive breakthrough designation and look forward to collaborating with the FDA to bring our products to market, starting with prostate cancer and expanding from there.”

The FDA grants breakthrough device status to technologies that have the potential to facilitate more effective diagnosis or treatment for life-threatening or debilitating diseases. The designation helps accelerate product development will offer priority regulatory review for Paige.AI's pathology solution.

Fuchs added: “We are honoured to have been granted breakthrough designation by the FDA, which underscores the groundbreaking nature of our technology as the leading clinical-grade AI in computational pathology to combine vast amounts of high-quality data with unique deep learning architectures in service of delivering better patient care.”