Enspectra Health Awarded NIH Grant to Advance Predictive Algorithms for AKs

Virtual biopsies may soon become a reality.

Enspectra Health scored $2M in grant funding from the National Cancer Institute (NCI) to support research for developing deep learning algorithms to predict which actinic keratosis (AK) lesions, are likely to progress to squamous cell carcinoma (SCC).

Enspectra's technology combines reflectance confocal and multiphoton laser scanning microscopy, resulting in high resolution images of cellular anatomy without the need for a biopsy. Unlike slide-based scanners that require biopsies for digitization, Enspectra's technology digitizes pathology directly from a subject's skin in vivo..

With the new grant, Enspectra plans to build a large database of digital histopathology on patients with AKs before topical therapy is applied. These subjects will then be followed through the course of their treatment to identify non-responders. AKs that do not respond to therapy are more likely to progress to SCC and present a higher clinical risk. Utilizing multimodal data from its novel imaging platform, Enspectra will train a deep learning algorithm intended to predict which AKs would be unresponsive to treatment based on the noninvasive images.

"We are thrilled to have been awarded a Direct-to-Phase II SBIR grant from the NCI to support the development of predictive algorithms for skin precancers," says Gabriel Sanchez, Ph.D., CEO and co-founder of Enspectra Health, in a news release. "This grant will accelerate our vision to noninvasively detect and monitor skin conditions earlier to advance care for the millions of patients with skin conditions."