Regulatory

FDA Clears IMVARIA’s Interstitial Lung Disease Screening Algorithm

ScreenDx enhances referral pathways by automatically assessing medical data for interstitial lung findings compatible with interstitial lung disease.

By: Michael Barbella

Managing Editor

IMVARIA Inc. has earned its second U.S. Food and Drug Administration (FDA) authorization, securing 510(k) clearance for its artificial intelligence (AI)-powered healthcare tool to assess interstitial lung disease (ILD).

ScreenDx is a software‐only device that receives and analyzes lung computed tomography (CT) imaging data to detect interstitial lung findings compatible with interstitial lung disease. The device supplements the standard-of‐care workflow by providing a qualitative output of imaging findings based on pattern recognition, in order to provide adjunctive information as part of a referral pathway to an appropriate qualified clinician. Patients with positively identified patterns may undergo assessment for lung fibrosis, but ScreenDx does not replace the current standard of care methods for diagnosis of lung fibrosis and the results of the device are not intended to rule-out or rule-in lung fibrosis.

“This FDA clearance marks another major milestone for IMVARIA, continuing the advancement of AI-based healthcare technologies,” IMVARIA Co-Founder/CEO Joshua Reicher, M.D., said.

Patients with ILD suffer from frequent diagnostic delays and often undergo multiple clinical and radiological assessments before receiving a diagnosis. IMVARIA developed ScreenDx to augment referral pathways by automatically assessing for interstitial lung findings compatible with interstitial lung disease, helping flag this deadly disease in a wide range of settings. It is intended as an incidental screening tool to set patients on a path to be given an appropriate diagnosis and treatment on a timely basis.

“With the FDA 510(k) clearance of our ScreenDx digital solution, IMVARIA continues on the quest to make AI highly practical, useful, and easy to incorporate into medical practice for the thousands of pulmonologists who treat patients with lung diseases,” IMVARIA Co-Founder/Chief Technology Officer Michael Muelly, M.D., stated. “Our ultimate goal is to help patients with serious diseases, like ILD, get identified and placed into the diagnostic pathway as efficiently as possible, regardless of whether they were first seen in emergency, lung cancer screening, or specialty clinical settings. As medical doctors ourselves leading IMVARIA, we are putting another powerful tool into the hands of clinicians to help enhance decision-making.”

This regulatory milestone builds on IMVARIA’s success last year with the FDA’s authorization of Fibresolve, an adjunctive diagnostic solution that uses AI to guide safe, non-invasive diagnosis of idiopathic pulmonary fibrosis (IPF). The authorization was the first by the FDA for a diagnostic tool of any type in lung fibrosis, and the first FDA Breakthrough-Designated AI diagnostic tool with simultaneously adopted CPT billing codes by the American Medical Association (AMA) in any disease.

In the United States, interstitial lung disease affects approximately 650,000 people and causes between 25,000 and 30,000 deaths annually, according to the JAMA Network. ILD is a type of lung condition that causes inflammation or fibrosis in lung tissue, characterized by shortness of breath and difficulty getting enough oxygen into the bloodstream. ILD is complex, as it can have many causes and vary in treatments. In some cases, ILD can eventually lead to a patient’s need for a lung transplant, if not detected in an early stage and not treated early enough.

IMVARIA is a health tech company pioneering AI-driven solutions that empower clinicians to make accurate diagnoses and prognoses at earlier stages of disease and reduce the need for invasive biopsy testing. Founded in 2019 by physician-engineers from Google and Stanford University, the Berkeley, Calif.-based company operates its AI Lab with automated, machine-learning algorithm technology to transform clinical decision-making into data science.

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