Avicenna.AI will integrate its CINA Head triage AI solution for stroke detection with Canon’s Automation platform.

The FDA-approved device identifies signs of intracranial hemorrhage and large vessel occlusion from CT scans.

"In stroke, time is brain. Our combined product ensures that the stroke CT solution may be delivered end-to-end with native integration from image acquisition through stroke detection, ensuring overall timely patient care. Every other product on the market is a third-party application that would need to be integrated separately," Dr. Peter Chang, radiologist and co-founder of Avicenna.AI, told HCB News.
“With its proven ability to help radiologists identify pathologies quickly, but also to highlight those that require the most urgent care, CINA Head is exactly the kind of innovative solution that we want to offer clinicians,” Toshiki Kato, general manager of Healthcare IT at Canon Medical Systems, said in a statement.

Canon’s Automation Platform is an AI-based, zero-click solution that uses deep learning technology to streamline workflow for fast results. The solution collects images directly from the scanner or repository and then analyzes, tags and sorts the data for triage, workflow prioritization and treatment decision-making.

CINA Head automatically detects and prioritizes acute ICH and LVO cases within 20 seconds through a combination of deep learning and machine learning, and alerts radiologists to any changes within their systems.

Data from 814 cases, conducted at more than 250 imaging centers across the U.S., were used to test the efficacy of the solution’s ICH capabilities, which showed 96% accuracy, 91.4% sensitivity and 97.5% specificity. An additional 476 cases were used to test the product’s LVO detection capability, which ranked with 97.7% accuracy, 97.9% sensitivity and 97.6% specificity.

AI-enabled CT stroke triage, specifically for large-vessel occlusion detection, is the first deep-learning tool to receive the CMS New Technology Add-on Payment designation, which is expected to encourage adoption of technologies that show substantial clinical improvement for standard of care. Under this designation, it is eligible for up to $1,040 in reimbursement, according to Chang.

"In the past few years, many startups and applications have been developed without a clear path to clinical value-added," he said. "This new shift should accelerate the innovation and adoption of deep learning technology by finally aligning incentives of both software developers and clinical providers."

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