RapidAI Among the First Stroke Imaging Companies with Software Approved for Medicare New Technology Add-on Payment

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Stroke Imaging Makes Significant Stride in Reimbursement. Helps U.S. Hospitals More Easily Adopt the Latest Advancements in Stroke Care

RapidAI, the worldwide leader in advanced imaging for stroke, today announced Rapid LVO is among the first software products to qualify for the New Technology Add-on Payment (NTAP). NTAP is part of the CMS Inpatient Prospective Payment System (IPPS). A significant advancement in stroke care and reimbursement for Medicare patients, the news further fuels the expansion of advanced stroke imaging for those technologies that meet the NTAP requirements, foremost Rapid LVO from RapidAI.

The new NTAP program applies to LVO triage and notification for stroke. Rapid LVO explicitly meets the definition of measuring arterial blood flow in the brain per the issued NTAP code definition. RapidAI offers the most comprehensive stroke imaging platform available. Once an LVO (Large Vessel Occlusion) is identified, Rapid ASPECTS can quantify the severity of the stroke, and Rapid CTP or Rapid MRI can then notify a stroke team if a patient is eligible for thrombectomy.

“Our mission can be simply stated…save lives, save time and save money,” said Don Listwin, CEO of RapidAI. “This exciting new development in Medicare reimbursement may at first seem like just a bookkeeping issue or just a win for RapidAI, the industry leader. However, in truth, this is not a step forward for any one company, but instead an advancement for various software technologies with the end goals of saving more lives and enabling better patient outcomes.”

RapidAI makes the most-widely used stroke imaging software for patient care, research, and clinical trials—helping hospitals around the world save time, money, and lives. Rapid is the only clinically validated platform available and considered by many to be the gold standard for advanced cerebrovascular imaging.

To learn more visit www.RapidAI.com.

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AI Technology Can Help Speed Up Stroke Diagnosis And Treatment

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A type of artificial intelligence known as deep learning can accurately use CT scan images to pick up blockages in the arteries that supply blood to the brain, which cause a large percentage of strokes.

The researchers aim was to reduce the amount of time to diagnosis as much as feasibly possible as it is vital to treat these blockages quickly.

“Minutes matter in this time-sensitive diagnosis,” says study lead Matthew Stib, a radiology resident at the Warren Alpert Medical School at Brown University in Providence, Rhode Island. “Every minute that we reduce the time [to treatment] extends the patient’s disability-free life by a week.”

A type of scan called CT angiography is the standard method to detect these blockages, which takes a few minutes to complete. However, normally a trained radiologist is required to identify them and when hospitals are busy or don’t have resident experts this can take valuable time.

With the aim of reducing the time to treatment of these patients, Stib and colleagues worked with the computer science department at Brown University to develop an open source algorithm, deep learning system to evaluate CT images to look for large vessel blockages.

After first training the system on hundreds of CT images taken from patients with suspected stroke, the researchers carried out a test simulation including 62 patients to see if the system could correctly identify the patients who had arterial blockages.   

They used both single-phase and multiphase CT angiography to see which technique provided the best results when combined with the deep learning system. Multiphase CT angiography takes images at several time points during the scan and therefore provides a more detailed image than single phase CT angiography, which