Deep learning model provides rapid detection of stroke-causing blockages

Deep learning model provides rapid detection of stroke-causing blockages

Press releases may be edited for formatting or style | September 23, 2020 Artificial Intelligence Cardiology Stroke
OAK BROOK, Ill. – A sophisticated type of artificial intelligence (AI) called deep learning can help rapidly detect blockages in the arteries that supply blood to the head, potentially speeding the onset of life-saving treatment, according to a study published in Radiology.

Large vessel occlusions are blockages in the arteries that supply oxygenated blood to the brain. These occlusions account for a significant proportion of ischemic strokes, the most common type of stroke. Prompt diagnosis is critical in order to begin recanalization, or opening of the blocked artery, through a treatment known as endovascular therapy.

"Minutes matter in this time-sensitive diagnosis," said study lead author Matthew T. Stib, M.D., a radiology resident a.t the Warren Alpert Medical School at Brown University in Providence, Rhode Island. "Every minute that we reduce the time to recanalization extends the patient's disability-free life by a week."

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CT angiography (CTA), a three-minute exam that provides detailed views of the blood vessels, is the gold standard for detecting these occlusions. Radiologists are highly accurate at identifying large vessel occlusions on CTA, but they are not always available, and any backlogs at the hospital can further delay care.

Dr. Stib and his colleagues at Brown explored the use of deep learning to help provide rapid detection of large vessel occlusions on CTA and reduce time to treatment.

Working closely with Brown's computer science department, the researchers developed a deep learning model from scratch. They used a large sample of CTA examinations for patients with suspected acute ischemic stroke to train the algorithm to recognize the appearance of large vessel occlusions and distinguish it from other conditions. Preprocessing of the CTA exams included the creation of maximum intensity projection images to emphasize the contrast-enhanced vasculature. The researchers also used multiphase CTA, a newer approach that provides more comprehensive information than the single-phase technique.

When they tested the deep learning model on multiphase CTA examinations of 62 patients, the model detected all 31 large vessel occlusions for a sensitivity of 100%, a statistically significant improvement over the 77% sensitivity rate of single-phase CTA. The use of multiphase CTA contributed to the improved performance.

"These results are quite promising," Dr. Stib said. "We really wanted to optimize the sensitivity of the model so that we were sure that we picked up every single case because missing a case has pretty dire consequences."

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