Ultrasound-embeddable AI sharp at diagnosing clogged carotid arteries
Testing AI’s ability to detect carotid artery disease on ultrasound, U.K. researchers have found their algorithm achieved 90% accuracy, along with 87% sensitivity and 82% specificity, at the task.
The same algorithm was even better at ruling out the disease, hitting 92% accuracy (with 91% sensitivity and 86% specificity) at identifying normal carotids.
Vascular surgeon Ali Kordzadeh, MBBS, MD, of Anglia Ruskin University and colleagues describe their work in a report published June 10 in Vascular [1].
The team prospectively acquired 156 neck ultrasound scans of patients undergoing duplex ultrasound for suspicion of carotid artery disease.
They had the images interpreted by a network built on convolutional neural network architecture.
Along with the up-or-down results above, the researchers found their AI 94% accurate at detecting carotid arteries that had stenosis of less than 50% plaque.
In sonograms of arteries with between 50% and 70% plaque stenosis, the algorithm had 88% accuracy.
And when evaluating arteries with more than 75% stenosis, the AI had 92% accuracy.
“This study demonstrates the feasibility, applicability and accuracy of artificial intelligence in the detection of carotid artery disease in grayscale static duplex ultrasound images,” Kordzadeh and co-authors write.
More:
This network has the potential to be used as a standalone software or to be embedded in any duplex ultrasound machine. This can enhance carotid artery disease recognition with limited or no vascular experience or serve as a stratification tool for tertiary referral, further imaging and overall management.”
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Reference:
- Ali Kordzadeh et al., “Artificial intelligence and duplex ultrasound for detection of carotid artery disease.” Vascular, June 10, 2022. DOI: https://doi.org/10.1177/17085381221107465