Smartphone video motion analysis accurately detects carotid artery stenosis
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Motion analysis of video recorded on a smartphone accurately identified carotid artery stenosis in adults, demonstrating utility for a tool that could improve ischemic stroke outcomes.
Carotid artery stenosis results in altered pulsation characteristics on the skin surface and small but distinct differences in the patterns between healthy and diseased vessels may in theory be detectable, Hsien-Li Kao, MD, an interventional cardiologist at National Taiwan University Hospital in Taipei, Taiwan, and colleagues wrote in the Journal of the American Heart Association. Digital video processing techniques with motion magnification can be used to detect subtle movements, and video-based motion analysis could potentially be used in telehealth to quickly screed for carotid artery stenosis in daily clinical practice.
“Between 2% and 5% of strokes each year occur in people with no symptoms, so better and earlier detection of stroke risk is needed,” Kao said in a press release.
In a prospective study, Kao and colleagues analyzed data from 202 adults with prior carotid Doppler ultrasound data at National Taiwan University Hospital (mean age, 68 years; 79% men). Researchers recorded a 30-second video clip of participants’ necks using a commercial mobile device and analyzed with video-based motion analysis using mathematical quantification of the amplitude of skin motion changes.
“Each patient was asked to lie in the supine position with his/her head placed inside a custom-made box positioned to avoid movements during video recording,” the researchers wrote. “An Apple iPhone 6 64GB was mounted through a rectangular cutout on the top side of the box and used to record the video. Two fiber-optic lights were installed inside the box on either side of the iPhone, oriented at an 45° angle in opposite directions to create a uniform light source.”
Data from the first 40 participants were used for the video motion analysis protocol and define cutoff values; data from the remaining participants were used for validation.
Within the cohort, 54% of participants had ultrasound-confirmed carotid artery stenosis.
Using receiver operating characteristic (ROC) curve analysis, the area under the curve of VMA-derived discrepancy values to differentiate patients with and without carotid artery stenosis was 0.914 (95% CI, 0.874–0.954; P < .01). The best cutoff value of video motion analysis-derived discrepancy values to screen for carotid artery stenosis was 5.1, with a sensitivity of 87% (95% CI, 79-93), a specificity of 87% (95% CI, 79-93), a positive predictive value of 89% (95% CI, 82-93) and negative predictive value of 85% (95% CI, 78-90) in detecting carotid artery stenosis.
The diagnostic accuracy was consistently high in different participant subgroups, including patients with bilateral disease, where the sensitivity remained as high as 89%, according to researchers.
The researchers noted that participants were all at high CV risk; more data are needed from adults at low CV risk.
“More research is needed to determine whether video recorded on smartphones is a promising approach to help expedite and increase stroke screening,” Kao said in the release. “Carotid artery stenosis is silent until a stroke happens. With this method, clinicians may be able to record a video of the patient’s neck with a smartphone, upload the videos for analysis and receive a report within 5 minutes. The early detection of carotid artery stenosis may improve patient outcomes.”
The U.S. Preventive Services Task Force in 2021 reaffirmed its 2014 recommendation for carotid artery stenosis screening in asymptomatic adults, saying the risks currently outweigh the benefits. It is listed as a D recommendation.
Reference:
- Smartphone video motion analysis detected narrowed neck arteries that may lead to stroke. https://newsroom.heart.org/news/smartphone-video-motion-analysis-detected-narrowed-neck-arteries-that-may-lead-to-stroke. Published on Wednesday, August 17, 2022. Accessed on Wednesday, August 17, 2022.
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