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A team at Cedars-Sinai Medical Center in Los Angeles has developed a new artificial intelligence algorithm that can predict the risk of suffering a heart attack in the next five years. To do this, it relies on a precise measurement of the deposits of fatty plaques which accumulate on the walls of the arteries and which lead to atherosclerosis. The verdict falls in just a few seconds.
To predict the likelihood of a myocardial infarction (more commonly known as a heart attack) occurring, doctors usually do a computed tomography angiogram (CTA). This is a 3D imaging examination that allows you to observe the vascular network and detect any abnormalities; it thus makes it possible to estimate the quantity of fatty deposits which have accumulated in the coronary arteries. This buildup of coronary plaque can lead to narrowing of the arteries, or even total blockage, which prevents blood from reaching the heart, increasing the likelihood of suffering a heart attack.
Nevertheless, this plaque volume measurement is not systematic, as Professor Damini Dey explainsfrom the Cedars-Sinai Biomedical Imaging Research Institute: there is no fully automated way to do this […]it takes at least 25-30 minutes for an expert “. In contrast, the algorithm developed by Dey and his colleagues only takes five to six seconds to quantify coronary plaque from CTA images. This study constitutes the first validation of a deep learning approach for the quantification of atherosclerosis from coronary angiography images.
Efficiency similar to that of human diagnostics
To train their algorithm, the researchers put together a set of six cohorts (921 patients in total), from five different countries, who all underwent coronary angiography examination by computed tomography between 2010 and 2019 (via several CT scanners and protocols different). The images had previously been reviewed by expert human readers. The clinical spectrum of coronary artery disease ranged from stable angina to acute myocardial infarction, up to post-myocardial infarction convalescent stage. The severity of the stenosis was scored from 0 to 5 depending on the level of narrowing of the artery.
The algorithm is based on a new neural network based on ConvLSTM — a hierarchical convolutional long-term memory network — which is the first to accurately quantify coronary stenosis and the volumes of all components of atherosclerotic plaque. It works by first highlighting the coronary arteries in 3D images and then identifying blood and plaque deposits in the coronary arteries.
The effectiveness of the tool has been validated on a set of test images of 275 patients. The results are unequivocal: the researchers report “excellent agreement between deep learning and expert readers” for the estimation of total plaque, calcified plaque and non-calcified plaque volumes. The agreement was also good for the evaluation of the diameter of the stenosis.
All for a record analysis time: “ The average deep learning plate analysis time per patient was 5.65 s when the calculation was performed using a graphics processing unit and 3.82 min when using a graphics processing unit. central processing “, specify the researchers in The Lancet Digital Health. For comparison, the average analysis time by the experts was 25.66 min per patient.
A reliable and non-invasive prediction tool
The team says the algorithm was found to be equally effective, when its results were compared to those obtained from images taken during two invasive tests considered to be very accurate in assessing plaque and narrowing of coronary arteries. : intravascular ultrasound and coronary angiography by catheter. ” Deep learning-based analysis showed excellent agreement and correlation with intravascular ultrasound for measurements of total plaque volume and minimum luminal area “, underline the authors of the study.
Once the measurement capabilities of the tool had been assessed, the researchers wanted to test its prognostic capabilities. The algorithm had to estimate the risk of a fatal or non-fatal myocardial infarction occurring within five years, based on scans of a sample of 1611 patients with stable chest pain (41 of whom actually suffered an infarction in the years following their examination). Again, the program showed great accuracy. It appears from the study that patients with a total plaque volume of 238.5 mm³ or more had a 7 times higher risk of having a myocardial infarction than patients with a lower volume.
Further research will be needed to train the algorithm from a larger and more varied population of patients. But this first study is a new example of the incredible performance of artificial intelligence in image analysis, which will most certainly be put to use in the future of medicine. ” More studies are needed, but we may be able to predict if a person is likely to have a heart attack and when, based on the amount and composition of the plaque imaged by this standard testing Dey said.
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A new AI accurately and quickly predicts the risk of heart attack over 5 years
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