Researchers explore the use of artificial intelligence in enhancing the yield of conventional screening for ASDs using 12-lead electrocardiography.
By Dr. Liji Thomas, MDAug 21 2023Reviewed by Benedette Cuffari, M.Sc. Congenital heart defects are relatively common, among which atrial septal defects are the most prevalent in adults. Without timely treatment, ASDs may lead to permanent cardiovascular damage with potentially fatal complications. However, ASDs are often diagnosed late or not at all due to the mild or asymptomatic nature of these defects, as well as the lack of overt examination findings.
Typically, the diagnosis of ASDs occurs as an incidental finding or after symptoms appear later in life. Screening for the condition would require routine echocardiography, which is accurate, sensitive, and non-invasive. However, the associated cost of this technique, the need for trained personnel, and the time required prevent its practical use as a population-based screening method.
What did the study show? The study covered two continents and three hospitals, including two in Japan and one in the United States. Taken together, approximately 81,000 participants were included in the analysis, with over 671,000 ECGs used for model development. The CNN technique successfully discriminated between positive and negative ASD cases. The area under the receiver operating curve was 0.85-0.90, thus demonstrating that this screening strategy effectively distinguished between patients with and without ASD in up to 90% of cases.
The simulated model resulted in a significantly improved sensitivity of screening, from about 80% based on ECG abnormalities observable by clinicians to almost 94% with the use of the CNN-based model. The specificity in both cases was 33.6%.
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