AI-based ML algorithms could increase detection of undiagnosed AF – Cardiac Rhythm News

A joint press release from Bristol Myers Squibb and Pfizer has highlighted the findings of an artificial intelligence (AI)-based machine learning (ML) technique that has been shown in a test database to exhibit greater predictive performance than other currently available risk prediction models for atrial fibrillation (AF). The data from the UK study were published in PLoS ONE.

The study found that the algorithms, developed using routine patient records, have the potential to enrich the patient population for targeted screening. According to the joint statement, the next stage is to test the algorithm in routine clinical practice and quantify its impact in terms of the number of AF cases identified, and the associated potential cost savings in the earlier detection of AF.

Current methods for AF detection, such as opportunistic pulse checking in those >65 years and in the over age group, mean that around 100 people are screened to identify one person with AF. The study found that adopting the AI algorithm could reduce this number to one in nine. It tested whether AI was more accurate than existing risk prediction models, using the health records of nearly three million people.

Commenting in the press release, Mark ONeill (St Thomas Hospital and Kings College, London, UK), one of the study authors, says: This AI technique represents quite an astonishing leap in precision. The implications are huge, especially because ML can be so easily and affordably used in routine clinical practice with the potential to transform the diagnosis of AF. If we can find and treat people living unwittingly with AF, we can do a much better job of preventing complications like stroke and heart disease.

The press release states that the ML algorithm is potentially more precise than routine practice because it not only looks for risk factors, but also how they change, and can spot complex relationships between risk predictors, that cannot be readily identified by humans, such as subtle changes in blood pressure prior to diagnosis or frequency of GP visits.

In 2007, Pfizer and Bristol-Myers Squibb entered into a global alliance to commercialise the oral anticoagulant apixaban.

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AI-based ML algorithms could increase detection of undiagnosed AF - Cardiac Rhythm News

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