Artificial Intelligence Model is Developed to Screen Pulmonary Embolism

New artificial intelligence (AI) algorithms can detect signs of pulmonary embolism in electrocardiograms (EKGs), according to a study published in the European Heart Journal - Digital Health.

Pulmonary embolisms are dangerous, lung-clogging blot clots. They happen when deep vein blood clots, usually formed in the legs or arms, breakaway and clog lung arteries. These clots can be lethal or cause long-term lung damage.

‘A new machine learning model along with electrocardiogram readings is 15 to 30 percent more effective at predicting severe pulmonary embolism cases.’

Although some patients may experience shortness of breath or chest pain, these symptoms may also signal other problems that have nothing to do with blood clots, making it difficult for doctors to properly diagnose and treat cases.

Moreover, current official diagnoses rely on computed tomography pulmonary angiograms (CTPAs), which are time-consuming chest scans that can only be performed at select hospitals and require patients to be exposed to potentially dangerous levels of radiation.

To make diagnoses easier and more accessible, researchers have spent more than 20 years developing advanced computer programs, or algorithms, designed to help doctors determine whether at-risk patients are experiencing pulmonary embolisms.

In a new study, researchers found that fusing algorithms that rely on EKG and EHR data may be an effective alternative because EKGs are widely available and relatively easy to administer.

Researchers created and tested out various algorithms on data from 21,183 Mount Sinai Health System patients who showed moderate to highly suspicious signs of having pulmonary embolisms.

While some algorithms were designed to use EKG data to screen for pulmonary embolisms, others were designed to use EHR data. In each situation, the algorithm learned to identify a pulmonary embolism case by comparing either EKG or EHR data with corresponding results from CTPAs.

Finally, a third, fusion algorithm was created by combining the best-performing EKG algorithm with the best-performing EHR one.

The results showed that the fusion model not only outperformed its parent algorithms but was also better at identifying specific pulmonary embolism cases than other currently used screening tests.

Researchers also estimated that the fusion model was anywhere from 15 to 30 percent more effective at accurately screening acute embolism cases, and the model performed best at predicting the most severe cases.

Furthermore, the fusion model's accuracy remained consistent regardless of whether race or sex was tested as a factor, suggesting it may be useful for screening a variety of patients.

These results support the theory that EKG data may be effectively incorporated into new pulmonary embolism screening algorithms. They plan to further develop and test these algorithms out for potential utility in the clinic.



Source: Medindia