Could EKGs help doctors use AI to detect pulmonary embolisms?
Pulmonary embolisms are dangerous, lung-clogging blot clots. In a pilot study, scientists at the Icahn School of Medicine at Mount Sinai showed for the first time that artificial intelligence (AI) algorithms can detect signs of these clots in electrocardiograms (EKGs), a finding which may one day help doctors with screening.
The results published in the European Heart Journal – Digital Health suggested that new machine learning algorithms, which are designed to exploit a combination of EKG and electronic health record (EHR) data, may be more effective than currently used screening tests at determining whether moderate- to high-risk patients actually have pulmonary embolisms.
The study was led by Sulaiman S. Somani, MD, a former medical student in the lab of Benjamin S. Glicksberg, PhD, Assistant Professor of Genetics and Genomic Sciences and a member of the Hasso Plattner Institute for Digital Health at Mount Sinai.
Pulmonary embolisms happen when deep vein blood clots, usually formed in the legs or arms, break away and clog lung arteries. These clots can be lethal or cause long-term lung damage. 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.
https://academic.oup.com/ehjdh/advance-article/doi/10.1093/ehjdh/ztab101/6440044
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