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Electrocardiogram screening for aortic valve stenosis using artificial intelligence

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Between 1989 and 2019, 258 607 adults [mean age 63 16.3 years; women 122 790 (48%)] with an echocardiography and an ECG performed within 180 days were identified from the Mayo Clinic database. Moderate to severe AS by echocardiography was present in 9723 (3.7%) patients. Artificial intelligence training was performed in 129 788 (50%), validation in 25 893 (10%), and testing in 102 926 (40%) randomly selected subjects. The sensitivity, specificity, and accuracy were 78%, 74%, and 74%, respectively. The sensitivity increased and the specificity decreased as age increased.


AI Algorithm Aids Early Detection of Low Ejection Fraction

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FRIDAY, May 28, 2021 (HealthDay News) -- An artificial intelligence (AI) algorithm that uses data from electrocardiography can help increase the diagnosis of low ejection fraction (EF), according to a study published online May 6 in Nature Medicine. Xiaoxi Yao, Ph.D., from the Mayo Clinic in Rochester, Minnesota, and colleagues randomly assigned 120 primary care teams, including 358 clinicians, to intervention (access to AI results from the low ejection fraction algorithm developed by Mayo and licensed to Anumana Inc.; 181 clinicians) or control (usual care; 177 clinicians) in a pragmatic trial at 45 clinics and hospitals. A total of 22,641 adult patients with echocardiography performed as part of routine care were included (11,573 in the intervention group; 11,068 controls). The researchers found positive AI results, indicating a high likelihood of low EF, in 6.0 percent of patients in both arms. More echocardiograms were obtained for patients with positive results by clinicians in the intervention group (49.6 versus 38.1 percent), but echocardiogram use was similar in the overall cohort (19.2 versus 18.2 percent).


How machine learning could help doctors improve the reading of medical images

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CHICAGO - The radiology world has been abuzz with discussions of machine learning and what artificial intelligence may mean for the future of the field. The goal is for the technology to quickly scan medical images and prioritize abnormal results, allowing doctors to spend their time on the more difficult cases. The machines would also provide a check on human error. Companies are jumping on board. IBM Watson Health, which acquired enterprise imaging software company Merge Healthcare in 2015, plans to put its Watson supercomputer to work analyzing medical images.


How machine learning could help doctors improve the reading of medical images

#artificialintelligence

The radiology world has been abuzz with discussions of machine learning and what artificial intelligence may mean for the future of the field. The goal is for the technology to quickly scan medical images and prioritize abnormal results, allowing doctors to spend their time on the more difficult cases. The machines would also provide a check on human error. Companies are jumping on board. IBM Watson Health, which acquired enterprise imaging software company Merge Healthcare in 2015, plans to put its Watson supercomputer to work analyzing medical images.


Caltech creates app that can give you a heart checkup

Daily Mail - Science & tech

Doctors have revealed a radical app that can use a smartphone camera to give a'heart healthcheck' in just minutes. It measured the tiny amount that the carotid artery displaces the skin of the neck as blood pumps through it. The team developed a technique that can infer the left ventricular ejection fraction (LVEF) of the heart by measuring the amount that the carotid artery displaces the skin of the neck as blood pumps through it. LVEF represents the amount of blood in the heart that is pumped out with each beat. When the heart is weaker, less of the total amount of blood in the heart is pumped out with each beat, and the LVEF value is lower.