Robotics & Machine Learning Daily News2024,Issue(Mar.5) :88-88.

Institute of Radiology Reports Findings in Artificial Intelligence (Diagnostic accuracy of artificial intelligence-enabled vectorcardiography versus myocardial perfusion SPECT in patients with suspected or known coronary heart disease)

Robotics & Machine Learning Daily News2024,Issue(Mar.5) :88-88.

Institute of Radiology Reports Findings in Artificial Intelligence (Diagnostic accuracy of artificial intelligence-enabled vectorcardiography versus myocardial perfusion SPECT in patients with suspected or known coronary heart disease)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligence is the subject of a report. According to news originating from Bad Oeynhausen, Germany, by NewsRx correspondents, research stated, "The present study evaluated with myocardial perfusion SPECT (MPS) the diagnostic accuracy of an artificial intelligenceenabled vectorcardiography system (Cardisiography, CSG) for detection of perfusion abnormalities. We studied 241 patients, 155 with suspected CAD and 86 with known CAD who were referred for MPS." Our news journalists obtained a quote from the research from the Institute of Radiology, "The CSG was performed after the MPS acquisition. The CSG results (1) p-factor (perfusion, 0: normal, 1: mildly, 2: moderately, 3: highly abnormal) and (2) s-factor (structure, categories as p-factor) were compared with the MPS scores. The CSG system was not trained during the study. Considering the p-factor alone, a specificity of >78% and a negative predictive value of mostly >90% for all MPS variables were found. The sensitivities ranged from 17 to 56%, the positive predictive values from 4 to 38%. Combining the pand the s-factor, significantly higher specificity values of about 90% were reached. The s-factor showed a significant correlation (p=0.006) with the MPS ejection fraction. The CSG system is able to exclude relevant perfusion abnormalities in patients with suspected or known CAD with a specificity and a negative predictive value of about 90% combining the p- and the s-factor."

Key words

Bad Oeynhausen/Germany/Europe/Artificial Intelligence/Cardiology/Cardiovascular Diseases and Conditions/Diagnostics and Screening/Emerging Technologies/Health and Medicine/Heart Disease/Heart Disorders and Diseases/Machine Learning

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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