Logo image
Back
Cost effective fabrication of ultrarefractory ceramic-ceramic composites by transient plastic phase processing
Conference proceeding

Cost effective fabrication of ultrarefractory ceramic-ceramic composites by transient plastic phase processing

D Brodkin, M Barsoum, A Zavaliangos and S Kalidindi
PROCEEDINGS OF THE 1995 NSF DESIGN AND MANUFACTURING GRANTEES CONFERENCE, v 37(11), pp 519-520
01 Jan 1994
DOI: https://doi.org/10.1016/j.cjca.2021.08.005
pdf
1994-Brodkin-CostEffectiveFabrication-SME4.40 MB
Published, Version of Record (VoR)login to download (DU) or request access (others) Restricted Access

Abstract

Engineering, Manufacturing Materials Science, Multidisciplinary Operations Research & Management Science Science & Technology Engineering Materials Science Mechanics Technology
Background: Current electrocardiogram analysis algorithms cannot predict the presence of coronary artery disease (CAD), especially in stable patients. This study assessed the ability of an artificial intelligence algorithm (ECGio; HEARTio Inc, Pittsburgh, PA) to predict the presence, location, and severity of coronary artery lesions in an unselected stable patient population. Methods: A cohort of 1659 stable outpatients was randomly divided into training (86%) and validation (14%) subsets, maintaining population characteristics. ECGio was trained and validated using electrocardiograms paired with retrospectively collected angiograms. Coronary artery lesions were classified in 2 analyses. The primary classification was no to mild (< 30% diameter stenosis [DS]) vs moderate (30%-70% DS) vs severe (>= 70% DS) CAD. The secondary classification was yes/no based on >= 50% DS in any vessel. Results: In the primary analysis, 22 patients had no angiographic CAD and were grouped mild CAD (56 patients, DS < 30%), 31 had moderate CAD (DS 30%-70%), and 113 had severe CAD (DS >= 70%). Weighted average sensitivity was 93.2%, and weighted average specificity was 96.4%. In the secondary analysis, 93 had significant CAD, and 128 did not. There was sensitivity of 93.1% and specificity of 85.6% in determining the presence of clinically significant disease (>= 50% DS) in any vessel. ECGio was able to predict stenosis with average vessel error in the left anterior descending coronary artery of 18%, the left circumflex coronary artery of 19%, the right coronary artery of 18%, and the left main coronary artery of 8%. Conclusions: This study strongly suggests that it is possible to use an artificial intelligence algorithm to determine the presence and severity of CAD in stable patients, using data from a 12-lead electrocardiogram.

Metrics

13 Record Views

Details

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Web of Science research areas
Engineering, Manufacturing
Materials Science, Multidisciplinary
Mechanics
Operations Research & Management Science
Logo image