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Artificial intelligence to detect abnormal heart rhythm from scanned electrocardiogram tracings

BACKGROUND: Electrocardiogram (ECG) interpretation is an integral part of the clinical ECG workflow; however, this process is often time‐consuming and labor‐intensive. We aim to develop a rapid, inexpensive means to detect abnormal ECGs using artificial intelligence (AI) from scanned ECG printouts....

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Autores principales: Bridge, Joshua, Fu, Lu, Lin, Weidong, Xue, Yumei, Lip, Gregory Y. H., Zheng, Yalin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9237304/
https://www.ncbi.nlm.nih.gov/pubmed/35785392
http://dx.doi.org/10.1002/joa3.12707
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author Bridge, Joshua
Fu, Lu
Lin, Weidong
Xue, Yumei
Lip, Gregory Y. H.
Zheng, Yalin
author_facet Bridge, Joshua
Fu, Lu
Lin, Weidong
Xue, Yumei
Lip, Gregory Y. H.
Zheng, Yalin
author_sort Bridge, Joshua
collection PubMed
description BACKGROUND: Electrocardiogram (ECG) interpretation is an integral part of the clinical ECG workflow; however, this process is often time‐consuming and labor‐intensive. We aim to develop a rapid, inexpensive means to detect abnormal ECGs using artificial intelligence (AI) from scanned ECG printouts. METHODS: The study included 1172 12‐lead ECG scans performed in 1172 individuals from a community in Guangzhou, China; 878 (74.9%) were diagnosed with sinus rhythm, and the remaining 294 (25.1%) with abnormal rhythms. A deep learning model consisting of a convolutional neural network based on InceptionV3 and a fully connected layer followed by a GEV activation was trained to classify scanned tracings as either normal or abnormal. RESULTS: In a hold‐out testing set, the model achieved a area under curve (AUC), sensitivity, specificity, PPV, and NPV of 0.932 (95% confidence interval [CI]: 0.890, 0.976), 0.816 (95% CI: 0.657, 0.923), 0.993 (95% CI: 0.959, 1.0), 0.969 (95% CI: 0.838, 0.999), and 0.950 (95% CI: 0.90, 0.980) respectively, when using a probability threshold of 0.5. When compared with a physiological expert, these results show comparable performance with a statistically significant increase in specificity and a non‐significant decrease in sensitivity at the 95% level. CONCLUSIONS: We have developed a rapid, inexpensive, accurate means to detect abnormal ECGs using AI. Easy and accurate identification of such “abnormal” ECGs could allow the mass automated review of ECGs in community settings where abnormal ones could be flagged using AI for detailed clinical review by healthcare professionals.
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spelling pubmed-92373042022-06-30 Artificial intelligence to detect abnormal heart rhythm from scanned electrocardiogram tracings Bridge, Joshua Fu, Lu Lin, Weidong Xue, Yumei Lip, Gregory Y. H. Zheng, Yalin J Arrhythm Original Articles BACKGROUND: Electrocardiogram (ECG) interpretation is an integral part of the clinical ECG workflow; however, this process is often time‐consuming and labor‐intensive. We aim to develop a rapid, inexpensive means to detect abnormal ECGs using artificial intelligence (AI) from scanned ECG printouts. METHODS: The study included 1172 12‐lead ECG scans performed in 1172 individuals from a community in Guangzhou, China; 878 (74.9%) were diagnosed with sinus rhythm, and the remaining 294 (25.1%) with abnormal rhythms. A deep learning model consisting of a convolutional neural network based on InceptionV3 and a fully connected layer followed by a GEV activation was trained to classify scanned tracings as either normal or abnormal. RESULTS: In a hold‐out testing set, the model achieved a area under curve (AUC), sensitivity, specificity, PPV, and NPV of 0.932 (95% confidence interval [CI]: 0.890, 0.976), 0.816 (95% CI: 0.657, 0.923), 0.993 (95% CI: 0.959, 1.0), 0.969 (95% CI: 0.838, 0.999), and 0.950 (95% CI: 0.90, 0.980) respectively, when using a probability threshold of 0.5. When compared with a physiological expert, these results show comparable performance with a statistically significant increase in specificity and a non‐significant decrease in sensitivity at the 95% level. CONCLUSIONS: We have developed a rapid, inexpensive, accurate means to detect abnormal ECGs using AI. Easy and accurate identification of such “abnormal” ECGs could allow the mass automated review of ECGs in community settings where abnormal ones could be flagged using AI for detailed clinical review by healthcare professionals. John Wiley and Sons Inc. 2022-03-29 /pmc/articles/PMC9237304/ /pubmed/35785392 http://dx.doi.org/10.1002/joa3.12707 Text en © 2022 The Authors. Journal of Arrhythmia published by John Wiley & Sons Australia, Ltd on behalf of the Japanese Heart Rhythm Society. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Original Articles
Bridge, Joshua
Fu, Lu
Lin, Weidong
Xue, Yumei
Lip, Gregory Y. H.
Zheng, Yalin
Artificial intelligence to detect abnormal heart rhythm from scanned electrocardiogram tracings
title Artificial intelligence to detect abnormal heart rhythm from scanned electrocardiogram tracings
title_full Artificial intelligence to detect abnormal heart rhythm from scanned electrocardiogram tracings
title_fullStr Artificial intelligence to detect abnormal heart rhythm from scanned electrocardiogram tracings
title_full_unstemmed Artificial intelligence to detect abnormal heart rhythm from scanned electrocardiogram tracings
title_short Artificial intelligence to detect abnormal heart rhythm from scanned electrocardiogram tracings
title_sort artificial intelligence to detect abnormal heart rhythm from scanned electrocardiogram tracings
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9237304/
https://www.ncbi.nlm.nih.gov/pubmed/35785392
http://dx.doi.org/10.1002/joa3.12707
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