Next Article in Journal
Surgical Strategy to Decrease the Revision Rate of Fassier–Duval Nailing in the Lower Limbs of Osteogenesis Imperfecta
Next Article in Special Issue
Predictive Power for Thrombus Detection after Atrial Appendage Closure: Machine Learning vs. Classical Methods
Previous Article in Journal
Personalized Medicine Approach in a DCM Patient with LMNA Mutation Reveals Dysregulation of mTOR Signaling
Previous Article in Special Issue
CardioNet: Automatic Semantic Segmentation to Calculate the Cardiothoracic Ratio for Cardiomegaly and Other Chest Diseases
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram

1
Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
2
Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 114, Taiwan
3
School of Medicine, National Defense Medical Center, Taipei 114, Taiwan
4
School of Public Health, National Defense Medical Center, Taipei 114, Taiwan
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2022, 12(7), 1150; https://doi.org/10.3390/jpm12071150
Submission received: 4 June 2022 / Revised: 2 July 2022 / Accepted: 13 July 2022 / Published: 15 July 2022
(This article belongs to the Special Issue The Challenges and Prospects in Cardiology)

Abstract

(1) Background: Acute pericarditis is often confused with ST-segment elevation myocardial infarction (STEMI) among patients presenting with acute chest pain in the emergency department (ED). Since a deep learning model (DLM) has been validated to accurately identify STEMI cases via 12-lead electrocardiogram (ECG), this study aimed to develop another DLM for the detection of acute pericarditis in the ED. (2) Methods: This study included 128 ECGs from patients with acute pericarditis and 66,633 ECGs from patients visiting the ED between 1 January 2010 and 31 December 2020. The ECGs were randomly allocated based on patients to the training, tuning, and validation sets, at a 3:1:1 ratio. We used raw ECG signals to train a pericarditis-DLM and used traditional ECG features to train a machine learning model. A human–machine competition was conducted using a subset of the validation set, and the performance of the Philips automatic algorithm was also compared. STEMI cases in the validation set were extracted to analyze the DLM ability of differential diagnosis between acute pericarditis and STEMI using ECG. We also followed the hospitalization events in non-pericarditis cases to explore the meaning of false-positive predictions. (3) Results: The pericarditis-DLM exceeded the performance of all participating human experts and algorithms based on traditional ECG features in the human–machine competition. In the validation set, the pericarditis-DLM could detect acute pericarditis with an area under the receiver operating characteristic curve (AUC) of 0.954, a sensitivity of 78.9%, and a specificity of 97.7%. However, our pericarditis-DLM also misinterpreted 10.2% of STEMI ECGs as pericarditis cases. Therefore, we generated an integrating strategy combining pericarditis-DLM and a previously developed STEMI-DLM, which provided a sensitivity of 73.7% and specificity of 99.4%, to identify acute pericarditis in patients with chest pains. Compared to the true-negative cases, patients with false-positive results using this strategy were associated with higher risk of hospitalization within 3 days due to cardiac disorders (hazard ratio (HR): 8.09; 95% confidence interval (CI): 3.99 to 16.39). (4) Conclusions: The AI-enhanced algorithm may be a powerful tool to assist clinicians in the early detection of acute pericarditis and differentiate it from STEMI using 12-lead ECGs.
Keywords: artificial intelligence; electrocardiogram; deep learning model; acute pericarditis; ST-segment elevation myocardial infarction artificial intelligence; electrocardiogram; deep learning model; acute pericarditis; ST-segment elevation myocardial infarction

Share and Cite

MDPI and ACS Style

Liu, Y.-L.; Lin, C.-S.; Cheng, C.-C.; Lin, C. A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram. J. Pers. Med. 2022, 12, 1150. https://doi.org/10.3390/jpm12071150

AMA Style

Liu Y-L, Lin C-S, Cheng C-C, Lin C. A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram. Journal of Personalized Medicine. 2022; 12(7):1150. https://doi.org/10.3390/jpm12071150

Chicago/Turabian Style

Liu, Yu-Lan, Chin-Sheng Lin, Cheng-Chung Cheng, and Chin Lin. 2022. "A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram" Journal of Personalized Medicine 12, no. 7: 1150. https://doi.org/10.3390/jpm12071150

APA Style

Liu, Y.-L., Lin, C.-S., Cheng, C.-C., & Lin, C. (2022). A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram. Journal of Personalized Medicine, 12(7), 1150. https://doi.org/10.3390/jpm12071150

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop