Development of the AI-Cirrhosis-ECG Score: An Electrocardiogram-Based Deep Learning Model in Cirrhosis
Cirrhosis is associated with cardiac dysfunction and distinct electrocardiogram (ECG) abnormalities. This study aimed to develop a proof-of-concept deep learning-based artificial intelligence (AI) model that could detect cirrhosis-related signals on ECG and generate an AI-Cirrhosis-ECG (ACE) score t...
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Published in | The American journal of gastroenterology Vol. 117; no. 3; pp. 424 - 432 |
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Main Authors | , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Wolters Kluwer Health Medical Research, Lippincott Williams & Wilkins
01.03.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Cirrhosis is associated with cardiac dysfunction and distinct electrocardiogram (ECG) abnormalities. This study aimed to develop a proof-of-concept deep learning-based artificial intelligence (AI) model that could detect cirrhosis-related signals on ECG and generate an AI-Cirrhosis-ECG (ACE) score that would correlate with disease severity.
A review of Mayo Clinic's electronic health records identified 5,212 patients with advanced cirrhosis ≥18 years who underwent liver transplantation at the 3 Mayo Clinic transplant centers between 1988 and 2019. The patients were matched by age and sex in a 1:4 ratio to controls without liver disease and then divided into training, validation, and test sets using a 70%-10%-20% split. The primary outcome was the performance of the model in distinguishing patients with cirrhosis from controls using their ECGs. In addition, the association between the ACE score and the severity of patients' liver disease was assessed.
The model's area under the curve in the test set was 0.908 with 84.9% sensitivity and 83.2% specificity, and this performance remained consistent after additional matching for medical comorbidities. Significant elevations in the ACE scores were seen with increasing model for end-stage liver disease-sodium score. Longitudinal trends in the ACE scores before and after liver transplantation mirrored the progression and resolution of liver disease.
The ACE score, a deep learning model, can accurately discriminate ECGs from patients with and without cirrhosis. This novel relationship between AI-enabled ECG analysis and cirrhosis holds promise as the basis for future low-cost tools and applications in the care of patients with liver disease. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Author Contributions Douglas A. Simonetto devised the project and the main conceptual ideas for the study. Joseph C. Ahn, Puru Rattan, and Seth Buryska performed data extraction. Zachi I. Attia developed, validated, and tested the deep neural network. Joseph C. Ahn, Puru Rattan, and Aidan F. Mullan performed statistical analysis and generated tables and figures. All authors interpreted the results and Joseph C. Ahn drafted the manuscript. Alina M. Allen, Patrick S. Kamath, Paul A. Friedman, Vijay H. Shah, Peter A. Noseworthy, and Douglas A. Simonetto revised the manuscript critically for important intellectual content. All authors approved the final version to be published. Joseph C. Ahn and Zachi I. Attia contributed equally to this study. |
ISSN: | 0002-9270 1572-0241 |
DOI: | 10.14309/ajg.0000000000001617 |