Li, Xin-Mu, Gao, Xin-Yi, Tse, Gary, Hong, Shen-Da, Chen, Kang-Yin, Li, Guang-Ping, Liu, Tong (2022) Electrocardiogram-based artificial intelligence for the diagnosis of heart failure: a systematic review and meta-analysis. Journal of Geriatric Cardiology, 19 (12). pp. 970-980. ISSN 1671-5411. (doi:10.11909/j.issn.1671-5411.2022.12.002) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:99708)
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Official URL: https://doi.org/10.11909/j.issn.1671-5411.2022.12.... |
Abstract
The electrocardiogram (ECG) is an inexpensive and easily accessible investigation for the diagnosis of cardiovascular diseases including heart failure (HF). The application of artificial intelligence (AI) has contributed to clinical practice in terms of aiding diagnosis, prognosis, risk stratification and guiding clinical management. The aim of this study is to systematically review and perform a meta-analysis of published studies on the application of AI for HF detection based on the ECG. We searched Embase, PubMed and Web of Science databases to identify literature using AI for HF detection based on ECG data. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria. Random-effects models were used for calculating the effect estimates and hierarchical receiver operating characteristic curves were plotted. Subgroup analysis was performed. Heterogeneity and the risk of bias were also assessed. A total of 11 studies including 104,737 subjects were included. The area under the curve for HF diagnosis was 0.986, with a corresponding pooled sensitivity of 0.95 (95% CI: 0.86-0.98), specificity of 0.98 (95% CI: 0.95-0.99) and diagnostic odds ratio of 831.51 (95% CI: 127.85-5407.74). In the patient selection domain of QUADAS-2, eight studies were designated as high risk. According to the available evidence, the incorporation of AI can aid the diagnosis of HF. However, there is heterogeneity among machine learning algorithms and improvements are required in terms of quality and study design.
Item Type: | Article |
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DOI/Identification number: | 10.11909/j.issn.1671-5411.2022.12.002 |
Subjects: | R Medicine |
Divisions: | Divisions > Division of Natural Sciences > Kent and Medway Medical School |
Funders: | National Natural Science Foundation of China (https://ror.org/01h0zpd94) |
SWORD Depositor: | JISC Publications Router |
Depositing User: | JISC Publications Router |
Date Deposited: | 27 Jan 2023 16:57 UTC |
Last Modified: | 05 Nov 2024 13:05 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/99708 (The current URI for this page, for reference purposes) |
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