The diagnostic value of electrocardiogram-based machine learning in long QT syndrome: a systematic review and meta-analysis

Wu, MF; Wang, Wen-Qin; Zhang, Wei; Li, Junhua; Zhang, Xingwei

doi:10.3389/fcvm.2023.1172451

Cited by 2 publications

(1 citation statement)

References 51 publications

(87 reference statements)

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“…The utility of AI and ML in diagnosing other cardiac conditions strengthens the case for using AI models in the diagnosis and prognosis of BrS. AI and ML have helped characterize different types of heart failure with preserved ejection fraction [32] , AI-enabled ECG-based screening tool for the diagnosis of left ventricular systolic dysfunction [33] , and prediction of atrial fibrillation [34] . ECG-based ML algorithms are being used for the diagnosis of other inherited arrhythmias, such as long QT syndrome (LQTS) [35] .…”

Section: Ai In the Diagnosis Of Other Cardiac Diseasesmentioning

confidence: 94%

Artificial intelligence streamlines diagnosis and assessment of prognosis in Brugada syndrome: a systematic review and meta-analysis

Leong,

Sharma,

Seth

et al. 2024

Conn Health Telemed

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Aim: The objective of this systematic review and meta-analysis was to determine the diagnostic and prognostic utility of artificial intelligence/machine learning (AI/ML) algorithms in Brugada Syndrome (BrS). Methods: A systematic review and meta-analysis of the literature was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. MEDLINE, EMBASE, SCOPUS, and WEB OF SCIENCE databases were searched for relevant articles. Abstract and title screening, full-text review, and data extraction were conducted independently by two of the authors. Conflicts were resolved via discussion among authors. A risk-of-bias assessment was performed using the QUADAS-2 tool for diagnostic studies and the PROBAST tool for prognostic studies. Forest plots and the summary area under the receiver operating characteristic (SAUROC) curve were done in R. Results: A total of 12 papers were included in our study. Among the best-performing diagnostic algorithms from each study, the sensitivity and specificity ranged from 0.80 to 0.89 and 0.74 to 0.97, respectively. In overall studies, sensitivity was 0.845 ± 0.014 and specificity was 0.892 ± 0.062 using a random effects model. A pooled analysis of the summary area under the receiver operating characteristic curve (SAUROC) was 0.77 for diagnostic studies. Prognostic studies showed good performance as well, with the AUC of the best-performing prognostic algorithms ranging from 0.71 to 0.90. Conclusions: Overall, AI/ML algorithms had high diagnostic and prognostic accuracy. These results highlight the potential of AI/ML algorithms for the diagnosis and prognosis of BrS and permit a choice of the best-performing ML algorithms.

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Section: Ai In the Diagnosis Of Other Cardiac Diseasesmentioning

confidence: 94%

Artificial intelligence streamlines diagnosis and assessment of prognosis in Brugada syndrome: a systematic review and meta-analysis

Leong,

Sharma,

Seth

et al. 2024

Conn Health Telemed

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Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis

Zhao,

Lan,

Yin

et al. 2023

BMC Med Imaging

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Background The gold standard to diagnose fatty liver is pathology. Recently, image-based artificial intelligence (AI) has been found to have high diagnostic performance. We systematically reviewed studies of image-based AI in the diagnosis of fatty liver. Methods We searched the Cochrane Library, Pubmed, Embase and assessed the quality of included studies by QUADAS-AI. The pooled sensitivity, specificity, negative likelihood ratio (NLR), positive likelihood ratio (PLR), and diagnostic odds ratio (DOR) were calculated using a random effects model. Summary receiver operating characteristic curves (SROC) were generated to identify the diagnostic accuracy of AI models. Results 15 studies were selected in our meta-analysis. Pooled sensitivity and specificity were 92% (95% CI: 90–93%) and 94% (95% CI: 93–96%), PLR and NLR were 12.67 (95% CI: 7.65–20.98) and 0.09 (95% CI: 0.06–0.13), DOR was 182.36 (95% CI: 94.85-350.61). After subgroup analysis by AI algorithm (conventional machine learning/deep learning), region, reference (US, MRI or pathology), imaging techniques (MRI or US) and transfer learning, the model also demonstrated acceptable diagnostic efficacy. Conclusion AI has satisfactory performance in the diagnosis of fatty liver by medical imaging. The integration of AI into imaging devices may produce effective diagnostic tools, but more high-quality studies are needed for further evaluation.

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The diagnostic value of electrocardiogram-based machine learning in long QT syndrome: a systematic review and meta-analysis

Cited by 2 publications

References 51 publications

Artificial intelligence streamlines diagnosis and assessment of prognosis in Brugada syndrome: a systematic review and meta-analysis

Artificial intelligence streamlines diagnosis and assessment of prognosis in Brugada syndrome: a systematic review and meta-analysis

Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis

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