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Published on: December 11, 2019
Detection of Right and Left Ventricular Dysfunction in Pediatric Patients Using Artificial Intelligence-Enabled ECGs
Scott Anjewierden1, Donnchadh O'Sullivan1, Kathryn E Mangold2
1Department of Pediatrics and Adolescent Medicine Mayo Clinic Rochester MN USA.
Insights
Artificial intelligence can now detect left and right ventricular systolic dysfunction in children using ECGs. Pediatric-specific AI models show improved accuracy for early detection of these heart conditions in young patients.
Area of Science:
- Pediatric cardiology
- Artificial intelligence in medicine
- Electrocardiography
Background:
- Early detection of left and right ventricular systolic dysfunction (LVSD and RVSD) in children is crucial for reducing morbidity and mortality.
- Existing AI algorithms for LVSD/RVSD detection are validated in adults, but their efficacy in pediatric populations is uncertain.
Purpose of the Study:
- To develop novel artificial intelligence-enabled ECG algorithms for detecting LVSD and RVSD in pediatric patients.
- To evaluate the performance of these pediatric-specific models compared to adult-derived models.
Main Methods:
- Utilized a dataset of 10,142 pediatric patients (age ≤18) with 12-lead ECGs and transthoracic echocardiograms.
- Developed novel AI models for detecting LVSD (defined by LVEF thresholds) and RVSD.
- Compared performance metrics (AUC, sensitivity, specificity, PPV, NPV) of pediatric models against adult-derived models.
Main Results:
- Novel pediatric models achieved excellent test areas under the curve (AUC): 0.93 for LVEF ≤35% and 0.88 for LVEF <50%.
- The pediatric model for LVEF <50% demonstrated high sensitivity (0.85) and specificity (0.80), with a negative predictive value of 0.995.
- A pediatric model for RVSD detection reached an AUC of 0.90.
- Adult-derived models showed good performance (AUC 0.87 for LVEF ≤35%) but were outperformed by pediatric-specific models.
Conclusions:
- AI-enabled ECG analysis accurately detects both LVSD and RVSD in pediatric patients.
- Pediatric-specific AI models offer superior performance compared to adult-trained models for diagnosing these conditions in children.
- These findings support the clinical utility of AI-ECG for early identification of pediatric heart dysfunction.
Background:
Early detection of left and right ventricular systolic dysfunction (LVSD and RVSD respectively) in children can lead to intervention to reduce morbidity and death. Existing artificial intelligence algorithms can identify LVSD and RVSD in adults using a 12-lead ECG; however, its efficacy in children is uncertain. We aimed to develop novel artificial intelligence-enabled ECG algorithms for LVSD and RVSD detection in pediatric patients.
Methods And Results:
We identified 10 142 unique pediatric patients (age≤18) with a 10-second, 12-lead surface ECG within 14 days of a transthoracic echocardiogram, performed between 2002 and 2022. LVSD was defined quantitatively by left ventricular ejection fraction (LVEF). RVSD was defined semiquantitatively. Novel pediatric models for LVEF ≤35% and LVEF <50% achieved excellent test areas under the curve of 0.93 (95% CI, 0.89-0.98) and 0.88 (95% CI, 0.83-0.94) respectively. The model to detect LVEF <50% had a sensitivity of 0.85, specificity of 0.80, positive predictive value of 0.095, and negative predictive value of 0.995. In comparison, the previously validated adult data-derived model for LVEF <35% achieved an area under the curve of 0.87 (95% CI, 0.84-0.90) for LVEF ≤35% in children. A novel pediatric model for any RVSD detection reached a test area under the curve of 0.90 (0.87-0.94).
Conclusions:
An artificial intelligence-enabled ECG demonstrates accurate detection of both LVSD and RVSD in pediatric patients. While adult-trained models offer good performance, improvements are seen when training pediatric-specific models.
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