Artificial Intelligence in Congenital Heart Disease: A Roadmap

Q: What is the clinical evidence from this study?

Study design: Review. Population: Congenital heart disease. Intervention: Artificial intelligence in cardiovascular imaging, wearable technologies, and clinical registries.

Rajkumar Dhar; Darpit Dave; Jacob Heiss; Kristopher Kormos; Animesh A Tandon

doi:10.14797/mdcvj.1921

January 1, 2026Methodist DeBakey Cardiovascular JournalOpen Access

Artificial Intelligence in Congenital Heart Disease: A Roadmap

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Key result

AI in congenital heart disease shows greatest maturity for imaging segmentation over wearables and registries.

Why the study?

Despite advances in congenital heart disease diagnosis and treatment, challenges remain in early detection, risk stratification, and long-term management. Artificial intelligence may enhance care by extracting actionable insights from complex clinical data, though barriers related to data quality, validation, interpretability, and implementation exist.

Does the integration of artificial intelligence across cardiovascular imaging, wearable technologies, and clinical registries improve diagnosis, monitoring, and outcome prediction in patients with congenital heart disease?

Key Points

To examine current and emerging applications of artificial intelligence across cardiovascular imaging, wearable technologies, and clinical registries to improve lifelong care in congenital heart disease.
Narrative review of artificial intelligence applications across cardiovascular imaging, wearable sensors, and multicenter clinical registries in congenital heart disease.
Artificial intelligence applications are currently most mature in imaging modalities such as echocardiography and cardiac magnetic resonance, whereas wearables provide continuous longitudinal physiological data but remain under-validated in congenital heart disease populations.
Multicenter surgical registries have implemented machine-learning models predicting mortality, prolonged ventilation, and length of stay with area under the curve values of 0.86, 0.85, and 0.82, respectively, though impact on prospective clinical decisions remains limited.
Translational challenges include fragmented evidence, single-center designs, under-representation of diverse anatomical phenotypes, and non-standardized registry data collection.

Structured PICO

Does the integration of artificial intelligence across cardiovascular imaging, wearable technologies, and clinical registries improve diagnosis, monitoring, and outcome prediction in patients with congenital heart disease?

P

Population

Pediatric and adult patients with congenital heart disease (CHD) across the lifespan

I

Intervention

Artificial intelligence (AI) applications across three domains: cardiovascular imaging (echocardiography, cardiac CT, CMR), wearable health technologies (smartwatches, ambulatory ECG patches, multiparametric sensors), and clinical registries

O

Outcome

Disease detection, automated anatomical and functional assessment, continuous outpatient physiologic monitoring, risk stratification, outcome prediction, and automated registry abstraction

Limitations

Small cohorts and single-center studies in current CHD AI literature
Under-representation of diverse and rare CHD phenotypes and complex surgical histories
Fragmented evidence across stages of care and lack of standardized data collection
Limited CHD-specific external validation for wearable technologies and automated registry abstraction
Potential for performance drift, automation bias, alert fatigue, and widening of healthcare disparities
Marked anatomical heterogeneity, smaller patient size, prior surgical reconstruction, and altered hemodynamics limit the generalizability of adult-trained AI models to CHD populations
Echocardiographic image quality and imaging views vary substantially across CHD patients and operators
Existing CHD registries typically focus on a particular stage of care, patient population, or data modality, fragmenting information across the patient journey
Much of the evidence for AI-guided acquisition, CT dose reduction, scan acceleration, and registry abstraction remains extrapolated from broader adult cardiovascular or oncology datasets without CHD-specific external validation

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Implication

This review outlines a translational roadmap for integrating artificial intelligence into congenital heart disease care via cardiovascular imaging, wearable sensors, and clinical registries, while emphasizing the critical need for CHD-specific validation to overcome anatomical heterogeneity and data fragmentation.

Abstract

Congenital heart disease (CHD) is a lifelong condition that requires ongoing monitoring due to its heterogeneous anatomy, physiology, and clinical course.Despite major advances in diagnosis and treatment, challenges remain in early detection, risk stratification, and long-term management.Artificial intelligence (AI) has the potential to enhance CHD care by extracting actionable insights from complex clinical data.This review examines current and emerging applications of AI in three key domains: cardiovascular imaging, wearable technologies, and clinical registries.We highlight opportunities to improve diagnosis, monitoring, and outcome prediction, while discussing important challenges related to data quality, validation, interpretability, and clinical implementation.Together, these areas illustrate both the promise and the barriers to integrating AI into CHD care.

Discussion

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Cite This Study

Dhar et al. (2026) conducted a review in Congenital heart disease. Artificial intelligence in cardiovascular imaging, wearable technologies, and clinical registries was evaluated. Artificial intelligence in congenital heart disease shows strongest maturity in imaging segmentation and detection, while wearables and automated registry abstraction require further validation.

synapsesocial.com/papers/6af5bf07e94012e8747ad98a https://doi.org/10.14797/mdcvj.1921

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Artificial Intelligence in Congenital Heart Disease: A Roadmap

Key Points

Structured PICO

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Artificial Intelligence in Congenital Heart Disease: A Roadmap

Key Points

Structured PICO

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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