AI-ECG in repaired Tetralogy of Fallot

Repaired Tetralogy of Fallot (rTOF) is a lifelong condition in which right ventricular outflow tract interventions and patching often lead to chronic pulmonary regurgitation. Over time, volume overload drives chamber dilation, electromechanical remodeling, and exercise intolerance, making the timing of pulmonary valve replacement a pivotal decision. An electrocardiogram is nearly universal in follow up and embeds subtle signatures of ventricular size, conduction, and dyssynchrony. Using artificial intelligence to decode these signatures into a clinically meaningful probability of approaching intervention offers a scalable signal for routine surveillance. If well calibrated, an ECG-first estimate could flag patients who warrant expedited imaging, closer rhythm monitoring, or earlier multidisciplinary review.

Clinical problem and current decision thresholds

Contemporary practice triangulates patient symptoms, exercise capacity, ventricular size by cardiac MRI, and conduction changes such as QRS duration when considering timing of pulmonary valve replacement. MRI provides volumetric accuracy, but availability, cost, and scheduling can limit frequency, especially in younger patients or those requiring anesthesia. ECG signals are easier to obtain, yet the granularity of conventional metrics may miss early or nonlinear shifts in remodeling. Centers vary in thresholds and weighting of data streams, producing practice heterogeneity and potential delays. A method that continuously updates risk using a ubiquitous test could reduce that variability if it integrates smoothly with established workflows.

Why an ECG-first AI could help

AI models can capture higher dimensional relationships among intervals, axes, morphologies, and subtle waveform dynamics that exceed human pattern recognition. For rTOF care, those patterns may reflect right ventricular remodeling, dyssynchrony, or atrial and His-Purkinje adaptations that accrue well before symptom shifts. An ECG-first model can be applied at every clinic visit or remotely, enabling earlier, data-driven risk stratification. If the output is presented as a probability with confidence intervals or clear risk bands, clinicians can align the signal with thresholds for imaging or case conference review. The potential is not to replace imaging but to prioritize it, smoothing care pathways and conserving resources while maintaining safety.

What the new tool adds

The newly reported AI-enabled ECG approach was developed to guide pulmonary valve replacement timing in rTOF and was evaluated with internal and external cohorts. According to the PubMed summary, performance appeared clinically useful, and the tool is framed as an adjunct to MRI- and rhythm-based assessment rather than a stand-alone decision maker (PubMed). The proposed mapping from ECG to a probability of being near intervention creates a common language for teams deciding whether to expedite imaging, intensify surveillance, or hold course. Importantly, model explainability and calibration curves can help clinicians judge plausibility and stability across patient subgroups. Evidence of transportability across centers and acquisition systems is equally important because ECG hardware, filters, and lead placement inconsistencies can shift inputs in real-world practice.

Interpreting outputs and avoiding misuse

Any probabilistic output must be integrated with the clinical picture, not used in isolation. High-risk flags should prompt appropriate next steps, such as confirmatory imaging, broader arrhythmia surveillance, or multidisciplinary discussion, depending on local pathways. Low-risk outputs should not defer imaging when symptoms, exercise testing, or biomarker trends are worrisome. False positives can be mitigated with tiered thresholds that trade sensitivity and specificity to suit surveillance versus preoperative contexts. Clear documentation, guardrails, and measurement of real-world reclassification compared with usual care help prevent drift toward overuse or underuse of downstream testing.

Implementation and workflow

Integration with imaging and rhythm surveillance

Practical deployment starts with embedding the model where ECGs are acquired and reviewed, with outputs routed to the congenital clinic dashboard. Many centers can map probability bands to predefined actions, for example, scheduling MRI within a set timeframe or bringing cases to a surgical conference when thresholds are met. Standardized notes can record the model score, calibration context, and the subsequent clinical decision, enabling auditability. Linking outputs with ambulatory monitoring, device diagnostics, or stress testing can enrich interpretation, particularly for patients with frequent ectopy or intermittent atrial arrhythmias. Teams should predefine action thresholds with cardiology, imaging, and surgical partners to ensure consistent responses to similar outputs.

EHR integration and clinical decision support

At the point of care, outputs should appear inside the electronic health record with data lineage, versioning, and interpretive notes. Embedding logic as clinical decision support allows triggers such as prior MRI volumes, change over time, and symptom updates to adjust recommendations. Human factors matter: busy clinicians need concise, actionable displays that minimize clicks, highlight trajectory, and avoid alarm fatigue. Dashboards that trend risk over time, with overlays of imaging dates and key events, help clinicians judge momentum rather than reacting to a single score. Governance should specify who acknowledges alerts, how disagreements are resolved, and what constitutes a closed-loop response.

Equity, generalizability, and external validation

Performance across age groups, body sizes, conduction variants, repair types, and comorbidity profiles must be demonstrated with rigorous external validation. ECG-device heterogeneity, lead placement variability, and site-specific preprocessing can challenge transportability if training data are narrow. Institutions should monitor calibration-in-the-large and subgroup metrics, looking for drift that signals dataset shift or workflow changes. Equity audits can examine access and downstream actions to ensure the tool does not inadvertently widen disparities in imaging or surgical referral. Shared evaluation frameworks across centers can accelerate learning while avoiding needless duplication of effort.

Regulatory and quality management considerations

Software intended to inform interventions will typically fall under medical device oversight, necessitating documented development, verification, validation, and postmarket surveillance. Sites should maintain a quality management file with model versioning, training lineage, and performance testing that mirrors local ECG devices and populations. Model updates require change management, including revalidation, stakeholder communication, and updated labeling or education. Clinicians should be able to see when the algorithm or thresholds changed and whether performance has shifted. Finally, institutions can designate a clinical safety officer or oversight committee to adjudicate issues, track unintended consequences, and coordinate corrective actions.

Research agenda and outlook

Designing prospective impact trials

Retrospective and cross sectional performance is a start, but what matters to patients is whether AI-enabled ECG guidance improves outcomes or resource use. Pragmatic cluster trials or stepped wedge designs can test whether adding the tool to routine care safely increases timely imaging, optimizes surgical timing, and reduces avoidable deterioration or admissions. A pre-specified analytic plan should measure reclassification against usual care and document balances between early and deferred surgery. Health economic analyses can estimate cost per correctly timed intervention, accounting for additional imaging, visits, and potential false alarms. Pre-registration, data sharing, and reproducible code promote credibility and translational value.

Biomarkers, thresholds, and patient-centered outcomes

Harmonizing AI-ECG outputs with biomarkers, exercise testing, and imaging creates a richer decision framework than any single modality. Over time, probabilistic outputs can be validated against longitudinal changes in MRI volumes, exercise capacity, and arrhythmia burden to refine thresholds. Explicit biomarker validation strengthens claims of clinical utility by linking predictions to biological change and meaningful endpoints. Patient-centered outcomes, including quality of life and activity measures, should be included to ensure that timing decisions align with what matters to patients and families. Transparent reporting of harms, such as unnecessary imaging or anxiety from elevated scores, is essential to balanced adoption.

Data stewardship and transparency

High-quality ECG datasets with verified labels, consistent preprocessing, and detailed metadata underpin trustworthy models. Sites can contribute deidentified ECG-imaging pairs to federated frameworks that respect privacy while enabling robust training and fairness checks. Documentation should describe inclusion criteria, missingness handling, and performance variability, with caution against extrapolating beyond the training domain. Interpretability tools can highlight waveform segments driving predictions, offering clinicians a way to assess plausibility in context. Governance that includes patients, electrophysiologists, imagers, and surgeons supports responsible use and continuous improvement.

What to watch next

Key next steps include prospective evaluation in diverse rTOF populations, head to head comparisons with existing pathways, and assessment of cost and access implications. The most compelling evidence will show better patient trajectories and more consistent timing decisions with equal or fewer resources. If validated, AI-ECG could help standardize surveillance intervals, reduce intercenter variation, and raise a flag before irreversible remodeling closes the window for optimal outcomes. Balanced guardrails and clear documentation can protect against misuse while enabling scale. With careful implementation, this line of work can bring more timely and equitable care to adults and adolescents living with repaired Tetralogy of Fallot.