Why AI ECG for repaired tetralogy of Fallot matters

Tetralogy of Fallot survivors live with lifelong risks of right ventricular dilation, arrhythmia, and late heart failure after initial repair. In this setting, an artificial intelligence-enabled electrocardiogram can surface latent electrical signatures correlated with structural disease, offering a frictionless signal for surveillance. The proposed AI-ECG model targets timing of pulmonary valve replacement by estimating the risk that remodeling and clinical trajectory warrant intervention. For clinicians, the promise lies in triaging who needs definitive imaging sooner and who can safely continue routine follow-up. For patients, the approach is appealing because a standard ECG can be captured anywhere, at low cost, and without contrast or sedation.

Clinical context and unmet need

Follow-up after repair is anchored to symptom assessment, periodic imaging, and vigilance for arrhythmic events. Many centers prioritize cardiac MRI to quantify right ventricular volumes and pulmonary regurgitation, but access, cost, and patient burden can limit cadence. Conversely, timing intervention too late risks fibrosis, ventricular-arterial uncoupling, and heart failure, while intervening too early may expose patients to repeat procedures without outcome benefit. An ECG-derived risk estimate addresses this gap by enabling more granular sampling between imaging visits using the ubiquitous clinic or ambulatory ECG. Such a signal is complementary, not competitive, with imaging, and could rationalize pathways by identifying patients who most need near-term cross-sectional assessment.

From QRS duration to learned ECG features

QRS duration has long served as a pragmatic marker in repaired tetralogy of Fallot, flagging risk of ventricular arrhythmia and progressive dilation. Yet QRS alone compresses a high-dimensional signal into a single interval, missing rich morphology and interlead interactions. Deep learning models can learn multiscale features from raw waveforms that reflect conduction heterogeneity, repolarization dispersion, and electromechanical delay. By moving beyond a single QRS duration threshold, an AI-ECG can embed a composite phenotype of remodeling that is not immediately visible to human readers. This has proven value in other domains, including left ventricular dysfunction and hypertrophic patterns, and now appears applicable to right-sided disease biology.

What the AI-ECG model adds

The reported work links ECG patterns with downstream structural and clinical outcomes relevant to pulmonary valve replacement decisions, creating a candidate biomarker for longitudinal surveillance. A learned score could track with right ventricular volumes, pulmonary regurgitant burden, or markers of right ventricular remodeling, while also correlating with arrhythmia risk and exercise capacity. The ability to estimate intervention-relevant risk from a routine ECG can help prioritize which patients warrant earlier imaging, assessment for catheter or surgical options, or closer Holter monitoring. Importantly, the model is not a replacement for physiology or shared decision-making; it is an adjunct signal that can sharpen timing. When embedded in teams and pathways, the net intent is higher-value care, not algorithmic determinism.

For a complex congenital population, this noninvasive signal could also reduce time-to-action when resources are constrained or scheduling delays arise. Where MRI is deferred due to anesthesia planning, logistics, or patient preference, an AI-ECG score can identify risk trajectories that should not wait. Conversely, in stable individuals with reassuring AI-ECG signals, routine intervals might be safely maintained in concert with guideline-based follow-up. The chief benefit is tighter coupling between electrophysiologic activity and the structural story unfolding in the right ventricle, without adding measurement burden. As learning systems mature, the fidelity of these associations is likely to improve with diverse training data.

Translating an AI-ECG score into clinical pathways

Implementing an AI-ECG tool in repaired tetralogy of Fallot care requires careful pathway design, clinical oversight, and explicit outcome targets. A typical use case begins in an outpatient congenital clinic, where the ECG is captured at the point of care and a score is returned within the visit workflow. If elevated, clinicians can expedite imaging, exercise testing, or extended monitoring, while low-risk outputs can support scheduled surveillance. This approach favors proactive management and operational efficiency, aligning capacity to those with greatest near-term need. It also creates coherent feedback loops as imaging and outcomes data update the care plan.

Use cases in surveillance and triage

A reasonable first deployment is risk triage to inform timing of cross-sectional imaging. Patients with borderline metrics or recent symptoms may benefit from early MRI if the AI-ECG suggests higher risk, whereas a reassuring score might support deferral until the standard window. For individuals with recurrent non-sustained ventricular tachycardia or palpitations, an elevated score could push for more granular rhythm evaluation to address arrhythmia risk. In the peri-intervention period, the same signal could help track remodeling response after valve replacement. Finally, when a patient misses imaging or relocates, ECG-based risk can provide continuity and prioritize reengagement.

To operationalize, centers could define action thresholds governing imaging intervals, multi-disciplinary review, or escalation to catheterization. Prospective registry capture would document how the score influences decisions, with explicit reasons for concordant or discordant actions. Feedback from congenital surgeons, electrophysiologists, and imagers ensures the score is interpreted in context and tempered by clinical nuance. Over time, the care team can calibrate thresholds based on service capacity, case mix, and local outcomes. Clear documentation templates and shared understanding of the score lessen variability and support equitable use.

Integration with cardiac MRI and biomarkers

No single signal should dominate timing for pulmonary valve replacement. The pragmatic approach is to align an ECG-derived score with MRI-derived volumes, regurgitant fraction, exercise capacity, and symptom trajectory. When the AI-ECG and imaging concur, decisions are straightforward. Discordance invites deeper review, including views on fibrosis burden, tricuspid or branch PA lesions, and exercise physiology. In select cases, advanced modalities and risk stratification tools such as CPET or natriuretic peptides can arbitrate.

Embedding AI-ECG in multi-signal dashboards can reduce cognitive load and document the rationale for action. In the EHR, decision pathways could display the longitudinal AI-ECG trajectory alongside MRI indices and clinical events. By linking these components, clinicians can recognize inflection points rather than single-visit snapshots. This orchestration is well suited to formal clinical decision support that offers suggestions without constraining judgement. Co-development with patients ensures that shared decisions remain central and that the value proposition is understood.

Operational considerations and governance

Reliable deployment depends on stable ECG acquisition, consistent preprocessing, and versioned model code with audit trails. Operational triggers should be explicit, with human-in-the-loop requirements and fallback plans for degraded performance. Governance should specify who can view scores, how alerts are triaged, and how often thresholds are revisited. Institutions must also track net benefit, resource utilization, and the balance between earlier intervention and procedure burden. Without these guardrails, even accurate algorithms can underperform in practice.

From an information security standpoint, locally hosted inference or approved cloud routes with deidentified waveforms may be necessary. Model provenance, change control, and bias monitoring should be formalized in the clinical AI lifecycle. Finally, economic evaluation is appropriate, capturing effects on imaging queues, cath lab utilization, and downstream outcomes. Teams should test whether the strategy improves time in target for right ventricular volumes and reduces adverse events, not just whether it predicts them well. These principles will generalize to other congenital contexts as AI-ECG tools proliferate.

Methodological and ethical guardrails for adoption

Performance metrics alone are not sufficient to green-light an AI-ECG for routine use. External benchmarking, transportability across devices and care settings, and lifecycle monitoring are essential. Investigators should provide details on training cohorts, label definitions, and how missingness was handled, given that outcomes such as intervention timing can be confounded by practice patterns. Pressure-testing robustness across age bands, repair types, and conduits increases confidence that the signal reflects biology rather than site-specific habits. In addition, calibration and decision-curve analysis clarify clinical utility beyond discrimination.

Generalizability, drift, and validation

Models trained on single-center or homogeneous cohorts often lose performance when exported. For repaired tetralogy of Fallot, differences in surgical era, conduit choice, and residual lesions can alter the ECG phenotype. A rigorous pathway includes multicenter external validation, assessment across common ECG hardware, and monitoring for drift as practices evolve. When shift is detected, proactive recalibration or transfer learning can restore performance. Transparent pre-specification of update schedules helps mitigate silent degradation.

Thresholds tuned to one clinic may not generalize to others with different MRI intervals or intervention thresholds. Decision support should therefore expose continuous scores, confidence intervals where available, and notify users when version or population changes occur. The aim is to empower clinicians to interrogate the signal rather than accept a binary classification. Local validation with prospectively collected outcomes provides final assurance prior to expansion. Clear reporting against TRIPOD-AI principles supports peer review and reproducibility.

Equity, transparency, and clinician oversight

ECG waveforms can vary with age, sex, and body habitus, and these factors may interact with right-sided pathology. Equity requires performance reporting across demographic strata and congenital subgroups, with remediations when gaps are identified. Explainability tools can show which waveform segments influence risk estimates, aiding clinician trust without overpromising mechanistic insight. Documentation should describe intended use, contraindications, and known failure modes in clear language. Most importantly, the clinical team retains authority to override algorithmic suggestions, particularly when values conflict with lived experience or patient goals.

Shared decision-making is central in congenital care, where trade-offs span immediate risk and long-term durability. AI tools should facilitate these discussions by framing risk in patient-centered terms, not drive them. Institutions ought to include patients and families in governance boards, particularly for topics such as alert frequency and user interface. Feedback loops that capture clinician and patient experience can reveal unanticipated friction, including alert fatigue. Over time, these insights help refine which outputs are visible and how they are presented at the point of care.

Designing prospective impact evaluations

After analytic validation, the key question becomes whether the AI-ECG improves outcomes. Pragmatic trials or stepped-wedge implementations can randomize clinics or weeks to AI-ECG augmented pathways versus standard care. Primary endpoints might include time to MRI in high-risk individuals, proportion meeting right ventricular volume targets at intervention, or composite adverse events. Process measures such as inappropriate imaging rates, clinic throughput, and admission prevention are also relevant. Health economic analysis should quantify net value considering procedure timing, resource use, and quality of life.

To avoid circularity, impact studies should predefine how scores will alter care, then measure whether those actions correlate with benefit. Clinician adherence, reasons for deviation, and event adjudication are necessary to interpret results. Signal transparency and immutable logging enhance scientific credibility and regulatory readiness. If positive, a staged rollout with post-market surveillance can maintain quality and catch rare failure modes. Ultimately, robust impact evidence will determine whether AI-ECG becomes a standard adjunct in congenital pathways.

In summary, an AI-enabled ECG signal for repaired tetralogy of Fallot aligns with a broader shift toward noninvasive, continuous assessment using ambient data streams. The approach promises earlier recognition of adverse remodeling, better triage for imaging, and more precise timing of pulmonary valve replacement, provided it is implemented with rigor. Limitations include potential confounding by practice patterns, device variability, and the need for comprehensive validation across diverse populations. Next steps include multicenter trials, economic evaluation, and careful integration into multidisciplinary decision-making. With these guardrails, AI-ECG could become a practical, equitable tool that improves outcomes while preserving clinician intuition and patient values.