AI-ECG to guide pulmonary valve timing in repaired TOF

Adults and adolescents with repaired tetralogy of Fallot commonly develop chronic pulmonary regurgitation, progressive right ventricular dilation, and patchy fibrosis that can culminate in symptoms, reduced exercise capacity, and arrhythmia. The clinical task is to time pulmonary valve replacement before irreversible remodeling while avoiding premature intervention. Contemporary practice relies on history, physical examination, transthoracic echocardiography, cardiopulmonary exercise testing, and periodic advanced imaging, particularly cardiac magnetic resonance. Many centers also track electrical markers, including QRS duration, recognizing their association with ventricular arrhythmias and adverse outcomes. Yet these data points can be noisy and asynchronous, and access to CMR may be limited or delayed, especially outside tertiary congenital programs.

In this context, AI models trained on routine ECGs offer a pragmatic adjunct. By learning subtle repolarization and conduction patterns that correlate with chamber size, loading conditions, and scar, an AI-enabled ECG may flag patients approaching imaging-derived thresholds or showing trajectories of adverse right ventricular remodeling. The report indexed on PubMed describes a strategy in which an ECG-based signal could prompt targeted imaging sooner, support prioritization in busy clinics, or trigger referral to adult congenital services. The premise is not to replace imaging or clinical judgment, but to enable earlier, risk-informed triage using a ubiquitous, low-cost test. The operational challenge is to define who gets flagged, what happens next, and how safety, equity, and performance are assured.

Why timing pulmonary valve replacement is hard

After Fallot repair, pulmonary regurgitation unloads the right ventricle in systole but chronically volume loads it in diastole. Over years, this can expand right ventricular volumes, increase wall stress, and promote fibrosis, ultimately impairing systolic function. Symptoms and exercise intolerance may lag behind structural change, and timing surgery late risks incomplete reverse remodeling. Conversely, intervening too early can expose patients to prosthetic valve complications and repeat procedures without clear benefit. Thus, a decision support layer that anticipates adverse trajectories could align follow-up intensity and intervention timing more precisely.

Clinical teams already use composite signals: symptoms, exercise data, echocardiographic surrogates, and CMR measurements of right ventricular size and function. Electrical markers, including QRS width and fragmentation, add incremental risk information but lack specificity for valve timing decisions. The appeal of an ECG-based AI is its potential to integrate complex waveform features beyond human perception, yielding a continuous probability that a patient meets or is trending toward criteria often confirmed by CMR. If calibrated properly, such a signal could act as a gatekeeper for imaging or as an early alert to shorten surveillance intervals, particularly in settings where imaging access is constrained.

What an AI-ECG might capture

Several physiologic perturbations leave an imprint on the ECG in repaired tetralogy of Fallot: conduction delay from prior ventriculotomy or right bundle injury, repolarization heterogeneity due to patchy scar, and secondary changes from right ventricular volume and pressure loading. A model trained using deep learning can ingest raw 12-lead signals and learn latent representations correlating with right ventricular size, pulmonary regurgitant burden, and strain. Unlike hand-engineered features, these representations may capture multi-lead interdependencies and non-linear motifs that scale with remodeling. The resulting output can be a calibrated probability that an individual is at or near imaging-based thresholds or is demonstrating an unfavorable trajectory. Importantly, this probability is not a diagnosis; it is a prioritization signal.

In practice, a calibrated AI-ECG could provide continuous risk estimates plotted over time, supporting trend-aware care rather than single thresholds. This aligns with how clinicians think about adverse right ventricular remodeling as a continuum. The model might perform best when trained on paired ECG-imaging datasets and regularly recalibrated, acknowledging shifts in local imaging protocols and surgical techniques across eras. Robust uncertainty estimates would help clinicians interpret the output in context, for example highlighting when the input ECG quality or rhythm limits confidence.

How AI-ECG could complement existing markers

Even in expert centers, decisions about pulmonary valve timing are often multi-dimensional and discussed in team conference. The strongest decisions integrate structural, functional, electrical, and patient-centered data. An ECG-based AI can add another layer by translating complex electrical patterns into an estimate that correlates with adverse remodeling or imaging-defined thresholds. This layer could refine the pretest probability before scheduling a CMR, making imaging queues more efficient. When the AI-ECG is persistently negative and clinical status is stable, clinicians might safely extend the interval between advanced imaging, always with oversight.

Importantly, this approach does not diminish the importance of QRS measurement or standard ECG interpretation. Rather, it reframes the 12-lead ECG as a richer signal that, when processed by AI, can augment classical markers. The value proposition is strongest where access to advanced imaging is limited, where waiting lists are long, or where patients travel far for specialty care. By focusing imaging resources on those most likely to benefit now, systems may reduce delays for individuals who are already nearing thresholds for pulmonary valve evaluation.

Translating an AI-ECG flag into practice

Clinicians will reasonably ask how to act on an AI-ECG flag in a way that is safe, equitable, and efficient. A practical pathway starts with clear thresholds for action that balance sensitivity and specificity, stratified by clinical context. For example, a moderate probability may trigger an earlier clinic review and echocardiogram, whereas a high probability may prompt expedited CMR or direct discussion at a multidisciplinary valve conference. Crucially, every action step should be mapped to a reversible, reviewable workflow that keeps the AI advisory and the clinician accountable. All steps must be documented in the electronic health record with the AI output and the rationale for decisions.

To avoid alert fatigue, AI-ECG outputs should be embedded where clinicians already review ECGs or longitudinal data, not as separate dashboards. Clear language matters: outputs labeled as probability of meeting imaging criteria for pulmonary valve evaluation are more actionable than opaque scores. Serial trends can be powerful, particularly for stable patients who may be drifting over time toward intervention; such trends support early counseling and resource planning. Integration with scheduling systems can allow automatic triage to imaging waitlists with clinician approval, ensuring those likely to be at threshold are seen first.

A triage algorithm for everyday clinics

A simple, clinician-led algorithm could unfold as follows. First, obtain a standard 12-lead ECG at routine follow-up, run the AI analysis, and record the probability with a confidence estimate. Second, if the probability exceeds a prespecified high threshold, expedite CMR or specialist review for pulmonary valve assessment; if intermediate, bring forward the next visit and echocardiogram; if low and stable, maintain current surveillance intervals. Third, at each step, reassess by synthesizing symptoms, exercise data, echo, and any available imaging, documenting why the AI flag was accepted or overridden. Finally, discuss results with the patient, emphasizing that the AI-ECG is a triage tool that complements but does not replace imaging or clinical judgment.

Such a protocol should be adapted to local resources and patient populations. Pediatric and adult congenital clinics may choose different thresholds given growth considerations and differing surgical histories. The algorithm should include explicit fail-safes, such as manual review of all high-probability outputs by an adult congenital cardiologist, and automatic escalation for any patient with new symptoms regardless of AI output. Pilot cycles with small cohorts can surface practical issues before larger scale deployment, and near-real-time feedback from clinicians can refine thresholds and output language.

Integration with risk stratification and arrhythmia care

Electrical signals relevant to pulmonary valve timing intersect with arrhythmia risk, especially as QRS duration increases and repolarization becomes heterogeneous. An AI-ECG workflow should therefore be designed to coexist with arrhythmia surveillance programs, carefully avoiding mixed messages. The first appearance of the term risk stratification here highlights that distinct outputs serve distinct purposes: one may estimate proximity to valve thresholds, another may flag electrical instability requiring electrophysiology input. Shared documentation templates can help teams track which flags prompted imaging versus electrophysiology referral, and what actions followed.

When AI-ECG trends suggest progression toward valve intervention, early collaboration with anesthesia, imaging, and surgical teams can smooth patient flow. For those with marked conduction delay or a history of sustained arrhythmia, coordination with electrophysiology for perioperative planning is prudent. Importantly, an AI-ECG flag should never be interpreted in isolation to up- or downgrade implantable cardioverter-defibrillator discussions; those decisions remain anchored in established criteria and personalized evaluation. Clear handoffs and communication pathways between teams lower the risk of duplication, delay, or inconsistent counseling.

Patient-centered communication and shared decisions

Patients deserve clarity about how AI contributes to their care. Early, clear explanations that an ECG-based algorithm estimates proximity to imaging criteria for valve evaluation can reduce anxiety and build trust. Using visual trend displays, clinicians can show how probabilities changed over time alongside symptoms and test results. This approach supports shared decision making, allowing patients and families to weigh risks, benefits, and timing in context. Written summaries after visits can reinforce the message that AI outputs are advisory and that final decisions reflect an integrated clinical assessment.

Equity considerations should be front and center. Differences in ECG quality, comorbidities, or care access can bias outputs or downstream actions. Teams should monitor for disparities in who is flagged and who receives advanced imaging or valve referral after a flag. Interpreter services, accessible educational materials, and flexible scheduling can reduce friction for families traveling long distances. Ensuring consistent oversight regardless of insurance or geography is essential to equitable adoption.

Validation, equity, and governance

Before clinical deployment, robust external validation is indispensable. Performance metrics should be reported across key subgroups, including age, sex, surgical era, and care setting, to detect differential performance. Calibration is as important as discrimination; overconfident probabilities can mislead triage decisions. Post-deployment, teams should monitor real-world performance and recalibrate when drift appears, especially as imaging protocols evolve. Transparent reporting of how the model was trained, what inputs were used, and how missing data were handled supports clinician trust and patient understanding.

Accountability requires clear clinical governance, including oversight committees, documentation standards, and audit trails. A governance framework should specify who reviews high-probability flags, how action thresholds are set and revised, and how exceptions are handled. It should also define procedures for pausing or rolling back deployment if safety signals emerge. Integration with institutional quality and safety programs allows learning loops that connect AI outputs to clinical outcomes. Explicit documentation of model version and output at the time of decision-making supports traceability and defensibility.

Defining the right outcome targets

Choice of labels for model training shapes clinical utility. If a model predicts meeting comprehensive imaging criteria for evaluating pulmonary valve replacement, its outputs are naturally tied to real decisions. Alternatively, training to predict right ventricular volumes or function provides granular physiologic targets but requires mapping to action thresholds. Hybrid approaches can predict both structural metrics and a downstream clinical endpoint, such as valve referral within a defined window. Whatever the target, labels should be adjudicated, reproducible, and reflective of contemporary practice to ensure outputs remain clinically relevant.

Surrogate outcomes must be contextualized. For instance, right ventricular volume alone does not dictate timing for intervention; symptoms, exercise capacity, and tissue characteristics matter too. Thus, even a well-performing model should be framed explicitly as a triage aid that focuses attention on those most likely to benefit from earlier imaging or evaluation. The goal is to reduce delays to appropriate care, not to override the nuanced decision-making that defines adult congenital practice.

Performance metrics that matter

Clinicians benefit from performance summaries aligned with everyday decisions. Calibrated risk distributions, positive and negative predictive values across clinically chosen thresholds, and decision curves can show where an AI-ECG adds net benefit over usual care. Time-to-event analyses from the index ECG to imaging-confirmed thresholds or valve referral can further clarify how early the signal emerges. Subgroup analyses should be preplanned and prominent, highlighting settings where the tool performs strongly or poorly. Clear visualizations of calibration across deciles of predicted risk build trust and promote appropriate use.

Net benefit should reflect real operational constraints. For example, if advanced imaging slots are limited, thresholds may be set to favor specificity while preserving sensitivity for those at highest risk of missing the window for reverse remodeling. If imaging access is robust, thresholds can be relaxed to capture more patients trending adverse, with the understanding that some will be reclassified after imaging. These are explicit policy choices that should be documented and revisited as capacity and needs change.

Prospective implementation and learning health systems

Retrospective performance is a starting point, not an endpoint. Prospective studies can test whether AI-ECG triage shortens time to imaging for high-risk patients, reduces missed opportunities for timely valve intervention, or improves patient-reported outcomes. Randomized or stepped-wedge designs embedded in routine care can evaluate impact without stalling service delivery. Teams should capture process measures, including proportion of high-probability flags acted upon, reasons for override, and time from flag to action. This evidence moves the conversation from promise to accountable practice.

Continuous learning is both feasible and necessary. With safeguards, sites can periodically refresh the model or recalibrate probabilities as new data accrue, especially if surgical techniques or imaging criteria evolve. Feedback loops should include clinician surveys to surface usability barriers and patient feedback to ensure communication is clear and respectful. The learning health system mindset emphasizes iteration, transparency, and shared stewardship, aligning with the values of adult congenital programs.

Ethics, transparency, and explainability

Ethical deployment demands clarity about limitations. Clinicians and patients should know that an AI-ECG cannot adjudicate surgical timing on its own and that it may perform differently across populations. Feature-level explanations can be helpful if they illuminate recognizable patterns, but they should not displace robust validation and calibration. When uncertainty is high, the default should be to seek confirmatory testing rather than to defer care. Clear patient-facing materials should explain how data are used, with options to opt out if possible.

Transparency extends to documenting how the model was built and updated. Sites should record model version numbers, training data provenance, and update cadences. This record enables attribution if performance shifts and supports responsible sunset plans if a model becomes obsolete. Institutions should also publish governance summaries, including oversight membership and key decisions, to foster trust across the congenital community.

Regulatory and operational considerations

Depending on jurisdiction and claims, AI-ECG tools may fall under device regulation, requiring quality management systems and postmarket surveillance. Even when used as clinical decision support, institutions should apply internal review processes comparable to those for other high-impact tools. Procurement should evaluate security, interoperability, and vendor support, with contingency plans for downtime. Integration with ECG carts and ECG management systems should minimize manual steps, reduce transcription errors, and support automated result retention in the record.

Operational success depends on multidisciplinary collaboration, from adult congenital cardiology and imaging to electrophysiology, surgery, nursing, informatics, and quality. Education sessions can prepare staff to interpret outputs and explain them to patients. Periodic drills can test escalation pathways when high-probability flags appear, ensuring that access to imaging and specialty review keeps pace. These operational details make the difference between a promising algorithm and a reliable service improvement.

Contours of clinical governance

Institutions adopting AI-ECG triage should codify oversight in a written framework. This includes defining reviewers, action thresholds, escalation pathways, and audit schedules. Governance teams should periodically review fairness metrics, clinical impact measures, and override rates, adjusting thresholds or pausing use if safety signals emerge. Training and credentialing requirements for interpreting AI-ECG outputs can be specified to ensure consistent application. Clear alignment with institutional clinical governance processes reinforces accountability and keeps the technology focused on patient benefit.

Documentation templates embedded in the EHR can standardize how outputs are recorded, including the probability, confidence, clinical interpretation, and action taken. These templates reduce ambiguity, facilitate audits, and support research on implementation outcomes. They also help ensure that AI outputs are communicated in patient-friendly language and that care teams across settings can access the same information.

From method to bedside: what to demand next

To move responsibly from promising signals to routine care, teams should prioritize external validation, subgroup performance reporting, and impact studies. They should also demand model cards that describe intended use, limitations, and update plans. Calibration clinics can align probability outputs with local decision thresholds, preventing silent miscalibration. Finally, explicit plans for de-implementation, should the model fail to deliver benefit or show bias, are as important as the implementation plan itself. This discipline ensures that AI serves clinical goals rather than the other way around.

The AI-ECG approach is a natural fit for adult congenital practice, where serial ECGs are ubiquitous and imaging is precious. It offers a way to align attention and resources with evolving risk, while keeping clinicians and patients at the center of decisions. The next steps are clear: independent testing, transparent reporting, and careful implementation tied to outcomes that matter. With these guardrails, ECG-based AI can become a practical compass to time pulmonary valve evaluation more wisely and more equitably.