What question did this study set out to answer?

The aim is to explore how well AI-assisted ECG can identify VT exit sites compared to traditional mapping methods.

January 10, 2026Journal of Cardiovascular Electrophysiology0 citations

A Novel Workflow Combining Artificial Intelligence—Twelve Lead Electrocardiographic Analysis and Real‐Time Mapping for Hemodynamically Unstable Ventricular Tachycardia

FHFaris HaddadinElectrophysiology SKS. KesslerUniversity of Minnesota APAnthony PriscoUniversity of Minnesota

Key Result

AI-assisted ECG analysis of VT exit site achieved 100% agreement with successful ablation sites, resulting in non-induction of all VTs in a hemodynamically unstable cohort.

Key Points

The aim is to explore how well AI-assisted ECG can identify VT exit sites compared to traditional mapping methods.
Single-center proof-of-concept study with patients undergoing catheter ablation for unstable VT.
Comparison of AI-assisted 12-lead ECG analysis with substrate mapping for successful ablation sites.
Evaluation of rhythm induction and patient outcomes at 6 months post-procedure.
Nine hemodynamically unstable VT rhythms were induced in four patients.
AI-assisted ECG analysis showed 100% agreement with successful ablation sites from substrate mapping.
No induced or recurrent VTs and no device therapy were required for patients over a 6-month follow-up.

Structured PICO

Does AI-assisted 12-lead ECG analysis accurately localize VT exit sites to guide successful catheter ablation in patients with hemodynamically unstable scar-mediated VT?

Population

4 patients with hemodynamically unstable scar-mediated ventricular tachycardia undergoing catheter ablation procedure.

Intervention

Artificial intelligence (AI)-assisted 12-lead ECG analysis (Vektor Medical) for VT exit site localization combined with pace-mapping and mapping of channels of slow conduction in sinus rhythm.

Outcome

Level of agreement between AI-based 12-lead ECG localization of VT exit site and sites of successful ablation based on substrate mapping.surrogate

A novel workflow using AI-assisted 12-lead ECG analysis accurately localizes VT exit sites, enabling successful ablation of hemodynamically unstable VT in sinus rhythm.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

ABSTRACT Background Activation mapping of scar‐mediated ventricular tachycardias (VT) are often limited by hemodynamic instability. Substrate mapping and 12‐lead ECG localization of VT exit site is often limited by variable scar complexity. Emphasis on other mapping strategies in sinus rhythm would improve catheter ablation success in such cases. Objective The aim was to investigate the level of agreement between artificial intelligence (AI) based 12‐lead ECG localization of VT exit site and mapping utilizing pace‐mapping and mapping of channels of slow conduction within the scar tissue of hemodynamically unstable VT. Methods This was a single‐center proof‐of‐concept study that included patients who underwent catheter ablation procedure of hemodynamically unstable scar‐mediated VT. The performance of AI‐assisted ECG analysis of VT exit site (Vektor Medical, San Diego, CA) was compared with sites of successful ablation based on substrate mapping in sinus rhythm. Results A total of nine hemodynamically unstable VT rhythms were induced in four patients. In the seven VTs were AI‐assisted ECG analysis was used; there was a 100% level of agreement with the site of successful ablation based on substrate mapping. Ablation targeting those sites resulted in non‐induction of all induced and mapped VTs. None of the four patients had device therapy for recurrent VT or all‐cause mortality at 6 months of follow‐up. Conclusion A multi‐strategic approach utilizing digitalized analysis of 12‐lead ECG of VT exit site, pace‐mapping and mapping of channels of slow conduction in scar tissue, has the potential to enhance successful catheter ablation of hemodynamically unstable VT.

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