Machine learning-guided software for the ablation of cardiac arrhythmias

Unmet Need: Quick, accurate identification of cardiac isthmus sites for corrective ablation

Cardiac arrhythmia is the most common cause of sudden death in the United States. However, catheter ablation—the standard curative operation—is an inefficient procedure that relies upon manual visual inspection by the operator. This dependence on human interpretation leads to prolonged procedure times and reduced ablation success, resulting in increased risk of complications and disease persistence. Therefore, there is a need for an automated system that can rapidly and accurately identify the locations of cardiac isthmuses for the long-term treatment of arrhythmia.

The Technology: Integrative algorithm-guided system for the detection of arrhythmia-associated isthmuses

This software is a machine learning-guided approach to model the locations of arrhythmia isthmuses. Electroanatomic data with electrical signal features and anatomical information obtained from previously successful ablation procedures are used to construct and train the model. Once trained, this algorithm can be used to predict the presence of arrhythmia isthmuses in patients based off data acquired using traditional mapping techniques. As such, this technology can be used to improve ablation targeting for cardiac arrythmias.

This technology has been validated using a patient dataset.

Applications:

Detection of arrhythmia isthmuses
Research technology for studying arrhythmia
Electrogram analysis tool

Advantages:

Automated and unbiased analysis algorithm
Decreased procedure times
Improved isthmus identification reliability
Expanded operator eligibility

Lead Inventor:

Christine Hendon, Ph.D. Deepak Saluja, MD

Patent Information:

Patent Pending

Tech Ventures Reference:

IR CU22127
Licensing Contact: Dovina Qu