CARTO 3 EP Navigation System Software V8.0 (FG-5400-00, FG-5400-00U)

The CARTO™ 3 EP Navigation System Software V8.0 is designed to enhance catheter-based cardiac electrophysiological procedures by providing advanced 3D mapping of cardiac anatomy and electrical activity. It employs magnetic and ACL-based catheter tracking, sophisticated signal processing, and introduces machine learning and deep learning algorithms for automated ECG pattern recognition, complex signal identification, and anatomical segmentation, aiming to improve procedural accuracy and efficiency.

The CARTO™ 3 EP Navigation System V8.0 is an advanced software for catheter-based cardiac electrophysiological (EP) procedures, designed to aid clinicians in mapping the heart's electrical activity and catheter location. It creates 3D anatomical and electroanatomical maps by acquiring and analyzing intracardiac ECG signals and catheter positions using both magnetic sensor technology and Advanced Catheter Location (ACL) technology. The system maintains the same hardware platform and core location mapping technologies as its predicate but introduces significant software enhancements. These enhancements aim to improve the accuracy and efficiency of EP procedures by providing detailed visualizations and automated analyses of cardiac electrophysiology. Version 8.0 introduces several key software features, including Multipolar Mapping for better signal discrimination, Auto Pattern Acquisition for automated ECG pattern recognition, and Complex Signals Identification (CSI) using a machine learning algorithm to tag fractionated signals. The CARTOSOUND™ FAM module leverages deep learning for automatic 3D Left Atrium segmentation from ultrasound. Other additions like LAT Velocity Vectors offer novel visualization of conduction, and support for new catheters (OPTRELL™, LASSOSTAR NAV) and improved support for QDOT Micro™ expand its utility. Legacy features like VISITAG, Late Annotation Mapping, and CONFIDENSE (with SmartMap) have also been refined for better performance. Comprehensive verification, validation (including retrospective clinical data analysis), usability, animal, and a real-world clinical study on low/zero fluoroscopy workflows demonstrated the system's safety, effectiveness, and improved functionalities.

AI Regulatory Concerns

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Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

• Q1. How does the "Complex Signals Identification (CSI)" machine learning algorithm differentiate between true complex fractionated atrial electrograms (CFAEs) and noise or far-field signals, and what is its validated accuracy across diverse patient populations and arrhythmia types beyond atrial flutter?; What specific improvements in workflow efficiency or procedural outcomes (e.g., ablation success rates, procedure time) have been observed with the "CARTOSOUND™ FAM" deep learning-based automatic LA segmentation compared to manual or semi-automated methods?; How does the "Compensated VISITAG Stability Algorithm" account for varying respiratory patterns and amplitudes in real-time, and how does this improved stability assessment correlate with lesion quality or durability?; For the "Auto Pattern Acquisition" feature, what is the typical processing time for pattern clustering during a procedure, and how does it handle transitions between different cardiac rhythms or ectopy?; With the introduction of "LAT Velocity Vectors (LVV)," how is the vector data validated against actual conduction pathways, and can this feature help differentiate between passive "bystander" activation and critical isthmuses in re-entrant tachycardias?