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The intended use of the CARTO® 3 System is catheter-based cardiac electrophysiological (EP) procedures. The CARTO® 3 System provides information about the electrical activity of the heart and about catheter location during the procedure. The system can be used on patients who are eligible for a conventional electrophysiological procedure. The system has no special contraindications.

CARTO® The 3 EP Navigation System with CARTOSOUND™ 4D, software V7.4, is a catheter-based atrial and ventricular mapping system designed to acquire and analyze navigation catheter's location and intracardiac ECG signals and use this information to display 3D anatomical and electroanatomical maps of the human heart. The location information needed to create the cardiac maps and the local electrograms are acquired using specialized mapping catheters and reference devices. The CARTO® 3 System uses two distinct types of location technology – magnetic sensor technology and Advanced Catheter Location (ACL) technology.

The CARTO® 3 System with CARTOSOUND™ 4D consists of the following hardware components:

• . Patient Interface Unit (PIU)
• Workstation with Graphic User Interface (GUI)
• . Wide-Screen monitors, keyboard, and mouse
• . Intracardiac In Port
• Intracardiac Out Port ●
• . Power Supply
• Patches Connection Box and Cables (PU)
• Pedals ●
• Location Pad (LP) ●
• Signal Processing Unit (SPU) ●

All hardware components of the CARTO® 3 system with CARTOSOUND™ 4D are the same to those found in the predicate device.

The provided document focuses on the 510(k) summary for the CARTO® 3 EP Navigation System with CARTOSOUND™ 4D version 7.4. It describes the device, its intended use, and the testing conducted to demonstrate its substantial equivalence to a predicate device. However, it does not contain the specific details required to answer all parts of your request, particularly regarding detailed acceptance criteria with performance values, sample sizes for test and training sets, expert qualifications, or multi-reader, multi-case (MRMC) comparative effectiveness studies.

The document primarily describes functional and performance verification rather than a study validating a specific AI algorithm's diagnostic performance against ground truth (as would be typical for an AI/ML medical device). The "CARTOSOUND™ 4D" module appears to be a feature that combines and processes ultrasound and mapping data, not a standalone diagnostic AI algorithm.

Based on the information provided, here's what can be extracted:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not provide a table with specific quantitative acceptance criteria and corresponding numerical performance results for the device's diagnostic accuracy or AI performance. Instead, it states that:

Important Note: The document describes engineering and system verification tests, not a clinical study involving diagnostic AI performance metrics like sensitivity, specificity, or AUC, which would typically have specific numerical acceptance criteria.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: Not specified. The document mentions "Bench testing," "Animal Testing," and "Summative Usability testing" but does not give specific numbers of cases, patients, or animals used in these tests.
• Data Provenance: Not specified regarding country of origin or whether it was retrospective or prospective. Animal testing is mentioned, which is by nature prospective for the animal portion.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. It is implied that qualified personnel conducted and assessed the engineering and animal tests, but no details are given. This device processes and displays information for a human user (physician) to interpret; it does not itself make a diagnostic decision that requires establishing a "ground truth" through expert consensus in the typical AI sense.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable/Not specified. Given the nature of the described tests (functional, image quality, animal functionality, usability), there's no mention of a traditional expert adjudication process for establishing ground truth for diagnostic accuracy, as the device's primary function is data acquisition and display, rather than automated diagnosis.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• MRMC Study: No. The document does not describe any MRMC study comparing human readers with and without AI assistance. The device is a navigation system that provides information, not an assistive AI diagnostic tool for image interpretation.
• Effect Size of Human Reader Improvement: Not applicable, as no such study was performed or described.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• The document implies that the "performance data" refers to the device's functional performance in acquiring and processing data to specification. It does not describe a standalone study of an AI algorithm making diagnostic interpretations without human intervention. The device is a "Navigation System" that "provides information" to the user, not an autonomous diagnostic AI.

7. Type of Ground Truth Used

• Type of Ground Truth: The concept of "ground truth" in the traditional sense of a diagnostic outcome (e.g., pathology, clinical outcome, expert consensus on a diagnosis) is not explicitly discussed for the device's performance validation. The "truth" in this context refers to whether the system functions according to its engineering specifications and accurately displays the acquired data. For instance:
  • Bench Testing: Verification against internal system specifications and accurate data display.
  • Animal Testing: Verification of functionality in a simulated physiological environment.
  • Usability Testing: Assessment of the user interface's safety and effectiveness.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not applicable/Not specified. The document describes a medical device upgrade, not a de novo AI model that requires a distinct "training set" in the machine learning sense. The software updates described are improvements to an existing system, including a new module (CARTOSOUND™ 4D) and various other enhancements, for which "training data" is not a relevant concept as it would be for a deep learning model.

9. How Ground Truth for the Training Set Was Established

• How Ground Truth for Training Was Established: Not applicable. As there's no mention of an AI model with a training phase, the concept of establishing ground truth for a training set does not apply.

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