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The Diagnostic Fixed Electrophysiology Lumen Catheter and Diagnostic Electrophysiology Cable can be used in the evaluation of a variety of cardiac arrhythmias from endocardial and intravascular sites.

The Diagnostic Fixed Electrophysiology Lumen Catheter is a sterile, single-use 6F diagnostic catheter with multiple electrodes and a fixed distal curve. The catheter body is a continuous tube with a central lumen for fluid infusion through a last chance filter (LCF) female luer connector. The catheter is intended to be placed in the heart percutaneously to facilitate electrophysiology studies of the heart, such as the coronary sinus and atrioventricular annulus. The catheter is connected to separately cleared diagnostic electrophysiology equipment via an accessory, the Diagnostic Electrophysiology Cable. The cable is supplied sterile and is reusable.

The provided text describes a 510(k) submission for a medical device (Diagnostic Fixed Electrophysiology Lumen Catheter and Diagnostic Electrophysiology Cable) and does not contain information about an AI/ML-based device. Therefore, it does not provide the specific details required to answer the questions about acceptance criteria, study methodologies for AI performance, sample sizes, expert ground truth establishment, or human-in-the-loop performance.

The document primarily focuses on demonstrating substantial equivalence to a predicate device based on:

• Intended Use and Indications for Use: Identical.
• Fundamental Scientific Technology, Operating Principles, Mechanism of Action: Identical.
• Materials, Key Technological Characteristics, Packaging Configuration, Environment of Use, Sterility/Reusability, Sterilization Method: Similar or identical.
• Performance Testing: Non-clinical tests (Biocompatibility, Mechanical, Electrical, Bench Validation, Cleaning/Re-sterilization, Reuse Verification, Packaging Validation) to support safety and effectiveness. These are standard tests for medical devices, not specific to AI/ML performance.

Therefore, I cannot provide a table of acceptance criteria and reported device performance for an AI/ML model, nor information regarding sample sizes for test/training sets, expert adjudication methods, or MRMC studies, as these aspects are not discussed in the provided text.

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