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The Cordis Webster deflectable 720 electrode catheter is indicated for electrophysiological mapping of cardiac structures; i.e., stimulation and recording only. The preformed shape of the tip section is designed specifically for the tricuspid annulus.

The Cordis Webster deflectable 720 electrode catheter has been designed for electrophysiological mapping of the tricuspid annulus. The catheter has a high-torque shaft with a halo-shaped tip section containing ten pairs of platinum electrodes that can easily be seen under fluoroscopy. The tip section also contains a radiopaque marker in the center of the electrode array. The tip section of the catheter has a halo-shaped preformed loop which can be positioned around the atrial aspect of the tricuspid annulus.

A piston in the handpiece is attached to an internal puller which changes the radius of curvature. When the piston is pushed forward, the radius of curvature of the preformed loop is reduced; when the thumbknob is pulled back, the radius of curvature is increased until the tip section returns to the preformed shape. The high-torque shaft allows the plane of the loop to be maneuvered in order to facilitate accurate positioning.

The Cordis Webster deflectable T20 electrode catheter facilitates simultaneously local electrograms spanning the tricuspid annulus, from midseptal to anterior to lateral to posterolateral. Recordings of the entire annulus can be obtained without repositioning the catheter tip.

This product is a medical device (catheter), not an AI/ML device, and therefore the requested information regarding acceptance criteria, study details, and AI/ML specific performance metrics (like MRMC studies, standalone AI performance, training/test set details, and ground truth establishment for AI) is not applicable.

The provided text describes a 510(k) summary for a Cordis Webster T20 Diagnostic Deflectable Tip Catheter. This summary focuses on demonstrating substantial equivalence to a predicate device, which is a common regulatory pathway for medical devices that are not novel.

Here's an breakdown of the relevant information provided, explaining why AI-specific questions are not applicable:

1. Acceptance Criteria and Reported Device Performance:

   • Acceptance Criteria: For non-AI medical devices, acceptance criteria are generally established based on safety and effectiveness compared to a legally marketed predicate device. The goal is to show the new device is "as safe and effective" as the predicate.
   • Reported Device Performance: The document states that "The nonclinical performance testing performed on the 720 electrode catheter compared to the predicate device indicated that there were no statistically significant differences in the outcome of the tests for each of the devices that would affect the safety and effectiveness of the device."
   • Basis for Performance: The performance was evaluated through nonclinical tests (likely bench testing, mechanical tests, electrical integrity, biocompatibility, etc.) according to FDA's "Electrode Recording Catheter Preliminary Guidance."

   Table (Not Directly Available for AI Metrics, but for Device Equivalence):

   Acceptance Criterion (Implicit)	Reported Device Performance
   Safety and Effectiveness comparable to predicate device	"no statistically significant differences in the outcome of the tests for each of the devices that would affect the safety and effectiveness of the device."
   Performance according to FDA's "Electrode Recording Catheter Preliminary Guidance"	Tests "were performed according to FDA's 'Electrode Recording Catheter Preliminary Guidance'." (Success implied by "no statistically significant differences" conclusion)

2. Sample Size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

   • Not applicable: This pertains to clinical data for AI/ML or efficacy trials. The provided document describes nonclinical (bench) testing, not patient data trials. Therefore, sample sizes for "test sets" in the AI sense, data provenance, retrospective/prospective distinctions, and country of origin are not mentioned or relevant to this type of device submission.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

   • Not applicable: Ground truth and expert adjudication are concepts relevant to AI model validation, particularly in image interpretation or diagnostic aid devices. This catheter is a diagnostic tool for electrophysiological mapping, not an AI interpreting data or images. Its performance is assessed through engineering and physical tests.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

   • Not applicable: Adjudication methods are used in studies where human reviewers provide judgments (e.g., classifying images), and discrepancies need to be resolved. This is not relevant for a physical medical device undergoing nonclinical performance testing.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • Not applicable: MRMC studies evaluate the impact of an AI diagnostic aid on human reader performance. This device is a catheter used for recording, not an AI software.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   • Not applicable: This question refers to AI algorithm performance without human intervention. The device is a physical catheter that requires a human operator for its intended use.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • Not applicable: As explained above, "ground truth" as a reference for AI model accuracy is not relevant here. The "truth" in this context is whether the catheter meets its engineering specifications and performs comparably to the predicate device in nonclinical tests.

8. The sample size for the training set:

   • Not applicable: Training sets are used for machine learning models. This is a physical medical device.

9. How the ground truth for the training set was established:

   • Not applicable: See point 8.

In summary, the provided document describes a traditional 510(k) submission for a non-AI medical device (a catheter). The evaluation is based on nonclinical performance testing to demonstrate substantial equivalence to a predicate device, not on AI/ML performance metrics like accuracy, ground truth, or reader studies.

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