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The Reprocessed CS Bi-Directional Diagnostic EP Catheter is indicated for electrophysiological mapping of cardiac structures; i.e., stimulation and recording only. The catheter is designed for use in the coronary sinus.

The reprocessed Biosense Webster® Coronary Sinus (CS) EZ STEER™ Diagnostic Electrophysiology (EP) Catheter (hereinafter CS Bi-Directional Diagnostic EP Catheter) is a steerable, multi-electrode catheter with a deflectable tip designed to facilitate electrophysiological mapping of the heart. The device is a 7FR catheter with a usable length of 115 cm. The catheter has a high-torque shaft with a braided bi-directional deflectable tip section containing platinum electrodes that can be used for recording and stimulation. Two asymmetric curve types, DF and FJ, are available providing two 180° opposed, single plane curves. The rocker lever located on the handpiece is used to deflect the tip section. A Friction Control Knob is located on the opposite side of the rocker lever and can be rotated clockwise to lock both the tip curve and rocker lever in place. The high torque shaft allows the plane of the curved tip to rotate, enabling accurate positioning of the catheter tip at the preferred site.

The provided document (K100254) describes the performance data for a reprocessed medical device, the "Reprocessed CS Bi-Directional Diagnostic Electrophysiology Catheter." However, it does not contain the specific information requested in the prompt regarding acceptance criteria and a study proving the device meets those criteria, especially in the context of an AI/algorithm-driven device.

This submission is for a medical device reprocessed by Ascent Healthcare Solutions that is "substantially equivalent" to a predicate device. The performance data presented is focused on demonstrating that the reprocessed device performs as originally intended, which is typical for a 510(k) submission for reprocessed devices rather than a novel AI/algorithm.

Therefore, most of the specific questions regarding AI performance criteria, sample sizes for AI test sets, expert ground truth, MRMC studies, or training sets cannot be answered from the provided text.

Here's a breakdown of what can be extracted and what is missing:

1. A table of acceptance criteria and the reported device performance

• Acceptance Criteria: The document implies that the acceptance criteria are that the reprocessed device performs "as originally intended" and is "safe, effective, and substantially equivalent to the predicate devices." No specific quantitative acceptance criteria (e.g., minimum accuracy, sensitivity, specificity) are provided for the reprocessed catheter itself in terms of electrophysiological performance.

• Reported Device Performance:

  • Bench and laboratory testing was conducted.
  • Tests included: Biocompatibility, Validation of reprocessing, Sterilization Validation, Function test(s), Packaging Validation.
  • Conclusion: "Performance testing demonstrates that reprocessed electrophysiology catheters perform as originally intended."

  Table (Based on the document):

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

• Sample Size for Test Set: Not explicitly stated. The document mentions "Each individual CS Bi-Directional Diagnostic EP Catheter is tested for appropriate function," which suggests a 100% inspection or a statistically determined sample from reprocessed lots, but the exact numbers are not given.
• Data Provenance: Not specified. The testing was conducted by Ascent Healthcare Solutions.

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. This is a reprocessed physical device, not an AI/algorithm that requires expert-established ground truth for its performance evaluation in the way an AI diagnostic tool would. The "ground truth" for the device's function is its intended mechanical and electrical performance.

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

• Not Applicable. As above, this is for a physical device performance, not an AI output requiring expert adjudication.

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

• No. This is not relevant for the type of device and study described. There is no AI component or human reader interaction with an AI.

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

• No. There is no algorithm or AI component mentioned.

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

• The "ground truth" for this device's performance would be engineering specifications, established functionality of the original predicate device, and compliance with reprocessing standards. It's not a medical diagnostic ground truth like pathology or expert consensus on an image.

8. The sample size for the training set

• Not Applicable. There is no AI or algorithm that would require a training set.

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

• Not Applicable. There is no AI or algorithm that would require a training set.

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