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The St. Jude Medical™ CPS Aim™, MediGuide Enabled™ slittable inner catheter (subselector/cannulator) is designed for intracardiac access of the coronary sinus and subselection of the venous system of the heart, and to serve as a conduit during implantation for the delivery of contrast medium and St. Jude Medical™ devices, including leads and guidewires. In addition, CPS Aim™, MediGuide Enabled™ slittable inner catheters (subselector/cannulator) can work with outer guide catheters as a system. The CPS Aim™, MediGuide Enabled™ slittable inner catheter is used with the MediGuide™ System to enable realtime tip positioning and navigation within the anatomy. The MediGuide™ system is indicated for use as an adjunct to fluoroscopy.

The St. Jude Medical™ CPS Aim™, MediGuide Enabled™ slittable inner catheter (subselector/cannulator) is used to facilitate left heart lead delivery procedures. The CPS Aim, MediGuide Enabled slittable inner catheter (subselector/cannulator) contains a magnetic sensor allowing it to be tracked using the MediGuide™ System. The inner catheter facilitates cannulation of the coronary sinus or subselection of branch veins off of the coronary sinus/great cardiac vein during surgical implantation procedures. It may also be used for the delivery of contrast medium and St. Jude Medical™ devices, including guidewires. When used in conjunction with outer guide catheters, the CPS Aim, MediGuide Enabled slittable inner catheter (subselector/cannulator) can function as a dual-catheter assembly.

The provided text is a 510(k) summary for the St. Jude Medical CPS Aim™ MediGuide Enabled™ Slittable Inner Guide Catheter. It describes the device, its intended use, and states that performance testing was conducted to demonstrate substantial equivalence to predicate devices. However, this document does not contain the specific acceptance criteria or the detailed results of a study designed to prove the device meets those criteria, especially in the context of AI/ML performance.

This document focuses on the mechanical, functional, and biocompatibility testing of a physical medical device (a catheter). It does not involve any AI/ML components, therefore, aspects related to AI/ML performance such as sample sizes for test/training sets, data provenance, expert ground truth establishment, adjudication methods, MRMC studies, or standalone algorithm performance are not applicable and are not mentioned.

Therefore, most of the requested information regarding AI/ML device performance and related studies cannot be extracted from this document.

Here's what can be extracted based on the document provided:

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

The document lists types of performance testing but does not provide specific numerical acceptance criteria or detailed quantitative performance results for each test. It only states that the device "met all specified design and performance specifications."

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

This information is not provided in the document. The testing described is for a physical device's mechanical and functional properties, not data-driven AI/ML performance.

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)

This information is not applicable as the document describes testing of a physical medical device, not an AI/ML system requiring expert-established ground truth from data.

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

This information is not applicable for the reasons stated above.

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

This information is not applicable as the device is a physical catheter, not an AI/ML system designed to assist human readers.

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

This information is not applicable as the device is a physical catheter, not a standalone algorithm.

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

This information is not applicable as the document describes physical and functional testing of a device, not validation against a medical ground truth for an AI/ML system.

8. The sample size for the training set

This information is not applicable as the device is a physical catheter and does not involve AI/ML requiring a training set.

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

This information is not applicable for the reasons stated above.

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