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The AMPLATZER TorqVue Delivery Sheath is intended to provide a pathway through which devices are introduced within the peripheral vasculature.

The AMPLATZER® TorqVue® 2 Delivery Sheath is a sterile, single-use, general-purpose sheath that is an extension of the TorqVue Delivery System product line. It is designed to provide a pathway through which a device may be delivered to the peripheral vasculature. The AMPLATZER® TorqVue® 2 Delivery Sheath has a usable length of 120 cm and is available with a 5 Fr, 6 Fr, or 7 Fr inner diameter. A dilator, which eases penetration of tissue, is packaged with each sheath. The distal end of both the sheath and dilator are straight. The body of each sheath is radiopaque for visibility under fluoroscopy. The single-lumen sheath is constructed of two segments of different polyether block amide (Pebax®) materials to provide support for device advancement and tip flexibility for vessel engagement. The Pebax® encapsulates a stainless-steel braid support structure that provides maneuverability while minimizing kinking. The distal end of the sheath has a soft tip composed of 55-durometer Pebax® to minimize vessel trauma.

The provided text describes a 510(k) summary for the AMPLATZER® TorqVue® 2 Delivery Sheath, a medical device. This document is a regulatory submission to the FDA, and as such, it focuses on demonstrating substantial equivalence to a predicate device rather than fulfilling typical "acceptance criteria" and "studies" in the way one might expect for a novel AI/software medical device.

Therefore, many of the requested points related to AI software performance (e.g., sample sizes for test and training sets, number of experts for ground truth, MRMC studies, standalone performance, provenance of data) are not applicable to this type of device and submission.

Here's an analysis based on the information provided:

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

For this device, "acceptance criteria" are not reported as specific performance metrics in the same way an AI algorithm would have sensitivity/specificity thresholds. Instead, the acceptance criteria are implicitly that the device performs comparably to the predicate device across various functional and safety characteristics. The reported "performance" is that it met these criteria through bench testing.

Study Title: Not explicitly named, but referred to as "Bench testing" and "laboratory verification test."

2. Sample size 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 specified. Bench testing often involves a statistically determined number of units or replicates for each test, but the exact numbers are not detailed in this summary.
• Data Provenance: The testing appears to be internal "bench testing" and "laboratory verification tests" performed by the manufacturer, AGA Medical Corporation, in Plymouth, MN, USA.
• Retrospective/Prospective: As this is bench testing of physical properties, these terms are not applicable in the typical clinical study sense. The tests are designed and executed prospectively in a lab setting.

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 physical medical device (delivery sheath), not an AI algorithm requiring expert interpretation for "ground truth" on images or clinical data. The "ground truth" for its performance is derived from standardized engineering tests measuring physical properties.

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

• Not Applicable: As above, this is bench testing of a physical device. There is no "adjudication method" involving experts for a test set in the context of clinical interpretation or diagnosis. Test results are objective measurements against predetermined specifications.

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: This is a physical medical device, not an AI algorithm. Therefore, MRMC studies are not relevant.

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

• Not Applicable: As this is a physical medical device, not an algorithm, the concept of "standalone performance" for an algorithm is not relevant.

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

• Ground Truth Type: For a physical device, the "ground truth" is defined by engineering specifications, industry standards, and the performance characteristics of the predicate device. For instance, the "ground truth" for tensile strength would be a defined force threshold, or for kinking, a specific angle of bend without occlusion. These are objective, measurable physical properties.

8. The sample size for the training set

• Not Applicable: This is a physical medical device. There is no "training set" in the context of machine learning or AI.

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

• Not Applicable: As there is no training set, the establishment of ground truth for it is not relevant.

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