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Datascope's 10 Fr. FlexiSheath™ Percutaneous Introducer is intended for the percutaneous introduction of Datascope's 9.5Fr. Intra-Aortic Balloon Catheters.

Datascope's 10 Fr. FlexiSheath™ Percutaneous Introducer is intended for the percutaneous introduction of Datascope's 9.5 Fr. Intra-Aortic Balloon Catheters.

The provided text describes a 510(k) premarket notification for Datascope's 10 Fr. FlexiSheath™ Percutaneous Introducer. In the context of 510(k) submissions, the concept of "acceptance criteria" and a "study that proves the device meets the acceptance criteria" in the traditional sense of a clinical trial with predefined performance metrics and statistical endpoints is generally not applicable for devices seeking substantial equivalence.

Instead, the submission aims to demonstrate that the new device is "substantially equivalent" to predicate devices already on the market. This is done by showing that the new device has similar technological characteristics and performance, and does not raise different questions of safety and effectiveness.

Here's an analysis of the provided text based on your request, interpreting "acceptance criteria" as the demonstration of substantial equivalence and "study" as the non-clinical testing performed:

1. Table of Acceptance Criteria (Substantial Equivalence Pillars) and Reported Device Performance

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: The document mentions "in-vitro tests" but does not specify the sample size used for these tests.
• Data Provenance: The document does not specify the country of origin of the data or whether the tests were retrospective or prospective. Given that this is an in-vitro study for a US submission, it's highly likely the tests were conducted in a US-based lab, but this is not explicitly stated. The tests are prospective in nature as they evaluate the device's characteristics.

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 or provided in the context of this 510(k) submission. For in-vitro tests demonstrating substantial equivalence, "ground truth" is typically established by engineering specifications, validated test methods, and comparison against the performance of predicate devices, not by expert consensus on clinical data. No human experts evaluating test results for "ground truth" are mentioned.

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

• This information is not applicable or provided. Adjudication methods are typically used in clinical studies involving interpretation of medical images or patient outcomes, not for in-vitro engineering tests.

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, an MRMC comparative effectiveness study was not done. This type of study is relevant for AI-powered diagnostic devices, which is not the nature of Datascope's 10 Fr. FlexiSheath™ Percutaneous Introducer.
• The device is a medical introducer sheath, a physical medical device, not an AI or imaging diagnostic tool. Therefore, the concept of human readers improving with AI assistance is not applicable.

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

• No, a standalone (algorithm only) study was not done. As mentioned, this is a physical medical device, not an algorithm or software. Its performance is evaluated through physical and material properties, and functionality in a lab setting.

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

• For the non-clinical tests, the "ground truth" would be established by engineering specifications, validated test methods, and direct comparison of performance metrics (e.g., tensile strength, flexibility, lubricity, burst pressure, flow rates, etc.) against the established performance of the predicate devices. It is not based on expert consensus, pathology, or outcomes data.

8. The sample size for the training set

• This information is not applicable or provided. The concept of a "training set" and "validation set" is relevant for machine learning algorithms. This submission is for a physical medical device and relies on engineering and material characteristic tests rather than data-driven model training.

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

• This information is not applicable or provided as there is no "training set" in the context of this device and its substantial equivalence submission.

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