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The DuraSheath Introducer Sheath System is indicated to be used for introduction of interventional and diagnostic devices into the peripheral (and coronary) vasculature. The device is also intended to be used within a pediatric population.

The DuraSheath Introducer Sheath System is a sterile, disposable device consisting of (a) a coil reinforced shaft with an atraumatic tip at the distal end; (b) a hemostasis valve with a side port and color coded 3-way stopcock; and (c) a tapered tip dilator with snap-fit hub at the proximal end.

The provided text describes a 510(k) premarket notification for a medical device called the DuraSheath Introducer Sheath System. It details the device's design, materials, indications for use, and a comparison to predicate devices, along with non-clinical performance data.

However, the document does not contain acceptance criteria or study details in the format requested, which typically relates to performance metrics for AI/ML devices or clinical endpoints for studies proving substantial equivalence. Instead, it lists various engineering and biological tests conducted to demonstrate the safety and effectiveness of the device as a whole, rather than specific performance metrics against pre-defined acceptance thresholds for an AI algorithm.

The document mainly focuses on proving the new device is substantially equivalent to existing, legally marketed devices. It details a comprehensive set of non-clinical tests (mechanical, biocompatibility, sterilization, accelerated aging, packaging integrity, simulated use, and usability) to demonstrate this equivalence and the device's safety and effectiveness.

Given the nature of the device (an introducer sheath system, which is a physical medical device, not an AI/ML algorithm or a diagnostic tool with specific performance metrics like sensitivity/specificity), the requested information regarding "acceptance criteria and the study that proves the device meets the acceptance criteria" in terms of AI performance, expert ground truth, adjudication methods, MRMC studies, and standalone algorithm performance is not applicable.

Therefore, I cannot populate the table and answer the specific questions (2-9) about acceptance criteria, test set sample size, expert qualifications, adjudication, MRMC studies, standalone performance, ground truth types, and training set details because this information is not present in the provided FDA document, as it pertains to a mechanical medical device, not an AI/ML diagnostic or therapeutic device.

The closest information to "acceptance criteria" is the list of standards the device testing conformed to, indicating that meeting these standards served as the "acceptance criteria" for demonstrating safety and effectiveness of a physical medical device. The "study that proves the device meets the acceptance criteria" is the collection of all the non-clinical tests listed in the "PERFORMANCE DATA" section.

Here's what can be extracted from the provided text, recognizing the constraints:

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

2. Sample size used for the test set and the data provenance: Not applicable in the context of an AI/ML device. For the mechanical device, the number of units tested for each specific non-clinical test (e.g., how many sheaths were subjected to pull-out testing) is not explicitly stated, although it is implied that sufficient units were tested to meet the requirements of the listed standards. There was feedback from 16 clinical cases in Germany, but this was for European CE certification and not a formal clinical trial for this 510(k).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth, in the AI/ML context, does not apply to this mechanical device.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.

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.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable. The "ground truth" for this physical device is successful performance according to engineering and biocompatibility standards.

8. The sample size for the training set: Not applicable.

9. How the ground truth for the training set was established: Not applicable.

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The DuraSheath Introducer Sheath System is indicated to be used for introduction of interventional and diagnostic devices into the human vasculature, including but not limited to femoral access via a contralateral approach to access the popliteal and infrapopliteal arteries.

The DuraSheath Introducer Sheath System is a sterile, disposable device consisting of (a) a coil reinforced shaft with an atraumatic tip at the distal end; (b) a hemostasis valve with a side port and color coded 3-way stopcock; and (c) a tapered tip dilator with snap-fit hub at the proximal end. The distal end of the sheath contains a radiopaque marker. Lubricous coating is applied to the distal end of the sheath. The system is packaged inside a sealed Tyvek pouch.

The DuraSheath Introducer Sheath System is a prescription medical device that is used only in healthcare facilities or hospitals. The device is placed in patients for up to 24 hours.

This document is a 510(k) Premarket Notification from the FDA regarding the DuraSheath Introducer Sheath System. It details the device's indications for use, comparison to predicate devices, and performance data provided for substantial equivalence, but it does not contain the acceptance criteria or a study proving the device meets specific acceptance criteria in the manner you've requested for AI/diagnostic devices.

Instead, this document focuses on demonstrating that the DuraSheath Introducer Sheath System is "substantially equivalent" to legally marketed predicate devices, meaning it performs similarly and raises no new questions of safety or effectiveness. The performance data provided is primarily non-clinical and related to the physical and material properties of the device, rather than diagnostic accuracy or clinical outcomes as would be seen in studies for AI-powered diagnostic tools.

Therefore, many of your requested points cannot be answered based on the provided text.

Here's how the available information relates to your request:

1. Table of acceptance criteria and the reported device performance

N/A. The document does not provide a table of explicit acceptance criteria with numerical targets for clinical performance (e.g., sensitivity, specificity, AUC) or the device's measured performance against those criteria. Instead, it refers to "non-clinical tests" and "performance data" to demonstrate substantial equivalence to predicate devices in terms of safety and effectiveness, rather than meeting specific quantifiable performance benchmarks.

The "performance data" mentioned falls into these categories:

• Mechanical testing: Including tests required under relevant international standards, coating adhesion, particulate testing, and transportation integrity testing. Reported Performance: Performed to verify and validate the design and to demonstrate process capability, with results demonstrating comparable performance to predicates.
• Biocompatibility testing: Full range of tests. Reported Performance: Demonstrated biocompatibility.
• Sterilization Validation Adoption: To confirm sterility of the device upon exposure to the selected sterilization cycle. Reported Performance: Confirmed sterility.
• Accelerated age testing: To confirm product performance and end of shelf life. Reported Performance: Confirmed product performance and end of shelf life.

2. Sample size used for the test set and the data provenance

N/A. The document describes non-clinical engineering and material tests, not tests on patient data sets. Therefore, there is no "test set" in the context of clinical data, sample size, or data provenance (country of origin, retrospective/prospective). These tests are typically conducted in a laboratory setting on manufactured device samples.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

N/A. Since this isn't a diagnostic device and there's no clinical "test set" involving patient data, there's no ground truth established by medical experts in the way that would apply to, for example, an AI image analysis tool.

4. Adjudication method for the test set

N/A. As there is no clinical test set requiring expert adjudication, this is not applicable.

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

N/A. This device (an introducer sheath system) is a physical medical instrument, not an AI-powered diagnostic tool. Therefore, MRMC studies, human reader improvement with AI, or effect sizes are not relevant to its evaluation.

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

N/A. This device does not involve an algorithm.

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

The "ground truth" for this device's evaluation is based on engineering standards, material science, and manufacturing quality control. For example, the ground truth for mechanical testing would be the specified strength or flexibility required by relevant standards, and for biocompatibility, it would be the absence of adverse biological reactions. It's not a clinical "ground truth" derived from patient diagnosis.

8. The sample size for the training set

N/A. The concept of a "training set" applies to machine learning models. This device does not involve AI or machine learning.

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

N/A. As there is no training set, this is not applicable.

In summary: The provided document is an FDA 510(k) clearance letter and summary for a medical device (an introducer sheath). The evaluation framework for such a device focuses on "substantial equivalence" to existing predicate devices, primarily through non-clinical performance data (mechanical, biocompatibility, sterilization, aging). This is fundamentally different from the evaluation criteria and study types used for AI-powered diagnostic tools, which would involve clinical data, ground truth establishment by experts, and statistical performance metrics.

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