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REXEED-S Series Dialyzer is intended for use in hemodialysis for the treatment of patients suffering from acute or chronic renal failure.

The line of Asahi REXEED-S Series Dialyzer (hereafter referred to as REXEED-S) is a family of high permeability hollow fiber dialyzers intended for the treatment of patients with acute or chronic renal failure.

REXEED-S is designed for single use.

REXEED-S is constructed of hollow fiber membrane housed within a plastic housing of Styrene-Butadiene block copolymer and is subject to gamma-ray irradiation prior to shipment.

This document is a 510(k) summary for a medical device called the Asahi REXEED-S Series Dialyzer, which is a high-permeability hemodialysis system. The submission describes modifications to the device's materials, specifically in the housing and stoppers, and aims to demonstrate substantial equivalence to previously cleared versions of the same device. Therefore, the "acceptance criteria" discussed are largely related to design verification rather than diagnostic performance metrics (like sensitivity, specificity, etc.) that would typically apply to an AI/ML medical device.

Based on the provided text, here's a breakdown of the requested information, focusing on the context of this specific device and submission:

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

Specific numerical acceptance criteria and performance values are not detailed in this summary document. The document states that "All test results met the acceptance criteria, and proved those modifications to be appropriate."

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

The document does not specify the sample size used for the test set or the data provenance. It refers generally to "Design verification tests."

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 provided in the document. Given that this is a material and design modification for a physical device, the "ground truth" would likely be established through engineering specifications, material science testing, and possibly clinical performance data (though not explicitly detailed here). The experts would be engineers, material scientists, and potentially medical professionals involved in hemodialysis.

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

Adjudication methods like 2+1 or 3+1 are typically used for establishing ground truth in diagnostic studies involving expert review. This is not applicable to this type of device modification submission, which focuses on engineering and material performance.

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

MRMC studies are relevant for diagnostic AI/ML devices. This is a submission for a physical medical device (dialyzer) with material modifications, so an MRMC study is not applicable and was not performed.

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

"Standalone" performance is relevant for AI/ML algorithms. This is a submission for a physical medical device, so this concept is not applicable.

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

For this device modification, the "ground truth" was established through:

• Engineering specifications: Standards for material properties, mechanical integrity, and functional performance (e.g., filtration rates, solute clearance).
• Material testing: Chemical and physical analysis of the new materials.
• Biocompatibility testing: To ensure safety when in contact with blood.
• Risk analysis (FMEA): To identify and mitigate potential failure modes.

8. The sample size for the training set

This concept is relevant for AI/ML models. This is a physical device, so there is no "training set" in the AI/ML sense. Design verification and validation are based on testing prototypes and production samples.

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

As there is no AI/ML "training set" for this device, this question is not applicable. The "ground truth" for verifying the device's performance (as described in point 7) was established through established engineering and scientific methodologies.

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