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The Hemocor HPH Mini Hemoconcentrator is intended for the relief or mitigation of overhydration, reduction of lower molecular weight constituents and to increase formed cellular elements and protein levels in the blood of patients during and/or after cardiopulmonary procedures.

The Minntech Hemocor HPH® Mini Hemoconcentrator is made of glycerin-free polysulphone membrane. Barbed luer adaptors of 3/16" and 1/4" for blood path connection and a no-rinse feature provide versatility for insertion of the device into the extracorporeal circuit.

This document describes a 510(k) premarket notification for a medical device, the Minntech Hemocor HPH® Mini Hemoconcentrator. The information provided focuses on demonstrating substantial equivalence to a predicate device through technological characteristics and performance testing. It does not contain information about the acceptance criteria and study design you've requested for typical AI/algorithm-based device evaluations.

Here's a breakdown of why this document doesn't fit your requested format and what information is missing:

Why this document doesn't fit your request:

• Device Type: This is a physical medical device (a hemoconcentrator), not a software/AI algorithm. Therefore, the concepts of "test set," "training set," "ground truth," "experts," "adjudication," "MRMC study," and "standalone performance" as they relate to AI/HIL performance are not applicable.
• Regulatory Pathway: This is a 510(k) submission, which focuses on demonstrating substantial equivalence to a legally marketed predicate device. This typically involves comparing device specifications and performance data, not clinical studies with "ground truth" and reader performance metrics in the way your questions imply for AI.
• Data Provided: The document details the device's physical characteristics, materials, and generic performance tests (e.g., Ultrafiltration Rate, Pressure Drop). It does not provide detailed information on clinical study design or outcomes related to diagnostic accuracy or human reader improvement.

Attempting to answer your questions based only on the provided text, while acknowledging the mismatch:

Given the nature of the device (a hemoconcentrator) and the provided 510(k) summary, the concepts of "acceptance criteria" and "study" in the context of an AI/algorithm-driven device are not directly applicable. The "acceptance criteria" here would be demonstrating that the device performs as intended and is substantially equivalent to the predicate, and the "study" is the performance testing described.

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

Note: The "acceptance criteria" are implied by the demonstration of substantial equivalence to the predicate device. The performance characteristics for the new device are directly compared to the predicate, and functional tests are listed as having been conducted to ensure effectiveness.

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

• Not Applicable / Not Provided. For a physical device like this, "test set" and "data provenance" in the context of diagnostic accuracy are not relevant. The "testing" refers to benchtop or in-vitro performance evaluations of the device's physical functions. The document does not specify the number of units tested, the origin of test data, or if it was retrospective/prospective.

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. Ground truth, in the sense of expert consensus for diagnostic cases, is not relevant for this physical device.

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

• Not Applicable. Adjudication is not relevant for this physical device.

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 study is not applicable to a physical hemoconcentrator.

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

• Not Applicable. This is a physical device, not an algorithm.

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

• Not Applicable. "Ground truth" as defined for AI/diagnostic algorithms is not relevant here. The "truth" is the device's physical and functional performance against established engineering and medical standards for hemoconcentrators.

8. The sample size for the training set

• Not Applicable. This is a physical device, not an AI algorithm. There is no training set.

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

• Not Applicable. This is a physical device, not an AI algorithm. There is no training set or associated ground truth.

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