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Minntech Corporation's RenaClean™ SH Dialyzer Cleaning System is used to facilitate the cleaning of blood and other debris from multiple-use hollow fiber dialyzers, prior to being reprocessed on a Renatron® Dialyzer Reprocessing System.

The Renaclean SH Dialyzer Cleaning System facilitates the cleaning and removal of blood and other debris from multiple-use hollow fiber dialyzers prior to reprocessing on a Renatron® Dialyzer Reprocessing System. The stand-alone countertop system utilizes ANSI/AAMI quality water and a pre-diluted sodium hypochlorite solution.

The operator, through the use of a membrane switch front panel, controls the Renaclean SH Dialyzer Cleaning System. Operators have the following cycle choices: High Flux dialyzer clean, Low Flux dialyzer clean and System Sanitize.

This device, the RENACLEAN™ SH Dialyzer Cleaning System, is a medical device used to pre-clean dialyzers. The provided text is a 510(k) summary for this device, outlining its description, intended use, and a comparison to a predicate device, as well as the FDA's clearance letter. However, the provided text does not contain detailed acceptance criteria or a comprehensive study plan with specific performance metrics, sample sizes, or ground truth establishment methods typical for AI/ML device evaluations. This submission predates the widespread use of advanced AI/ML in medical devices, and the evaluation relies on functional testing and material compatibility, rather than AI-specific performance metrics.

Based on the information provided, here's a structured response addressing the requested points to the extent possible:

1. Table of Acceptance Criteria and Reported Device Performance

The submission states that "Minntech Corporation has performed functional testing to show the Renaclean Dialyzer Cleaning System is safe and has equivalent performance with respect to the predicate device." It also mentions "All materials have been tested for material compatibility with the chemicals used in the system as specified in the labeling."

Given the nature of the device (a dialyzer pre-cleaning system) and the submission type (510(k) summary from 2004), the "acceptance criteria" appear to be centered around functional equivalence and material compatibility with the predicate device and specified chemicals. Specific quantitative acceptance criteria are not detailed in the provided documents.

2. Sample Size Used for the Test Set and Data Provenance

The provided text does not specify the sample size used for the functional testing or material compatibility tests.
The data provenance (e.g., country of origin, retrospective/prospective) is also not mentioned.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications

Given that this is a functional device (a cleaning system) rather than an image analysis or diagnostic AI, the concept of "ground truth established by experts" in the typical AI sense does not directly apply. The evaluation would likely involve engineering and quality control specialists assessing cleaning efficacy and material integrity, but no specific number or qualifications of experts are provided.

4. Adjudication Method for the Test Set

Not applicable to this type of device and evaluation as described. There's no mention of expert review or adjudication in the context of diagnostic performance.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No. The provided information does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was conducted. This type of study is typically associated with diagnostic or image analysis AI/ML devices where human readers interpret data with and without AI assistance. This device is a pre-cleaning system for dialyzers, not a diagnostic tool.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

While the device operates "standalone" in the sense that it performs a pre-cleaning cycle automatically, the concept of "algorithm only without human-in-the-loop performance" as typically discussed for AI/ML devices is not directly applicable. The device's performance is intrinsically linked to its mechanical and chemical processes, not an AI algorithm that makes diagnostic or treatment recommendations. The mentioned "functional testing" would represent the standalone performance of the physical system.

7. Type of Ground Truth Used

For functional testing of a cleaning device, "ground truth" would likely involve objective measurements of cleaning efficacy (e.g., residual blood/debris levels) on dialyzers after the cleaning cycle, compared against established benchmarks or the performance of the predicate device. For material compatibility, it would be material integrity assessments after exposure to chemicals. The specific methodologies are not detailed in the summary.

8. Sample Size for the Training Set

Not applicable. The device is a physical cleaning system, not an AI/ML algorithm that requires a "training set" in the context of machine learning.

9. How the Ground Truth for the Training Set Was Established

Not applicable, as there is no "training set" for this type of device.

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