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The ClearFlux™ Dialyzer Reprocessing System is indicated for the reprocessing of polysulfone-based high-flux dialyzers for reuse, for preprocessing the dialyzers prior to their assignment to patients for first use, and for tracking the reprocessed dialyzer for use only by the patient to whom the dialyzer was initially assigned. The steps used in reprocessing hemodialyzers with the ClearFlux™ System include: (1) pre-cleaning, (2) cleaning, (3) rinsing, (4) volume and leak testing, and (5) disinfecting the dialyzers in accordance with the "AAMI Recommended Practice for Reuse of Hemodialyzers." The ClearFlux™ System performs the patented in-situ two-phase cleaning cycle during reprocessing, which recovers the total cell volume and the clearance of small and middle molecules of the dialyzers to levels that are approximately equivalent to those of new dialyzers. The ClearFlux™ Dialyzer Reprocessing System is also indicated for performing record keeping of the dialyzer processing operation. The ClearFlux™ Dialyzer Reprocessing System is indicated to be used only with the ClearFlux Formula™ cleaning solution.

The ClearFlux™ Dialyzer Reprocessing System includes hardware and software designed to preprocess dialyzers before their first use by patients, to assign preprocessed dialyzers to patients, and to reprocess the dialyzer for reuse by the same patient to whom the dialyzer was originally assigned. The ClearFlux™ System employs the Novaflux patented two-phase flow cleaning process which is to be used only with the ClearFlux Formula™ (Cleaning Solution) in reprocessing reusable polysulfone-based high-flux dialyzers, and recovers the total cell volume (TCV) and the clearance of small and middle molecules of the dialyzers to levels that are approximately equivalent to those of new dialyzers.

The ClearFlux™ Dialyzer Reprocessing System consists of the following hardware and software components: the ClearFlux™ Machine(s), which performs the actual processing of the dialyzer; the ClearFlux™ Records Management System (CRMS), a patient-dialyzer tracking software loaded onto a System Computer to manage dialyzer reprocessing and reuse; a System Computer; a wireless hub for communication between the ClearFlux™ Machine(s) and the CRMS on the System Computer; a bar code reader to maintain identification of the dialyzers, the ClearFlux™ Machine(s), and the users of the System (e.g., technicians, administrators); a label printer to print the labels for the dialyzers, as well as to print the bar codes for the dialyzers. the users, and the ClearFlux™ Machine(s); a report printer; and an oil-less air compressor with a HEPA filter to provide the filtered airflow necessary for the operation of the ClearFlux™ Machine(s). The ClearFlux™ Dialyzer Reprocessing System (with one System Computer loaded with the CRMS software) can operate and track the dialyzer reprocessing operations of up to 12 ClearFlux™ Machines.

The chemicals used by the ClearFlux™ System include: Peracetic Acid Disinfectant used in the preprocessing and reprocessing of the dialyzers and in the daily and weekly disinfection of the fluid pathways of the ClearFlux™ Machine(s); the ClearFlux Formula™, a proprietary cleaning solution used exclusively in the ClearFlux™ Dialyzer Reprocessing System and in the weekly cleaning of the fluid pathways of the ClearFlux™ Machine(s); and optionally, Formula 409 NF for the weekly cleaning of the fluid pathways of the ClearFlux™ Machine(s).

Here's an analysis of the provided text regarding the ClearFlux™ Dialyzer Reprocessing System's acceptance criteria and study:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical acceptance criteria in a structured table. Instead, it describes general performance goals relative to new dialyzers.

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

The document does not specify a distinct "test set" sample size in the context of typical AI/software studies. The performance claims are based on "in vitro and functional tests." There is no information provided on the sample size (number of dialyzers tested) or the provenance (country of origin, retrospective/prospective) of the data used for these non-clinical performance studies.

3. Number of Experts Used to Establish Ground Truth and Qualifications

This information is not applicable to this type of device and study. The "ground truth" here refers to objective measures of dialyzer physical and functional characteristics (e.g., TCV, clearance values, integrity). These are established through laboratory assays and instrumentation, not expert consensus.

4. Adjudication Method

This is not applicable as the "ground truth" is established through objective measurements, not subjective expert reviews requiring adjudication.

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

A MRMC comparative effectiveness study was not performed, nor is it relevant to this device. This device is an automated reprocessing system, not an imaging AI diagnostic aid that would involve human readers.

6. Standalone (Algorithm Only) Performance Study

The entire study described is essentially a standalone performance evaluation of the ClearFlux™ Dialyzer Reprocessing System. The system, through its hardware and software, performs the reprocessing automatically. The "non-clinical performance studies" directly assess the algorithm/system's effectiveness in restoring dialyzer function and not causing adverse effects, without human intervention during the reprocessing itself.

7. Type of Ground Truth Used

The ground truth used is based on objective laboratory measurements and tests of dialyzer performance and integrity. This includes:

• Total Cell Volume (TCV) measurements
• Clearance rates of small and middle molecules
• Tests for adverse effects: Cytotoxicity, Scanning Electron Microscopy, X-Ray Photoelectron Spectroscopy, Size Exclusion Chromatography, Complement Activation, Albumin Loss, Hydraulic Permeability, and Pressure Leak.
• Comparison to "new dialyzers" serves as the reference ground truth for desired performance levels.

8. Sample Size for the Training Set

The document does not specify a training set sample size. This is not a typical machine learning AI model that requires a distinct training set in the sense commonly understood in AI development. The "learning" for this system is embedded in its patented two-phase flow cleaning process and algorithms for controlling machine operations, which would have been developed and refined through engineering and iterative testing, rather than supervised machine learning on a large dataset.

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

Since there isn't a "training set" in the conventional AI sense, there's no ground truth established for it. The development of the ClearFlux™ System's cleaning process and algorithms would have been guided by biomedical engineering principles, understanding of dialyzer function, and iterative testing to optimize the recovery of TCV and clearance while ensuring dialyzer integrity. The effectiveness was then demonstrated through the "non-clinical performance studies" described.

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To aid in the cleaning of the headers, header spaces, and header caps of multiple-use hemodialyzers prior to an approved reprocessing procedure for reusable hemodialyzers.

The ASSIST Header Cleaner™ is a one-piece device used to assist with the manual cleaning of headers in reusable hemodialyzers. One end of the ASSIST unit is inserted and attached to the arterial blood port on the header cap. The other end of the ASSIST unit is connected to a supply of reverse osmosis water. Jets of RO water are sprayed inside of the header to break up and clear away any debris, including clotted blood, in the header space. The dialyzer is manually turned along its longitudinal axis during this process to allow the water jets to contact all side surfaces of the header. This operation is repeated on the venous end where, besides cleaning, the water is used to flush the blood/debris/water mixture from the arterial end of the dialyzer. The dialyzer is flipped again and the process is repeated for a second time at the arterial end to flush away any blood/debris/water mixture from the venous end of the dialyzer.

This submission does not contain information typically found in a study for an AI/ML device. The "ASSIST Header Cleaner™" is a non-AI manual device designed to assist in cleaning hemodialyzer headers. Therefore, the questions related to AI/ML device performance, ground truth, expert adjudication, and comparative effectiveness studies are not applicable.

Here's an analysis based on the provided text, addressing the relevant sections:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not specify quantitative acceptance criteria. Instead, it makes a general statement about the device's performance relative to existing technology.

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

The document states that "Tests have been performed," but does not provide any details regarding sample size, type of test set (e.g., dialyzers tested), or data provenance (e.g., country of origin, retrospective or prospective). Given the nature of the device (manual cleaning tool), the "test set" would likely refer to the number of hemodialyzers on which the cleaning performance was evaluated and the impact on the dialyzer's integrity.

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

This information is not provided. For a non-AI device like this, "ground truth" would likely refer to objective measurements of cleanliness or dialyzer integrity, rather than expert interpretation of medical images.

4. Adjudication Method for the Test Set

No adjudication method is mentioned.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and if so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not Applicable. This is a manual medical device, not an AI/ML algorithm. Therefore, an MRMC comparative effectiveness study involving human readers and AI assistance is not relevant. The comparison mentioned is between the new device and existing "dialyzer cleaning systems currently in use in the United States."

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

Not Applicable. This is not an AI algorithm; it's a manual device. It can be considered a standalone device, as its function doesn't rely on human interpretation of its internal "decisions" or AI output, but its effectiveness is still predicated on human operation.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

The document does not specify the type of ground truth used for testing. For a device cleaning hemodialyzers, the ground truth would likely involve:

• Visual inspection: To confirm removal of visible debris/blood.
• Quantitative measurements: Such as residual blood protein assays, or testing of dialyzer performance parameters (e.g., clearance, ultrafiltration coefficient) after cleaning and reprocessing to ensure no damage.
• Material compatibility tests: To ensure the device does not leach harmful substances or damage the dialyzer materials.

8. The Sample Size for the Training Set

Not Applicable. This is a manual device, not an AI/ML model, so there is no "training set."

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

Not Applicable. As there is no training set for an AI/ML model, this question is not relevant.

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SteriChek® Glutaraldehyde Reagent Strips provide a convenient means of testing the concentration of Glutaraldehyde in dialyzer disinfecting solutions. The test is not intended to replace microbiological tests or quantitative determinations of concentrations of stock solutions of Glutaraldehyde. The test may be used on a random sample of reprocessed dialyzers in order to document the presence of the agent during storage. SteriChek® Glutaraldehyde Reagent Strips do not measure residual levels of Glutaraldehyde

The device is made up of a 0.20 inch square light red reagent pad that has been chemically treated to test the concentration of Glutaraldehyde in solutions for disinfecting dialyzers used in Hemodialysis. The pad is affixed to one end of a 3.25 inch by 0.20 inch white opaque polystyrene strip.

The provided text describes the SteriChek® Glutaraldehyde Reagent Strips, a qualitative reagent strip method designed to measure Glutaraldehyde concentration in dialyzer disinfecting solutions. The regulatory submission (K040660) focuses on establishing substantial equivalence to a predicate device, the Serim™ Glutaraldehyde Test Strips.

Here's an analysis of the requested information based on the provided text:

1. Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical acceptance criteria in the format of a table. However, it indicates the device's performance characteristics are based on analytical studies and that it effectively measures the pH or hydrogen ion concentration, which is the underlying mechanism for both the proposed device and its predicate.

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

The document states, "Assessment of The performance characteristics of SteriChek Glutaraldehyde Reagent Performance: Strips are based on analytical studies using samples of Glutaraldehyde solutions of various concentrations."

• Sample Size for Test Set: Not specified. The document only mentions "samples of Glutaraldehyde solutions of various concentrations."
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective). Due to the nature of the device (reagent strips for chemical testing), it's highly likely to be prospective laboratory studies.

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

This information is not provided in the document. The ground truth was established by a reference method, not by human experts interpreting the device's output.

4. Adjudication Method for the Test Set

Not applicable. The ground truth was established by an analytical reference method for Glutaraldehyde concentration, not by human interpretation requiring adjudication.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done

No, an MRMC comparative effectiveness study was not done. This type of study is typically used for diagnostic imaging or clinical interpretation where human readers are involved. This device is a chemical reagent strip.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) Was Done

The device itself is a standalone chemical test; therefore, its performance assessment is inherently standalone. The study evaluates the reagent strip's ability to react to Glutaraldehyde solutions. There is no "algorithm" in the modern sense as this device is a chemical indicator system.

7. The Type of Ground Truth Used

The ground truth for the performance assessment was established using a reference chemical assay method: "The reference method for determination of Glutaraldehyde is based on the reaction of Glutaraldehyde with hydroxylamine hydrochloride followed by titration of the released acid (Aldrich Chemical Company, "Basics Assay Method for Determination of Determination of Aldehydes and Ketones".)"

8. The Sample Size for the Training Set

Not applicable. The device is a chemical reagent strip. There is no machine learning "training set" in the context of this device.

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

Not applicable, as there is no "training set" for this type of device. The chemical formulation and performance were likely developed through standard chemical and product development processes.

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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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The MAKY 21.1 is indicated for use in automating the in-vitro rinsing, cleaning, testing and disinfection of hollow fiber type hemodialyzers in accordance with the AAMI Recommended Practice for Reuse of Hemodialyzers when such automation is chosen for use by a prescribing physician. It is also indicated for record keeping of these functions.

The MAKY 21.1 Dialyzer Reprocessing System is designed and manufactured to reprocess standard, high efficiency, and high flux hollow-fiber dialyzers. Each type of dialyzer can be processed by one of two pre-programmed cycles, or by a custom cycle configured by HDC Maquinolas, LLC. Custom cycles meet the same minimum requirements as the pre-programmed cycles. The MAKY 21.1 may be configured to utilize one of two approved cleaning agents (peracetic acid, bleach), and one of three approved disinfection agents (peracetic acid, glutaraldehyde, formaldehyde). The MAKY 21.1 will automatically dilute the cleaning and disinfection agents, with the exception of glutaraldehyde, which must be diluted by the user.

The provided text does not contain acceptance criteria or detailed information about a study proving the device meets specific performance metrics in the way typically expected for a medical device efficacy study (e.g., sensitivity, specificity, accuracy, or expert agreement).

Instead, the document is a 510(k) summary and an FDA clearance letter for a medical device (MAKY 21.1 Dialyzer Reprocessing System), focusing on substantial equivalence to predicate devices rather than a de novo clinical performance study.

Here's a breakdown of what can be extracted and what is missing based on your request:

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

• Acceptance Criteria: Not explicitly stated as quantifiable metrics (e.g., "must achieve 90% cleaning efficiency"). The criteria are implicitly tied to performing "as intended" and being "substantially equivalent" to predicate devices.

• Reported Device Performance:

  • "Each function of the MAKY 21.1 System was tested to see if it performed as intended."
  • "In-vitro testing was also performed to assure the MAKY 21.1 properly diluted the cleaner/disinfectant concentrate to the in-use concentrations of active ingredients."
  • "All materials have been tested for material compatibility with the chemicals used in the system as specified in the labeling."
  • "The results from these tests show that the MAKY 21.1 performed as expected."

  Table (based on available information):

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: Not specified. The document only references "testing" and "in-vitro testing" without detailing the number of dialyzers, tests performed, or the duration/scope of these tests.
• Data Provenance: Not specified.

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. This type of information is typically for studies involving human interpretation or subjective assessments. The testing described is functional and chemical, not involving expert interpretation of results to establish ground truth in the context of diagnostic accuracy.

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

• Not applicable. Adjudication methods are used when multiple human experts interpret data to establish ground truth, which is not the nature of the described testing.

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. This device is a reprocessing system, not an AI-assisted diagnostic tool.

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

• Not explicitly stated in these terms. The document describes the system performing its functions ("automating the in-vitro rinsing, cleaning, testing and disinfection"). The "testing" mentioned is likely functional testing of the device's automated processes itself, rather than an "algorithm only" performance evaluation against an external benchmark. The closest concept is the "in-vitro testing" to assure proper dilution, which would be a standalone test of that function.

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

• The "ground truth" for the functional tests seems to be the expected operational parameters of the device (performing as intended, accurate dilution) and adherence to established standards/specifications (e.g., "in-use concentrations of active ingredients," "material compatibility"). For the substantial equivalence claim, the ground truth is the performance and characteristics of the predicate devices.

8. The sample size for the training set

• Not applicable. This device does not use a "training set" in the context of machine learning or AI that would typically require such a data set.

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

• Not applicable for the same reasons as #8.

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The Amuchina Automatic Reprocessing Machine (ARM) is a device designed for both (a) reprocessing hemodialyzers for reuse, and (b) preprocessing hemodialyzers prior to first use. Reprocessed hemodialyzer will be reused on the same patient on who originally used the hemodialyzer. Both the reprocessing and preprocessing procedures use peracetic acid/hydrogen peroxide based disinfectant.

The Amuchina ARM Automatic [Dialyzer] Reprocessing Machine is a stand alone device designed for the automated reprocessing of hemodialyzers for reuse and for the pre-processing of hemodialyzers prior to first use. The ARM Unit has 4 stations which can sequentially process up to 4 dialyzers at one time. The ARM Unit has no direct or indirect patient contact. The ARM Unit uses a peracetic acid/hydrogen peroxide based disinfectant as both a cleaning solution and a disinfectant. The disinfectant concentrate is diluted to the inuse strength with AAMI quality water. When reprocessing dialyzers, the ARM Unit uses the following cycles: Rinse, Cleaning, Flush, Volume & Leak Test, and Disinfection. When pre-processing dialvzers, the following cycles are used: Flush, Volume & Leak Test (only if instructed for by the user), and Disinfection. For regularly scheduled maintenance, the ARM Unit has the following system cycles: System Rinse, System Disinfect, and System Self Test. Other cycles which are included in the ARM Unit include: Prime Pump for priming the chemical pump with the disinfecting agent, Line Volume Calibration for use in determining the total cell volume of the dialyzer, and System Void to purge fluids from the circuits prior to moving the machine. The ARM Unit incorporates the feature of including a patient photograph on the dialyzer label, thus reducing the possibility of reused dialyzers being used on the wrong patient.

Here's an analysis of the provided 510(k) summary regarding the Amuchina ARM Automatic Reprocessing Machine:

1. Table of Acceptance Criteria and Reported Device Performance

The 510(k) summary for the Amuchina ARM Automatic Reprocessing Machine (K013713) does not explicitly present a table of "acceptance criteria" in the typical sense of quantitative performance metrics with associated thresholds (e.g., sensitivity > 90%, specificity > 85%). Instead, the "acceptance criteria" are implied by the comparative features and the results of the non-clinical performance and in-vitro testing.

The primary acceptance criterion appears to be functional equivalence to the predicate device (Seratronic DRS-4 Dialyzer Reprocessing System, K860674) for the intended uses of reprocessing and pre-processing dialyzers. The device's performance is demonstrated by functionality tests and in-vitro testing confirming proper dilution of the disinfectant.

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

The document does not specify a sample size for a "test set" in the context of a clinical study or a large-scale data analysis. The testing described is "Non-Clinical Performance Data" and "In-vitro testing."

• Test Set Sample Size: Not specified in terms of number of dialyzers or patient cases. The description implies a series of functional tests on the device itself and in-vitro tests on the dilution capabilities.
• Data Provenance: The testing is described as non-clinical and in-vitro, meaning it was likely conducted in a controlled laboratory environment by the manufacturer (Amuchina International, Inc.). There is no mention of country of origin for data specifically, beyond the company's address in Gaithersburg, MD, USA. It is retrospective in the sense that the testing was completed before the 510(k) submission. No prospective clinical data is mentioned.

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

This information is not provided in the summary. For non-clinical and in-vitro functional testing of a device like this, ground truth is typically established by engineering specifications, chemical analysis, and adherence to established standards (e.g., AAMI quality water). There's no mention of human experts interpreting results for a test set in the way a radiologist might for medical images.

4. Adjudication Method for the Test Set

This is not applicable as there's no mention of human interpretation of results requiring adjudication (e.g., by multiple experts). The tests described are objective functional and chemical 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 MRMC comparative effectiveness study was done, nor is it applicable. This device is an automated reprocessing machine; it does not involve human "readers" or AI assistance for diagnostic interpretation. Its function is to mechanically and chemically prepare dialyzers for reuse.

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

Yes, a form of standalone performance was demonstrated through the non-clinical and in-vitro testing. The device's "individual functions" and its ability to "properly dilute the cleaner/disinfectant concentrate" were tested as an automated system without human intervention influencing the core performance metrics. The machine itself is designed to operate without a "human-in-the-loop" for its primary reprocessing functions, beyond operator setup and maintenance.

7. The Type of Ground Truth Used

The ground truth used for this device's evaluation is primarily based on:

• Engineering Specifications and Design Requirements: Ensuring each function performs "as intended/programmed."
• Chemical Analysis: Verifying "in-use concentrations of active ingredients" following dilution.
• Comparison to Predicate Device's Performance Characteristics: Demonstrating substantial equivalence to the Seratronic DRS-4.
• Adherence to Standards: Implicitly, AAMI quality water standard.

There is no mention of pathology, outcomes data, or expert consensus in a clinical diagnostic sense.

8. The Sample Size for the Training Set

There is no mention of a training set in this 510(k) summary. This type of device does not utilize machine learning or AI models that require a "training set." Its functionality is based on programmed logic and mechanical/chemical processes.

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

As there is no training set mentioned or applicable for this device, this question is not applicable.

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Amukin-D is a chlorine-based, high-level disinfectant for reprocessing hemodialyzers.

Amukin-D is a chlorine-based, high-level disinfectant for reprocessing hemodialyzers.

This document is an FDA 510(k) clearance letter for a medical device called Amukin-D, a disinfectant for hemodialyzers. It confirms that the device is substantially equivalent to a legally marketed predicate device.

However, the provided text does not contain any information regarding acceptance criteria, device performance studies, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, ground truth types, or training set details.

Therefore, I cannot fulfill your request for a table of acceptance criteria and device performance, or any of the other detailed study information, as this data is not present in the provided document. The letter is solely a regulatory approval based on equivalence, not a summary of performance data from a specific study.

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Minntech Corporation's RenaClear™ Dialyzer Cleaning System is used to facilitate the cleaning of blood and other debris from multiple-use hollow fiber dialyzers prior to being reprocessed in an approved dialyzer reprocessing program.

The RenaClear™ Dialyzer Cleaning System facilitates the cleaning and removal of blood and other debris from multiple-use hollow fiber dialyzers prior to an institutions approved dialyzer reprocessing program. The stand-alone countertop system is equipped with two header cleaners that attach to the blood ports of a dialyzer. The header cleaners have a retractable, rotating, directional needle jet that will inject a water/air mixture into the dialyzer header and blood pathway. Simultaneously the dialyzer dialysate compartment and the exterior of the dialyzer fibers will be pressurized water to achieve reverse ultrafiltration.

The operator, through the use of a membrane switch front panel, controls the RenaClear™ Dialyzer Cleaning System. Operators have the following cycle choices: Dialyzer clean, system clean, system sanatize and system rinse.

Here's a breakdown of the acceptance criteria and study information for the RENACLEAR™ DIALYZER CLEANING SYSTEM based on the provided text:

1. Acceptance Criteria and Reported Device Performance

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

The provided text does not specify the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective). It only states that "Minntech Corporation has performed functional testing."

3. Number of Experts Used to Establish Ground Truth and Qualifications of Experts

The provided information does not mention using experts to establish ground truth or their qualifications. The evaluation appears to be based on functional testing and material compatibility.

4. Adjudication Method for the Test Set

The provided text does not describe any adjudication method for a test set. This type of detail is typically relevant for studies involving human interpretation or subjective assessment, which doesn't seem to be the primary focus here.

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

The provided text does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done, nor does it mention any effect size for human reader improvement with or without AI assistance. This type of study is not relevant for a device focused on mechanical cleaning.

6. Standalone (Algorithm Only) Performance Study

The provided text does not describe a standalone (algorithm only) performance study. The device is a physical cleaning system, not a software algorithm.

7. Type of Ground Truth Used

The ground truth for this device's evaluation appears to be based on:

• Functional performance criteria: Showing the system performs its intended cleaning function.
• Material compatibility testing: Verifying that the materials used are suitable for the chemicals involved.
• Comparison to a predicate device: Demonstrating that its performance is equivalent to an already approved device.

There is no mention of expert consensus, pathology, or outcomes data as a ground truth for cleaning efficacy in the provided summary.

8. Sample Size for the Training Set

The provided text does not mention a training set sample size. This concept is typically relevant for machine learning or AI models, which is not the nature of this device.

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

Since there is no mention of a training set, the establishment of its ground truth is not applicable to this device description.

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Micro-X Potency Test Strips are intended for use as a semi-quantitative test of the presence of peracetic acid in dialyzers after reprocessing. This device may be used with all peroxyacetic/peracetic acid germicides used in the reprocessing of dialyzers as part of an established quality assurance program.

Micro X Potency Test Strips are approximately 0.25" x 2.0" packaged 100strips /container. The strips are labeled for use in Reprocessing Hemodialyzers. Micro Xen Potency Test Strips are impregnated with an indicator solution which is activated by the presence of peracetic acid. The reaction results in a color change of the indicator which provides visual confirmation of the presence of peracetic acid.

The provided text describes a medical device called "Micro X Potency Test Strips" and its 510(k) submission. However, the document does not contain explicit "acceptance criteria" in a tabulated format or a detailed description of a "study that proves the device meets the acceptance criteria" in the way one would expect for a quantitative performance study involving AI or complex diagnostics.

The core of the submission is a demonstration of substantial equivalence to a predicate device, not necessarily a demonstration of meeting predefined numerical performance targets in a standalone study.

Here's an attempt to extract and infer the requested information based on the provided text, while acknowledging the limitations of what's available:

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

Based on the document, the primary "acceptance criterion" is the demonstration of "identical performance" to the predicate device. The device's fundamental characteristic is a binary output (YES/NO) for the presence of peracetic acid above a minimum level.

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

The document states: "Comparative testing between Micro X Potency Strips, Renalin Indicator Test Strips indicate identical performance among the test strips."

• Sample Size for Test Set: Not specified. The phrase "among the test strips" suggests multiple strips were tested, but no specific number is provided.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). It appears to be a laboratory comparison performed by the manufacturer.

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

This information is not provided. The "ground truth" for this type of chemical indicator would typically be established by known concentrations of peracetic acid solutions, rather than expert interpretation of a complex image or signal.

4. Adjudication method for the test set

Not applicable/Not specified. Given the nature of a chemical indicator changing color, visual observation is the method. There's no indication of multiple readers adjudicating results or complex decision-making.

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 Study: No.
• Effect Size: Not applicable. This device is a simple chemical indicator, not an AI-powered diagnostic tool.

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

No. This is a manual chemical indicator, requiring human visual interpretation of a color change. It is not an algorithm or an AI device.

7. The type of ground truth used

The implicit ground truth is the known presence/absence and concentration of peracetic acid solutions. The device is activated by the presence of a minimum level of peracetic acid.

8. The sample size for the training set

Not applicable. This device does not use machine learning or AI, so there is no concept of a "training set."

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

Not applicable. There is no training set for this device.

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SteriChek™ Residual Peroxide Reagent Strips provide a convenient means for measuring the concentration of peroxide remaining in water being used to rinse out dialysate lines and dialyzers following disinfection with peroxide/peracetic acid.

The device is made up of a 0.20 square inch off-white reagent pad that is chemically treated to detect peroxide in water. The pad is affixed to one end of a 3.25 inch by 0.20 inch white opaque polystyrene strip.

The provided text does not contain detailed information about specific acceptance criteria or a dedicated study with performance metrics in the way a typical medical device study would describe. The document is a 510(k) Safety and Effectiveness Summary for the SteriChek™ Residual Peroxide Reagent Strips.

This type of submission focuses on demonstrating substantial equivalence to a predicate device rather than presenting a de novo performance study against defined acceptance criteria.

However, based on the information provided, we can infer some aspects and highlight what is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

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