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OTC: ExSept WC Skin and Wound Cleanser is intended for cleansing and removal of dirt, debris and foreign material from minor skin abrasions, minor lacerations, minor irritations, minor cuts, and intact skin.

Professional Use: Under supervision of healthcare professionals, ExSept WC Skin and Wound Cleanser is intended for use for mechanical cleansing, debridement and removal of foreign material and debris from exudating and/or dirty wounds, such as stage I-IV pressure ulcers, diabetic foot ulcers, post surgical wounds, first and second degree burns, grafted and donor sites, and catheter exit sites.

ExSept WC Skin and Wound Cleanser is a clear to slight yellow, slight chlorine odor, solution which is intended for the mechanical cleansing of debris and foreign material from exudating/dirty epidermal and dermal wounds. It is supplied in a 200 ml pump-spray and 100 ml, 250 ml and 500 ml pour bottles. ExSept WC Skin and Wound Cleanser has a 30 month shelf life.

The provided text describes a medical device, the "ExSept WC Skin and Wound Cleanser," and its regulatory clearance process (510(k) submission). However, it does not include the detailed information requested regarding specific acceptance criteria, a structured study with a test set, ground truth establishment, or performance metrics in the format of the questions provided.

The document primarily focuses on establishing substantial equivalence to predicate devices based on intended use, mechanism of action (mechanical cleansing), and general safety/efficacy. It mentions "numerous in-vitro and in-vivo studies" and "stability studies" but does not provide details about their methodology, specific acceptance criteria, or quantitative results.

Therefore, I cannot populate most of the requested fields. Here's a breakdown of what can be extracted and what is missing:

Information NOT present in the provided text:

• A table of acceptance criteria and the reported device performance: No specific quantitative acceptance criteria or performance metrics are detailed.
• Sample size used for the test set and the data provenance: No information on a specific test set, its size, or origin.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: No mention of ground truth experts.
• Adjudication method for the test set: Not applicable as no test set data is provided.
• If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and its effect size: Not applicable as this is not an AI/imaging device.
• If a standalone (i.e. algorithm only, without human-in-the-loop performance) was done: Not applicable as this is a wound cleanser, not an algorithm.
• The type of ground truth used: Not explicitly stated beyond general "efficacy, safety and biocompatibility."
• The sample size for the training set: No information on a training set.
• How the ground truth for the training set was established: Not applicable.

Information that can be inferred or directly stated:

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For debridement and the removal of foreign material and debris from exudating and/or dirty wounds, abrasions and minor irritations, cuts, exit sites, and intact skin.

ExSept WC is a clear to slight yellow, slight chlorine odor, solution which is intended for the mechanical cleansing of debris and foreign material from exudating/dirty epidermal and dermal wounds. It is supplied in a 200 ml pump-spray and a 100 ml, 250 ml, and 500 ml pour white, opaque, bottles. ExSept WC has a shelf-life of 30 months.

The provided 510(k) summary is for a wound cleanser (ExSept WC), which is a medical device. Medical devices, especially wound cleansers, typically do not undergo the same type of performance studies (e.g., studies using AI, human readers, or image analysis) as diagnostic or imaging devices. The document focuses on demonstrating substantial equivalence to predicate devices based on non-clinical in-vitro testing for biocompatibility and shelf-life, rather than clinical performance metrics in the way a diagnostic AI would.

Therefore, the requested information (acceptance criteria and study details for device performance like those used in AI/diagnostic studies) is not present in the provided text. The device approval is based on substantial equivalence to existing predicate devices (Allclenz Wound Cleaner, CarraKlenz Wound Cleanser, Dermacyn Wound Cleanser) in terms of its "cleansing functions and intended uses."

Here's a breakdown of what can be extracted or inferred based on the document, and what cannot:

1. Table of Acceptance Criteria and Reported Device Performance:

• Acceptance Criteria: Based on the document, the acceptance criteria are not explicitly stated in terms of performance metrics like sensitivity, specificity, accuracy, or effect size. Instead, the primary acceptance criterion appears to be demonstrating substantial equivalence to predicate devices. This equivalence is based on the device having the same intended use and similar technological characteristics (a flushing action to remove foreign material and debris from wounds).
• Reported Device Performance: The document states: "Non-clinical in-vitro testing was performed to demonstrate the biocompatibility of ExSept WC for it's intended purpose and to support the 30 month shelf-life for the product." And the conclusion is: "Based upon the information in the 510(k) submission, ExSept WC will perform as intended as a wound cleanser, and is substantially equivalent in its actions and functions as the marketed devices Allclenz, CarraKlenz, and Dermacyn."
  • Biocompatibility: The device passed biocompatibility testing. No specific metrics are given.
  • Shelf-life: The device met the criteria for a 30-month shelf-life. No specific metrics are given.
  • Cleansing Function: The device is considered substantially equivalent in its cleansing function to predicate devices. No specific quantitative performance metrics for cleansing are provided.

Therefore, a table cannot be constructed with quantitative performance metrics for "device performance" in the way one would for an AI or diagnostic device.

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

• Not applicable/Not provided. The testing mentioned is "non-clinical in-vitro testing" for biocompatibility and shelf-life, not clinical performance testing with a "test set" of patient data.

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

• Not applicable/Not provided. Ground truth establishment for a diagnostic test set is not relevant for a wound cleanser's substantial equivalence determination.

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

• Not applicable/Not provided. Adjudication methods are relevant for expert review of diagnostic cases, which is not part of this submission.

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/Not done. This device is a wound cleanser, not an AI-assisted diagnostic tool.

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

• Not applicable/Not done. This is not an algorithm or AI device.

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

• Not applicable. For in-vitro biocompatibility and shelf-life testing, "ground truth" would refer to established laboratory standards and methods rather than clinical outcome data or expert consensus on patient cases.

8. The sample size for the training set:

• Not applicable. There is no AI algorithm being trained for this device.

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

• Not applicable. There is no AI algorithm being trained for this device.

In summary, the 510(k) summary for ExSept WC wound cleanser confirms its substantial equivalence based on non-clinical in-vitro testing for biocompatibility and shelf-life, and its similarity in function and intended use to existing predicate devices. It does not involve performance studies typically associated with AI or diagnostic imaging devices.

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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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