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PELNAC® Meshed Bilayer Wound Matrix is indicated for the management of wounds including:

• · partial and full-thickness wounds,
• · pressure ulcers,
• · venous ulcers,
• · diabetic ulcers,
• · chronic vascular ulcers,
• · surgical wounds (donor sites/grafts, post-Moh's surgery, post-laser surgery, podiatric, wound dehiscence),
• · trauma wounds (abrasions, lacerations, partial thickness burns, and skin tears), and
• · draining wounds.

The device may be used in conjunction with negative pressure wound therapy. The device is intended for one-time use.

PELNAC® Meshed Bilayer Wound Matrix is a collagen-based wound matrix that consists of two layers: a porcine derived sponge layer and a silicone film layer. Slits are added to the silicone and collagen layers to aid in the drainage of exudate. When applied to full-thickness skin defects, PELNAC® provides a scaffold for cellular invasion and capillary growth. The device is offered in sheet form of various sizes and is provided terminally sterilized by ethylene oxide, is for single patient use, and can only be applied to a patient by a qualified doctor in a professional setting for the management of full-thickness skin defects as described in its product labeling.

The provided document is a 510(k) Premarket Notification for the PELNAC® Meshed Bilayer Wound Matrix. It demonstrates substantial equivalence to a predicate device, INTEGRA™ Meshed Bilayer Wound Matrix (K081635), and a reference device, PELNAC™ Bilayer Wound Matrix (K191992).

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided text:

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

The document does not explicitly present a "table of acceptance criteria" with "reported device performance" in the format typically seen with quantitative thresholds for diagnostic accuracy (e.g., sensitivity, specificity, AUC). Instead, it relies on comparative testing to the predicate device and established standards for safety and performance for medical devices.

The acceptance criteria are implicitly met by demonstrating conformance to recognized standards and by showing similar performance characteristics to the predicate and reference devices in non-clinical tests.

However, based on the Non-Clinical Performance Data section, we can infer the following performance characteristics and the fact that they were met:

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 primarily discusses non-clinical performance testing. For these tests, specific sample sizes are not explicitly stated. For example, how many samples of the device were tested for "tensile strength" or "pore size" is not provided. The data provenance is also not specified in terms of country of origin or whether it's retrospective or prospective for these non-clinical tests. However, the tests are laboratory-based, performed according to recognized standards (e.g., ISO, ASTM, USP, FDA guidance).

The document states: "Except for new viral inactivation studies performed on the subject device, all other animal tissue testing was leveraged from the reference device (K191992)." and "All biocompatibility testing of the subject device was leveraged from the reference device (K191992)." and "All sterilization, packaging and shelf life testing of the subject device was leveraged from the reference device (K191992)." This implies that many of the non-clinical tests were not repeated for the subject device but relied on data from the previously cleared reference device.

For the NPWT compatibility testing, it mentions "Side-by-side testing was conducted between the subject device in conjunction with NPWT, the predicate device in conjunction with NPWT, and NPWT without a wound matrix group." This implies a comparative test setup, but the number of test units or replicates used in this "simulated wound model" is not specified.

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

Not applicable. The reported studies are non-clinical performance tests evaluating physical, chemical, and biological properties, as well as compatibility with another medical device (NPWT). These types of studies do not typically involve human expert adjudication to establish ground truth in the way clinical studies for diagnostic devices do. Conformance to specifications and standards is assessed by laboratory testing and technical evaluation.

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

Not applicable. As this is non-clinical performance and safety testing, there is no "adjudication" of results in the context of clinical expert agreement for a ground truth. Test results are compared against defined specifications and regulatory standards.

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. The device is a "wound matrix," not an AI-powered diagnostic tool, and the studies described are non-clinical. Therefore, no MRMC study or AI assistance evaluation was performed.

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

Not applicable. This device is not an algorithm or software requiring standalone performance evaluation.

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

For the non-clinical performance tests, the "ground truth" is defined by:

• Established industry standards (e.g., ISO 10993-1 for biocompatibility, ISO 11135 for sterilization, ASTM F1886 for packaging integrity).
• FDA guidance documents (e.g., regarding animal derived materials, viral safety).
• Product specifications for features like pore size, cross-linking, tensile strength, etc.
• Performance metrics of predicate/reference devices for comparative testing, particularly for NPWT compatibility.

There is no clinical "ground truth" derived from patient outcomes, pathology, or expert consensus required for this type of 510(k) submission focused on non-clinical substantial equivalence.

8. The sample size for the training set

Not applicable. This is a medical device for wound management, not a machine learning model. Therefore, there is no "training set" in the context of artificial intelligence/machine learning.

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

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

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Kerecis Silicone is indicated for the management of wounds including:

• Partial and full-thickness wounds
• Pressure ulcers
• Venous ulcers
• Chronic vascular ulcers
• Diabetic ulcers
• Trauma wounds (abrasions, lacerations, partial-thickness burns, skin tears)
• Surgical wounds (donor sites/grafts, post-Mohs surgery, post-laser surgery, podiatric, wound dehiscence)
• Draining Wounds

The subject device is a bilayer of processed resorbable acellular fish dermal matrix adhered to a thin, transparent, porous, soft silicone layer.
The subject device is obtained from fish skin via standardized controlled GMP manufacturing process. The fish dermal matrix layer is approximately 1 mm in thickness and is porous.
The silicone layer is a transparent polyurethane film single-coated with soft, medical grade silicone that is attached to the scaly side of the fish dermal matrix. The silicone layer is porous, soft and conformable to the wound surface.
The subject device is supplied as a sterile intact sheet offered in two configurations: a) with the silicone layer extending beyond the borders of the fish dermal matrix and b) the silicone layer having the same dimension as the fish dermal matrix with no silicone layer extending beyond the fish dermal matrix.
The silicone acts as: protection for the fish dermal matrix layer, as additional wound coverage, and in configuration (a), as an adhesive contact layer to the skin surrounding the wound.
The silicone layer can be peeled off as the fish dermal matrix is resorbed.
The device is intended for single use only.

The provided document is a 510(k) summary for a medical device (Kerecis Silicone) and does not describe a study involving an AI/Machine Learning device or a human-in-the-loop study.

This document describes a medical device, Kerecis Silicone, which is a bilayer wound dressing. The substantial equivalence determination is based on the technological characteristics and performance testing of the device itself (biocompatibility, physical properties, etc.), not on the performance of a software algorithm or AI.

Therefore, most of the requested information regarding AI/ML acceptance criteria, ground truth establishment, sample sizes for AI training/testing, expert adjudication, or MRMC studies for AI assistance is not applicable to this document.

However, I can extract the relevant information regarding the device's acceptance criteria (in terms of performance testing) and the study proving it meets these criteria based on the provided text.

Device: Kerecis Silicone (a bilayer wound dressing)
Regulatory Status: 510(k) clearance (K213231)

Acceptance Criteria and Reported Device Performance

The acceptance criteria for this medical device are based on demonstrating biocompatibility and physical/chemical characteristics that are comparable to or safe for its intended use, typically by meeting the requirements of established ISO and ASTM standards. The reported device performance is indicated by a "Pass" for each test.

Study Proving Device Meets Acceptance Criteria

The study proving the device meets the acceptance criteria is detailed under "7. PERFORMANCE TESTING" in the 510(k) summary. These are pre-clinical (biocompatibility, physical, and chemical) tests.

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

   • The document does not explicitly state the sample sizes for each specific biological or physical test. These tests generally use a small number of samples (e.g., typically n=3 or n=5 for physical tests, or a defined number of animals for in-vivo biocompatibility tests) as per the specific ISO/ASTM standards referenced.
   • The data provenance is not specified in terms of country of origin, but it is implied to be from laboratory testing supporting the device's manufacturing and regulatory submission. The tests are retrospective to the submission date.

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

   • This question is not applicable as this document does not describe a study involving image interpretation by experts or a clinical diagnostic AI. The "ground truth" here is established by validated laboratory testing methods governed by the cited ISO/ASTM standards, and the results are interpreted by qualified laboratory personnel.

3. Adjudication method for the test set:

   • Not applicable. There is no "adjudication" in the sense of expert consensus for laboratory tests. The results are typically quantitative or qualitative (e.g., cytotoxic/non-cytotoxic) based on direct measurement and predefined criteria within the respective standards.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:

   • No. This is not an AI/ML diagnostic device, so an MRMC study comparing human readers with and without AI assistance is irrelevant and was not performed.

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

   • Not applicable. This is a physical medical device, not a software algorithm.

6. The type of ground truth used:

   • The ground truth for this device's performance is established through adherence to recognized international standards (ISO, ASTM, ANSI/AAMI) for biological safety and physical properties. This involves direct laboratory measurements and observations under controlled conditions to determine if the device elicits specific biological responses or possesses particular physical characteristics as defined by these standards.

7. The sample size for the training set:

   • Not applicable. This concept applies to machine learning models, not to the pre-clinical performance testing of a physical medical device. The "training" for such a device involves product development and manufacturing process controls, not data-driven algorithm training.

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

   • Not applicable. As above, this pertains to AI/ML context.

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PELNAC® Wound Matrix is indicated for the management of wounds including:

• · partial and full-thickness wounds,
• · pressure ulcers,
• · venous ulcers,
• · diabetic ulcers,
• · chronic vascular ulcers,
• · tunneled/undermined wounds,
• · surgical wounds (donor sites/grafts, post-Moh's surgery, post-laser surgery, podiatric, wound deliscence),
• · trauma wounds (abrasions, lacerations, partial thickness burns and skin tears), and
• · draining wounds.

The device is intended for one-time use.

PELNAC® Wound Matrix is a single layer wound matrix of 100% atelocollagen sponge derived from porcine Achiles tendon that is applied directly to the wound surface. When applied to full-thickness skin defects, PELNAC® Wound Matrix provides a scaffold for cellular invasion and capillary growth. The device is offered in sheet form of various sizes and in two levels of thickness and is provided terminally sterilized by ethylene oxide. PELNAC® Wound Matrix is for single patient use and can only be applied to a patient by a qualified doctor in a professional setting for the management of full-thickness skin defects as described in its product labeling.

The provided document is a 510(k) Premarket Notification for the PELNAC® Wound Matrix. It primarily focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than presenting a study of the device's performance against specific acceptance criteria.

Based on the content, the document does not provide the information requested regarding acceptance criteria and a study proving the device meets those criteria.

Instead, the document details a comparative analysis for substantial equivalence, focusing on:

• Indications for Use: Demonstrating the PELNAC® Wound Matrix has the same indications as the predicate device (AVAGEN Wound Dressing).
• Technological Characteristics: Comparing material composition, construction, mode of action, and other attributes to the predicate and reference devices.
• Non-Clinical Performance Data: Listing the types of non-clinical tests performed (e.g., biocompatibility, sterilization, physical/chemical properties) and stating that results confirmed product specifications were met, leveraging data from a previously cleared device (K191992).

Therefore, I cannot populate the requested table or answer most of the specific questions as the information is not present in the provided text.

Here's why and what can be inferred:

• Acceptance Criteria & Reported Performance: Not explicitly stated as pass/fail criteria for a specific clinical or non-clinical performance study. The document states that "Results confirm that the product specifications for the subject device have been met" for the non-clinical tests, but the specifications themselves or the detailed results are not provided.
• Study Design: No clinical study proving device performance against acceptance criteria is described. The document explicitly states: "Clinical Performance Data: Not applicable to this submission."
• Sample Size, Data Provenance, Experts, Adjudication, MRMC, Standalone Performance, Ground Truth, Training Set: These are all concepts relevant to a clinical or AI-based performance study, none of which were conducted or are described for this 510(k) submission. The non-clinical tests mentioned (biocompatibility, sterilization, etc.) would have their own sample sizes and testing protocols, but these are not detailed in the summary.

In summary, the provided text describes a 510(k) submission focused on demonstrating substantial equivalence through a comparison of technological characteristics and leveraging existing non-clinical performance data, rather than a de novo study against explicit acceptance criteria.

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