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Apis is indicated for use in management of wounds, including

• · full and partial thickness wounds
• · pressure ulcers (stages I-IV)
• venous stasis ulcers
• · diabetic ulcers
• abrasion
• · surface wounds
• · traumatic wounds (healing by secondary intention)
• · donor site wounds
• · surgical wounds

Apis, for the management of wounds, is a sterile, single-use device. Apis is a conformable solid sheet that is biodegradable and absorbable. Apis, in its final form, is comprised of a highly purified collagen derivative (gelatin) from porcine skin, Manuka honey, and hydroxyapatite (HAp).

The intended use of Apis in the management of wounds is to cover and protect the wound, absorb wound exudate, and provide and maintain a moist wound environment. The structural reinforcement of Apis is achieved through the incorporation of hydroxyapatite into the crosslinked collagen derivative structure. The absorption and moisture-retaining properties are based on the manufacturing process and incorporation of honey into the crosslinked structure.

The device is biodegradable and absorbs into the surrounding tissue. The degradation occurs through natural processes such as enzymatic breakdown, hydrolysis and other chemical/biological reactions depending on wound type and level of exudation.

This document is a 510(k) Summary for a medical device called "Apis," a wound dressing. It focuses on demonstrating substantial equivalence to predicate devices rather than proving the device meets specific performance criteria through a rigorous clinical study with AI components.

Therefore, the information requested in your prompt related to AI performance, such as "effect size of how much human readers improve with AI vs without AI assistance," "standalone (i.e. algorithm only without human-in-the-loop performance)," "number of experts used to establish the ground truth," and "sample size for the training set," cannot be found or inferred from this document. This document is a regulatory submission for a wound dressing, not an AI-powered diagnostic device.

However, I can extract information related to the device's technical characteristics and the non-clinical testing performed to demonstrate its safety and effectiveness relative to predicate devices.

Here's the information that can be extracted or inferred from the provided text, adapted to the closest relevant categories of your request:

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

The document does not explicitly state "acceptance criteria" in the format of quantitative thresholds for the Apis device's performance. Instead, it aims to demonstrate substantial equivalence to predicate devices by comparing their technological characteristics and intended use. The "performance" is described as meeting the same functional goals as the predicate, supported by non-clinical testing.

Table: Comparison of Apis to Predicate Device (Elasto-Gel™ Manuka Honey Wound Dressing)

Nonclinical Testing Performed (Functioning as "Performance Data"):

• Fluid uptake
• Conformability
• Porosity testing
• Mechanical testing
• Endotoxin level testing
• Chemical composition testing
• Sterilization testing
• Distribution testing
• Shelf-life testing

Biocompatibility Testing:

• Cytotoxicity
• Sensitization
• Irritation
• Acute systemic toxicity
• Material-mediated pyrogenicity
• Subchronic toxicity
• Implantation testing
• Chemical characterization and toxicological risk assessment (for chronic toxicity, genotoxicity, carcinogenicity)

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

This document primarily describes non-clinical testing (e.g., fluid uptake, mechanical testing, biocompatibility). It does not mention a "test set" in the context of human data or AI model validation. Therefore, there's no information on sample size for a test set of data provenance in that sense. The "sample" here refers to the physical device samples used for laboratory testing.

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

Not applicable. This is a medical device (wound dressing) submission, not an AI diagnostic submission. Ground truth would relate to the specific non-clinical tests performed (e.g., measurements of fluid uptake, mechanical strength, chemical composition), which are established by standard laboratory methods and equipment, not human experts for ground truth labeling.

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

Not applicable. There is no human reading or adjudication process described for the performance evaluation of this wound dressing.

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 is not an AI device.

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

Not applicable. This is not an AI device.

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

For the non-clinical tests mentioned (e.g., fluid uptake, mechanical testing, chemical composition), the "ground truth" is established through standardized laboratory testing procedures and measurements using calibrated equipment. For biocompatibility, established ISO standards and chemical characterization methods define the "truth" regarding material safety. The document does not describe the use of human expert consensus, pathology, or outcomes data to establish ground truth for the device's characteristics.

8. The sample size for the training set

Not applicable. This is not an AI device, so there is no "training set."

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

Not applicable. This is not an AI device, so there is no "training set" or corresponding ground truth establishment process.

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