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Innovative Technologies' Hydrocolloid with Zinc Wound Dressings may be used for the management of wounds including:

• Partial full thickness wounds, ie. arterial, venous, diabetic ulcers
• Post-operative surgical wounds
• Superficial burns (1st & 2nd degree)
• Donor Sites
• Trauma Wounds
• Dermal Lesions
• Protective dressing

Innovative Technologies' Hydrocolloid with Zinc Wound Dressings is a combination of two products with existing 510(k)s: 510(k) #K951499, Dermagran-B™ Hydrophilic Wound Dressing manufactured by Derma Sciences, and 510(k) #K971126, Innovative Technologies' Hydrocolloid & Intelligent Hydrocolloid Wound Dressing. Innovative Technologies' Hydrocolloid with Zinc Wound Dressings are highly conformable, sterile, primary wound dressings intended to provide an environment ideally suited for the management of moderate to heavily exuding partial to full thickness wounds. The low profile of the dressing allows for ease in the use of pressure garments and normal clothing. The hydrocolloid dressing may be left in place until the exuding phase is complete. The wound may then be covered with a breathable polyurethane film such as Innovative Technologies' Film Wound Dressing 510(k) #K9204008 which protects the new tissue from mechanical trauma and maceration. Dressings are supplied sterile in single use blisters / pouches. Product is gamma irradiated in accordance with EN 552, JEN556, and ANSI / AAMI / ISO11137-1994, Sterilisation Of Health Care Products - Requirements For Validation and Routine Control - Radiation Sterilisation, 3rd Edition, Method 1 for dosimetric release with a sterility assurance level of 10^-6.

The provided text is a 510(k) premarket notification for a medical device, specifically Innovative Technologies' Hydrocolloid with Zinc Wound Dressing. This document is a regulatory submission for market clearance and does not describe a study that establishes acceptance criteria and proves the device meets them in the way a clinical or performance study would for an AI/ML diagnostic device.

Instead, this document focuses on demonstrating substantial equivalence to existing legally marketed predicate devices, which is the regulatory pathway for this type of medical device. The "acceptance criteria" here are essentially the characteristics and performance of the predicate devices. The "study" is the comparison made between the new device and the predicate devices.

Let's break down why this information is not present in the format requested, and then extract what is available:

Why the requested information (for AI/ML diagnostic devices) is not directly applicable:

• Acceptance Criteria & Device Performance Table (for AI/ML): This typically involves performance metrics like sensitivity, specificity, AUC, etc., measured against a gold standard for a diagnostic task. For a wound dressing, performance is assessed differently (e.g., biocompatibility, absorbency, sterility, wound healing environment).
• Sample Size, Ground Truth, Experts, Adjudication, MRMC, Standalone (for AI/ML): These are concepts specifically related to the evaluation of diagnostic algorithms. They do not apply to the evaluation of a physical wound dressing in the same manner.
• Training Set (for AI/ML): This is irrelevant as there's no algorithm being trained.

Information Extracted from the provided document, reframed as closely as possible to the request's intent for a non-AI/ML device:

1. Table of Acceptance Criteria (Predicate Characteristics) and Reported Device Performance (New Device Characteristics):

The document uses a comparative table to show that the new device's characteristics are similar to the predicate devices. The "acceptance criteria" are implied to be the characteristics of the predicate devices, and the "reported device performance" are the characteristics of the Innovative Technologies Hydrocolloid with Zinc.

2. Sample size used for the test set and the data provenance:
This type of information is not applicable to a 510(k) submission for a wound dressing, which relies on demonstrating substantial equivalence to predicate devices rather than a performance study on a specific test set of cases. The "data provenance" for the predicate devices would be their own regulatory submissions and market history.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
Not applicable. Ground truth as understood in AI/ML is not a concept for wound dressings in this context. Substantial equivalence relies on comparing product design, materials, and intended use as assessed by regulatory bodies.

4. Adjudication method used for the test set:
Not applicable.

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 algorithm.

7. The type of ground truth used:
For the purpose of substantial equivalence, the "ground truth" is effectively the established safety and effectiveness of the legally marketed predicate devices. The document states:

• "Previous biocompatibility testing and use in the market place have shown safety and effectiveness of the two products [predicate devices]."
• The device is found "substantially equivalent... to devices marketed in interstate commerce prior to May 28, 1976..."

8. The sample size for the training set:
Not applicable. There is no AI algorithm or training set.

9. How the ground truth for the training set was established:
Not applicable.

In summary: The provided document is a regulatory submission for a physical medical device (wound dressing) seeking market clearance through substantial equivalence. It is not an AI/ML device, and thus the requested specific details regarding AI/ML study design and evaluation metrics are not present or applicable. The "study" for this device is the comparison of its characteristics and indications for use against established predicate devices.

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