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The Disposable Face Mask is intended to be worn to protect both the patient and healthcare personnel from transfer of microorganisms, body fluids, and particulate material. These face masks are intended for use in infection control practices to reduce the potential exposure to blood and body fluids. This is a single-use, disposable device, provided non-sterile.

The proposed device, Disposable Face Mask is a three-layer, single-use, flat-pleated mask. The ear loops to secure the mask over the users' mouth and face and includes a nosepiece to provide a firm fit over the nose. The disposable surgical mask is available in four different specifications: 14.5cmx9.5cm and 17.5cmx9.5cm. The device is single use and provided non-sterile.

The provided text describes the 510(k) submission for a Disposable Face Mask (K202905) and its equivalence to a predicate device (K160269). However, it does not contain information related to an AI/ML-enabled medical device or a clinical study involving human readers and AI assistance.

Therefore, I cannot provide the requested information regarding acceptance criteria, study details, sample sizes, ground truth establishment, expert qualifications, adjudication methods, or MRMC studies for an AI device. The document is strictly about the non-clinical testing and comparison of a traditional medical device (face mask) to a predicate device to demonstrate substantial equivalence.

The document covers:

• Device: Disposable Face Mask
• Purpose: To protect against transfer of microorganisms, body fluids, and particulate material.
• Regulatory Class: Class II, Product Code FXX.
• Acceptance Criteria/Performance (based on standards for face masks):
  • Fluid Resistance (ASTM F1862)
  • Particulate Filtration Efficiency (ASTM F2299)
  • Bacterial Filtration Efficiency (ASTM F2101)
  • Differential Pressure (EN 14683)
  • Flammability (16 CFR 1610)
  • Biocompatibility (ISO 10993-5 and ISO 10993-10)

Here's a breakdown of what can be extracted from the document, tailored as much as possible to your request, but highlighting the absence of AI/ML specifics:

1. Table of Acceptance Criteria and Reported Device Performance

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Antibacterial bandage is to be applied topically to the skin for management of minor scrapes and minor burns.
Antibacterial bandage is to be applied topically to the skin for management of minor cuts, minor scrapes and minor burns.

The device, Antibacterial bandage, is to be applied topically to the skin for management of minor cuts, minor scrapes and minor burns. The proposed devices are available in fabric backing and plastic backing. All of devices consist of (1) backing (fabric or plastic), (2) absorbent pad (composed of Polyester fiber, viscose fiber, PE mesh, Benzalkonium chloride) and (3) release paper (composed of cellulose pulp, water, kaolin, starch, ethanol and ethylene copolymer). Each device type is available in several models. The difference between each model is the device size. This dressing contains 0.08% benzalkonium chloride which has shown effectiveness against (Staphylococcus Aureus, Pseudomonas Aeruginosa, Escherlchia coli, Enterococcus Faecalis, Klebsiella Pneumoniae, Streptococcus Pyogenes, Candida Albicans and Aspergillus Niger) for up to 24 hours, as demonstrated via in vitro testing.

The provided text describes the 510(k) summary for an "Antibacterial Bandage" (K201324) and compares it to a predicate device (K113583). It outlines non-clinical tests performed to demonstrate substantial equivalence, but it does not include a multi-reader multi-case (MRMC) comparative effectiveness study, standalone algorithm performance, or details on ground truth for AI model training/testing, as it is not an AI/ML device.

Here's an analysis of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

8. The sample size for the training set
Not applicable. This is not an AI/ML device, so there is no training set for an algorithm.

9. How the ground truth for the training set was established
Not applicable. There is no training set for an AI/ML algorithm.

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