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

Disposable Surgical Mask, Ear Loop Type, Size M and S is a single use, three-layer, flat-pleated mask with ear loops and a nose piece. The Disposable Surgical Mask is manufactured with three layers, of which the inner and outer layers are made of spun-bond polypropylene, and the middle layer is made of melt blown polypropylene filter. The ear loops are held in place over the users' mouth and nose by two elastic ear loops welded to the face mask. The latex free elastic ear loops are made of polyamide fibers and spandex. The nose piece in the layers of face mask allows the user to fit the face mask around their nose, which is made of core iron wire with halogen-free polyethylene covering. The Disposable Surgical Mask will be provided in blue of outside and white of inside. The Disposable Surgical Mask is provided non-sterile and for single use.

The provided document is a 510(k) Premarket Notification for a Disposable Surgical Mask (K212944). It describes the device, its intended use, and a comparison with a predicate device, along with non-clinical test results.

However, the questions you've asked are typically relevant to the validation of Artificial Intelligence (AI)/Machine Learning (ML) medical devices, particularly those that involve image analysis, diagnosis, or risk prediction. This document is for a physical medical device (surgical mask) and the submitted information pertains to its physical performance characteristics, not its performance in an AI/ML context.

Therefore, many of your specific questions, such as those related to:

• Ground truth establishment by experts
• Adjudication methods
• Multi-reader multi-case (MRMC) studies
• Standalone algorithm performance
• Training set size and ground truth establishment

are not applicable to the context of this 510(k) submission for a surgical mask. The document does not involve AI/ML.

I can, however, extract the relevant information regarding the acceptance criteria and the study that proves the device meets those criteria for the surgical mask, based on the provided text.

Acceptance Criteria and Device Performance for Disposable Surgical Mask (K212944)

The device in question is a Disposable Surgical Mask, and its performance is evaluated against established physical and biological standards for medical masks.

Here's a summary tailored to the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Physical Performance Tests:
  • Fluid Resistance (ASTM F1862): 32 samples were tested to achieve the acceptance criteria of "29 out of 32 pass". The reported performance was "31 out of 32 pass".
  • For other tests (Particulate Filtration Efficiency, Bacterial Filtration Efficiency, Differential Pressure, Flammability Class), the specific number of samples tested is not explicitly stated, but the results are reported as single values (percentages, pressure, or class), implying multiple samples were likely tested to obtain representative values for product conformance.
• Data Provenance: Not explicitly stated regarding the origin of the testing data itself (e.g., country of origin of the samples). However, the manufacturer is in Heilongjiang, China. The tests were performed in accordance with ASTM and MIL standards, which are international standards. The study is a non-clinical test to demonstrate device performance against established standards. It is analogous to a prospective study in the sense that the device was manufactured and then tested to these specific criteria.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Those Experts

• Not Applicable. This is a physical medical device (surgical mask), not an AI/ML diagnostic or predictive device. "Ground truth" in this context refers to the measured physical characteristics (e.g., filtration efficiency, fluid resistance) as determined by standardized laboratory testing methods, not by expert interpretation of images or patient data. The expertise lies in performing and interpreting these specific laboratory tests according to the cited ASTM and MIL standards.

4. Adjudication Method for the Test Set

• Not Applicable. As per point 3, there is no expert adjudication for the objective physical and biological performance tests of a surgical mask. Results are based on quantitative measurements.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• No. This type of study is relevant for AI/ML devices where human readers (e.g., radiologists) use the AI to improve their performance. This 510(k) is for a physical surgical mask and does not involve AI assistance for human readers.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

• Not Applicable. There is no algorithm or AI component to this medical device.

7. The Type of Ground Truth Used

• The "ground truth" for this device is based on objective, standardized laboratory measurements of its physical properties (e.g., fluid resistance, filtration efficiency, differential pressure, flammability) and biocompatibility (cytotoxicity, irritation, sensitization). These are determined by adhering to specified test methods from organizations like ASTM and MIL.

8. The Sample Size for the Training Set

• Not Applicable. This is not an AI/ML device, so there is no "training set" in the machine learning sense. The device is manufactured based on design specifications and then tested to verify its performance.

9. How the Ground Truth for the Training Set Was Established

• Not Applicable. As per point 8, there is no training set.

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