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The Handan N95 Particulate Respirators and Surgical Masks HY8510, HY9810 are intended for single use by operating room personnel and other health care workers to protect both the patients and the health care workers from transfer of microorganisms, blood and body fluids, and airborne particulate materials. This includes use as a procedure mask, isolation mask or dental face mask. This device also meets CDC Guidelines for TB Exposure Control.

Handan HY8510 N95 particulate respirators and surgical mask is constructed from a polypropylene spunbond used in the inner and outer cover. The polypropylene melt blown filter media and polyester filter fabric are layered between the inner and outer cover. The head strap is made of polyester elastic(for single head strap) which is circled to the mask. The inside nosepiece is a PU foam.

Handan HY9810 N95 particulate respirators and surgical mask is constructed from a polypropylene spunbond used in the outer cover, polyester filter fabric used in the inner cover. The polypropylene melt blown filter media is layered between the inner and out cover. The head straps are made of polyester elastic(for double headband) which is welded to the mask. The inside nosepiece is a PU foam.

Both items are no - sterilized and only for single use.

Acceptance Criteria and Study for Handan N95 Particulate Respirators and Surgical Masks (HY8510 & HY9810)

The provided document describes the Handan N95 Particulate Respirators and Surgical Masks (HY8510 & HY9810) and their substantial equivalence to predicate devices, rather than a standalone AI/algorithm-based device requiring detailed performance metrics typical of AI/ML studies. Therefore, the information provided below is adapted to reflect the nature of the submitted 510(k) summary for a medical device that relies on established material and physical property testing.

The acceptance criteria for the Handan N95 Particulate Respirators and Surgical Masks are based on established NIOSH (National Institute for Occupational Safety and Health) standards for N95 respirators and flammability standards. The study demonstrates that the device meets these criteria through non-clinical bench testing.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document explicitly states, "It is our conclusion that performance testing meet all relevant requirements of the aforementioned test standard."

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

The document does not specify the exact sample sizes used for each individual test (Exhalation Resistance, Inhalation Resistance, NaCl, Flammability, Biocompatibility). These are non-clinical bench tests performed on physical samples of the manufactured respirators. The data provenance is from the manufacturer's internal testing or contracted laboratories, performed for the purpose of demonstrating compliance with the specified standards. The nature of these tests is prospective for the device being submitted.

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

This section is not applicable in the context of this device and study. The ground truth for these tests is established by objective, standardized measurement protocols defined by NIOSH and other regulatory bodies (e.g., 16 CFR 1610, ISO 10993). The "experts" involved would be technicians and engineers qualified in performing these specific tests accurately according to the established methodologies, rather than clinical experts establishing a ground truth based on medical interpretations.

4. Adjudication Method for the Test Set

Adjudication methods like "2+1" or "3+1" are not applicable here. The tests involve objective measurements against predefined thresholds. The results are either compliant or non-compliant, based on the measured values falling within the acceptable ranges specified by the standards.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, an MRMC comparative effectiveness study was not done. This type of study is relevant for AI/ML devices that assist human readers in tasks like image interpretation. The Handan N95 respirators are physical devices whose performance is evaluated through material and physical property testing, not through human reader interpretation.

6. Standalone Performance Study

Yes, a standalone performance was done in the sense that the device's performance was evaluated independently against established standards through a series of bench tests. The algorithm, in this context, is the physical design and material composition of the respirator itself. The device (respirator) was tested on its own to demonstrate its intrinsic performance characteristics, without direct human-in-the-loop assistance for its primary function (filtration, resistance, etc.).

7. Type of Ground Truth Used

The ground truth used is based on objective, quantitative measurements against pre-defined regulatory and industry standards. For example:

• NIOSH standards: Specify acceptable levels of inhalation and exhalation resistance, and filtration efficiency for particulate matter (e.g., NaCl aerosol).
• 16 CFR 1610: Defines flammability performance classes based on burn time.
• ISO 10993: Defines biocompatibility requirements through various tests (e.g., cytotoxicity, irritation, sensitization).

8. Sample Size for the Training Set

This concept is not applicable to the evaluation of this device. The Handan N95 respirators are physical products, not AI/ML algorithms. Therefore, there is no "training set" in the context of machine learning. The design and manufacturing process of the respirators would be analogous to the development phase, but not a data-driven training process.

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

As noted above, the concept of a training set and its ground truth is not applicable for this type of medical device submission. The design and engineering of the respirators would be guided by the requirements of the standards (NIOSH, ISO, etc.), and the performance is then verified through the mandated non-clinical tests.

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The Delta N95 Respirator and Surgical Mask will be used for the health care practitioner from blood and body fluids and particulates and to meet the NIOSH / CIX' TB Prevention Guidelines.

DELTA N95 RESPIRATOR AND SURGICAL MASK is indicated for respiratory protective device. This disposable Surgicul mask is a one-piece, light weight half mask with filter material forming the mask. The component includes an inner serim hayer, netting material forming the mask. The component includes an inner scrim layer, netting layer, meltblown protection layer, outer scrim layer and clastic band. DELTA N95 RESPIRATOR AND SURGICAL MASK is NIOSH approved (Approval #TC-84A-0003) and has a filtration efficiency level of 95% and is effective against particulate acrosols free of oil. DELTA N95 RESPIRATOR AND SURGICAL MASK is available in four sizes, extra small, small, medium and large. The nose clip and elustic headband ensures effective face to mask seal.

The provided text describes a medical device (DELTA N95 RESPIRATOR AND SURGICAL MASK) and compares it to predicate devices. However, it does not contain information about acceptance criteria, a specific study proving the device meets these criteria, or any details related to AI/algorithm performance (standalone or with human readers).

The document is a 510(k) summary for a premarket submission of a physical medical device (an N95 respirator and surgical mask), focusing on demonstrating substantial equivalence to existing devices. Therefore, a significant portion of the requested information cannot be extracted from the provided text.

Based on the information available, here's what can be provided:

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

The acceptance criteria for this device are those defined by the Code of Federal Regulations, Title 42, Part 84 for N95-class of Respiratory Protective Devices, and meeting CDC guidelines for preventing transmission of Mycobacterium tuberculosis.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

This information is not provided in the document. The submission focuses on product specifications and comparison to predicates, not specific test data results for a particular sample size.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not provided in the document. This type of detail is not typically included in a 510(k) summary for a physical respiratory protection device unless specific clinical studies requiring expert evaluation were conducted and reported.

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

This information is not provided in the document.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done as this is a physical medical device (respirator/mask), not an AI/software device designed to assist human readers. Thus, there is no discussion of human readers or AI assistance.

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

No, a standalone algorithm performance study was not done as this is a physical medical device (respirator/mask), not an AI/software device.

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

For this type of device, the "ground truth" would be established by standardized laboratory testing methods to measure filtration efficiency, fit, and other performance characteristics as defined by NIOSH (42 CFR Part 84) and potentially fluid resistance (as it is also a surgical mask). It's not based on expert consensus, pathology, or outcomes data in the typical sense for diagnostic/screening devices.

8. The sample size for the training set

This information is not applicable and not provided. This device is a physical product, not a machine learning model that requires a "training set."

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

This information is not applicable and not provided. As explained above, it's not a machine learning model.

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