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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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