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The Pleats Plus ™ 1054 and 1054S N95 Respirators and Surgical Masks 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.

These masks are pleated, 3-ply masks, with a center layer of polypropylene meltblown material sandwiched by inner and outer layers of nonwoven material. The mask has 2 braided synthetic elastic headbands and a flexible wire tie nosepiece that allows the respirator to form to the bridge of the wearers nose. No fiberglass media is used in this product.

The Abbreviated 510(K) for Aearo Company Pleats Plus™ 1054 and 1054S Surgical N95 Respirators (K063013) describes the acceptance criteria and the studies performed to demonstrate that the device meets these criteria.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Sizes Used for Each Test and Data Provenance

The data provenance is from laboratory testing (e.g., Nelson Laboratories, AppTec Laboratory Services, NIOSH certification). These are prospective tests performed specifically for device evaluation. The country of origin for the manufacturing facility is Handan Hengyong Protective & Clean Products Co., LTD. Jiankang East Street Yongnian County High-Tech Development Area Hebei province, P.R. China, however, the testing laboratories are US-based (Nelson Labs and AppTec).

3. Number of Experts and Qualifications for Establishing Ground Truth

This document describes the testing of a physical medical device (N95 respirator) against established performance standards and methods. It does not involve a "ground truth" expert consensus in the same way a diagnostic imaging AI algorithm would. Instead, the "ground truth" is defined by:

• Standardized test methods: ASTM 1862-00a, 21 CFR part 58, NIOSH 42CFR Part 84, ISO-10993-1, ASTM F2101-01.
• Regulatory bodies and accredited laboratories: NIOSH approval TC-84A-4320, Nelson Laboratories, AppTec Laboratory Services.

Therefore, the concept of "number of experts used to establish ground truth" with specific qualifications (like radiologists) is not applicable here. The "experts" are the scientists and technicians in the accredited labs performing the standardized tests according to the protocols set by regulatory bodies.

4. Adjudication Method for the Test Set

The concept of an adjudication method (like 2+1, 3+1) is typically relevant for studies where human interpretation or consensus is required (e.g., medical image reading). For the physical testing of the N95 respirator, the results are quantitative and objective measurements against predefined acceptance criteria. Therefore, no adjudication method as described is applicable or performed in this context. The results are reported directly from the laboratory tests.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. This type of study is relevant for evaluating the impact of AI algorithms on human reader performance, typically in diagnostic tasks. This submission is for a physical device (N95 respirator), not an AI-powered diagnostic tool.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

No, this question is not applicable as the device is a physical N95 respirator, not an algorithm or software. Therefore, there is no "standalone algorithm" performance to report.

7. Type of Ground Truth Used

For this device, the "ground truth" is based on:

• Standardized physical and chemical test results: Objective measurements demonstrating performance against established industry standards and regulatory requirements (e.g., ASTM, NIOSH, ISO, CFR).
• Predicate device comparison: The device's performance is also compared against a legally marketed predicate device (Pleats Plus 1050), establishing substantial equivalence based on meeting similar performance characteristics.

8. Sample Size for the Training Set

This question is not applicable as the device is a physical N95 respirator, not a machine learning algorithm that requires a training set.

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

This question is not applicable as the device is a physical N95 respirator and does not use a training set in the context of machine learning.

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