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Surgical Gowns are intended to be worn by operating room personnel during surgical procedures to protect the surgical patient and operating room personnel from the transfer of microorganisms, body fluids and particulate material. This surgical gown meets the requrements of AAMI Level 3 barrier protection for a surgical gown per ANSI/A AMI PB70:2012 Liquid barrier performance and classification of protective apparel and drapes intended for use in health care facilities (AAMI PB70). The Surgical Gowns are single use, disposable medical devices, provided sterile.

The proposed device Surgical Gown is model 40083, its body, sleeve and belt are made of SSMMS non-woven material, and cuff is made of cotton. The proposed device is available in XS, S, M, L, XL, 2XL, 3XL sizes. This proposed device can meet the requirements for Level 3 per ANSI/AAMI PB70:2012. The proposed devices are disposable medical devices and provided in sterile and blue color.

This document is a 510(k) Premarket Notification from the FDA regarding a Surgical Gown. It evaluates the device's substantial equivalence to a legally marketed predicate device. The information provided is primarily focused on the physical and barrier performance characteristics of the surgical gown, not an AI/Software as a Medical Device (SaMD) product.

Therefore, most of the requested information regarding AI/SaMD performance criteria, such as "number of experts used to establish ground truth," "adjudication method," "MRMC study," "standalone algorithm performance," and "training set details," is not applicable to this submission.

However, I can extract the relevant information regarding the surgical gown's acceptance criteria and the studies proving it meets those criteria:

Device: Surgical Gown (Model 40083)
Intended Use: To be worn by operating room personnel during surgical procedures to protect surgical patients and operating room personnel from the transfer of microorganisms, body fluids, and particulate material. It meets AAMI Level 3 barrier protection.

Acceptance Criteria and Reported Device Performance:

Details Regarding the Study:

1. Sample sizes used for the test set and the data provenance:

   • Seam Strength, Breaking Strength, Tear Strength: "Average result from 30 samples" for each test.
   • Lint and other generation: "Average result from 6 samples."
   • Water Penetration Resistance, Static Hydrostatic Resistance, EO/ECH Residual, Cytotoxicity, Irritation, Sensitization, Flammability: The specific number of samples for these tests is not explicitly stated as "X samples" in the provided text, but the results are reported as "PASS," indicating that the required sample sizes for the respective standards were met.
   • Data Provenance: The document does not specify the country of origin for the testing data (e.g., in-house testing by the manufacturer's lab in China, or third-party labs). The submission is from Ammex-Weida (Hubei) Health and Safety Products Co., Ltd in China. All studies described are non-clinical, laboratory-based tests. They are not categorized as retrospective or prospective clinical studies.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not applicable. This is not an AI/SaMD product requiring expert-derived ground truth for diagnostic accuracy. The "ground truth" here is defined by the objective physical and chemical standards and their corresponding test methods (e.g., AATCC, ASTM, ISO).

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

   • Not applicable. This is not an AI/SaMD product. The results are from standardized laboratory tests.

4. 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:

   • Not applicable. This is not an AI/SaMD product.

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

   • Not applicable. This is not an AI/SaMD product.

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

   • The "ground truth" for this device's performance is established by internationally recognized industry standards and test methods (e.g., ANSI/AAMI PB70:2012 for barrier performance, ASTM for material strength, ISO for biocompatibility, etc.). These standards define the quantitative and qualitative acceptance criteria for the device's physical safety and performance.

7. The sample size for the training set:

   • Not applicable, as this is not an AI/SaMD product that requires a training set.

8. How the ground truth for the training set was established:

   • Not applicable, as this is not an AI/SaMD product that requires a training set.

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

The proposed device(s) are blue color, and flat pleated type mask, utilizing ear loops way for wearing, and they all has nose clips design for fitting the face mask around the nose. The proposed device(s) are manufactured with three layers, the inner and outer layers are made of spun-bond polypropylene, and the middle layer is made of melt blown polypropylene filter. The model of proposed device, ear loops, is held in place over the users' mouth and nose by two elastic ear loops welded to the face mask. The elastic ear loops are made of Nylon and polyester. The nose piece contained in the proposed device(s) is in the layers of face mask to allow the user to fit the face mask around their nose, which is made of polyethylene coated galvanized wire. The proposed device(s) are sold non-sterile and are intended to be single use, disposable device.

This document pertains to the 510(k) premarket notification for a Medical Face Mask, and as such, it describes performance testing for a physical medical device, not software or an AI-powered diagnostic tool. Therefore, many of the requested criteria related to AI/algorithm performance, ground truth establishment, expert adjudication, and MRMC studies are not applicable.

However, I can extract the relevant information regarding the device's acceptance criteria and the studies performed to demonstrate it meets those criteria.

Acceptance Criteria and Device Performance for Ammex-Weida (Hubei) Health and Safety Products Co., Ltd's Medical Face Mask

The device under review is a Medical Face Mask, a physical device, not an AI/algorithm-based one. The acceptance criteria are based on established performance standards for medical face masks.

1. Table of Acceptance Criteria and Reported Device Performance

Device: Medical Face Mask (Proposed Device, K211062)
Classification: Class II, ASTM F2100 Level 3 (meets Level 3 requirements)

Biocompatibility Testing (ISO 10993)

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

• Sample Sizes: The document specifies sample sizes for some tests:
  • Fluid Resistance (ASTM F1862): 32 samples were tested ("32 out of 32 Pass").
  • For other tests like Particulate Filtration Efficiency, Bacterial Filtration Efficiency, Differential Pressure, Flammability, and Biocompatibility, specific sample sizes are not explicitly stated in this summary, but these are standardized tests that follow defined sample size protocols for regulatory compliance.
• Data Provenance: The tests were non-clinical and conducted to verify design specifications and similarity to the predicate device. The tests were performed according to specified standards (e.g., ASTM, EN, ISO, CFR). The country of origin for the manufacturing company is China (Hubei, China), and the submission correspondent is based in Shanghai, China. The document does not specify whether the data collection for these performance tests was retrospective or prospective, though performance testing for device clearance is typically prospective by nature.

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Not Applicable. This is a physical device, not an AI/algorithm requiring expert-established ground truth for diagnostic accuracy. Performance is measured against physical and biological properties.

4. Adjudication Method for the Test Set

• Not Applicable. As this is a physical device, there is no "ground truth" established by human experts that requires adjudication in the context of diagnostic accuracy. The testing involves standardized laboratory methods with objective measurements.

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

• Not Applicable. This type of study is relevant for diagnostic imaging or AI algorithms where human interpretation is involved. This device is a medical face mask, and its effectiveness is determined by objective physical and biological barrier tests, not human interpretation.

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

• Not Applicable. This is a physical device, not an algorithm.

7. Type of Ground Truth Used

• Objective Measurement against Standards: The "ground truth" for this device's performance is established by objective laboratory measurements against predefined international and national standards (ASTM, EN, ISO, CFR). For example:
  • Fluid Resistance: Measured by the device's ability to resist penetration by synthetic blood at a certain pressure.
  • Filtration Efficiency: Measured by the percentage of particles or bacteria filtered out.
  • Differential Pressure: Measured by the breathability of the material.
  • Flammability: Measured by how easily the material ignites and burns.
  • Biocompatibility: Measured by the material's non-toxicity, non-irritating, and non-sensitizing properties in biological tests.

8. Sample Size for the Training Set

• Not Applicable. This device does not involve a "training set" in the context of machine learning. The manufacturing process and material selection are based on established engineering and materials science principles and validated through the non-clinical tests described.

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

• Not Applicable. As there is no training set for an AI/algorithm, there is no corresponding "ground truth establishment."

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