Search Results

Medical Surgical Masks are intended to be worn to protect both the patient and healthcare personnel from transfer of microorganisms, body fluids and particulate material. These face masks are 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.

Medical Surgical Masks are disposable medical masks and are made of mask body, nose clips and mask belt by ultrasonic heat sealing. The mask body is divided into three layers: inner, middle and outer layers, the inner and outer layers are Non-woven fabrics and middle layer is polypropylene melt blown cloth, nose piece is made of galvanized iron wire covered plastic materials. The mask belt is made of spandex. The mask style is flat pleated.

Here's an analysis of the acceptance criteria and study proving the device meets them, based on the provided FDA 510(k) summary for the "Medical Surgical Mask":

The document does not describe a clinical study in the traditional sense for evaluating the device's performance with human patients or AI assistance. Instead, it describes non-clinical performance testing (bench testing) to demonstrate that the device meets established safety and performance standards for surgical masks.

Acceptance Criteria and Reported Device Performance

The study described is a series of non-clinical, bench-top tests conducted to demonstrate that the Medical Surgical Mask meets industry standards for safety and performance.

Additional Information Not Applicable to This Device

The questions related to AI studies, multi-reader multi-case studies, and specific human expert qualifications for ground truth are not applicable to this 510(k) submission for a Medical Surgical Mask, as it is a medical device subject to bench testing for physical and biological characteristics, not an AI-powered diagnostic or assistive tool.

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

   • Sample Size: The document specifies "32 out of 32 pass" for fluid resistance, indicating a sample size of 32 masks for that particular test. For other tests like flammability, delta pressure, bacterial filtration, particulate filtration, and biocompatibility, specific sample sizes are not explicitly stated in this summary, but are typically defined by the referenced ASTM and ISO standards for such tests.
   • Data Provenance: The origin of the data is from non-clinical laboratory testing (bench testing) performed on the "subject device." The location of the testing facility is not specified, but the applicant company is located in Hangzhou, China. These are laboratory results, not patient data.

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

   • Not Applicable. For a medical surgical mask, "ground truth" is established by adherence to recognized international and national standards (e.g., ASTM, ISO, CFR) for performance and safety characteristics. These standards have defined methodologies and acceptance criteria. Experts are involved in setting these standards and performing the tests, but it's not and "expert consensus" in the way it would be for an AI diagnostic.

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

   • Not Applicable. Adjudication methods like 2+1 are used for human interpretation of medical images or data where a consensus is needed among experts. For the physical and biological performance tests of a surgical mask, the results are objectively measured according to standardized protocols, thus no expert adjudication in this sense is required.

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:

   • No. This is not an AI-assisted device.

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

   • No. This is not an AI-powered device.

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

   • The "ground truth" for this device is based on pre-defined, objective performance metrics and safety thresholds established by regulatory bodies and consensus standards (ASTM F2100, EN 14683, 16 CFR 1610, ISO 10993). It is not derived from clinical outcomes, pathology, or expert consensus on a diagnostic interpretation.

7. The sample size for the training set:

   • Not Applicable. This device is not an AI/ML algorithm that requires training data.

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

   • Not Applicable. This device is not an AI/ML algorithm.

Ask a specific question about this device