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Surgical Mask is intended to be worn to protect both the patient and healthcare personnel from transfer of microorganisms, body fluids and particulate material. It is 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.

The Durio Surgical Face Mask is a non-sterile, single use, flat-pleated style surgical mask with ear loops and nose piece. The Durio Surgical Face Mask is available in three or four ply models. In all cases, the inner and outer layers of the Durio Surgical Face Mask are made of non-woven spunbond polypropylene (PP). The outer layer repels fluid and droplets. The middle layer is made of non-woven melt blown PP fabric for filtration by mechanical blocking and electrostatic attraction. For the four-ply model, there is an additional middle layer made of non-woven spunbound PP. The ear loops are made of polyester covered spandex yarn. The elastic ear loops pull inward from welding on the outer layer to reduce gaps as the side between the face and mask. The nose strip is made of zinc electro galvanized steel wire allowing adjustability and to let it stand fixed for better fit. The Durio Surgical Face Mask is provided in blue. The device is not made from any natural rubber latex.

The provided document is a 510(k) Premarket Notification for a medical device (Durio Surgical Face Mask). It is a regulatory submission to the FDA demonstrating substantial equivalence to a legally marketed predicate device.

Crucially, this document is NOT about an AI/ML medical device where the "device performance" refers to the output of an algorithm against a ground truth. Instead, the "device performance" here refers to the performance of the physical surgical face mask according to established ASTM (American Society for Testing and Materials) standards.

Therefore, many of the requested elements of your prompt (e.g., sample size for test/training sets, data provenance, number/qualifications of experts, adjudication methods, MRMC studies, standalone algorithm performance, types of ground truth for AI, training set ground truth establishment) do not apply to this document as it does not describe an AI medical device.

However, I can extract the relevant information regarding the acceptance criteria and the study that proves the device meets those criteria for this physical medical device.

Here's the breakdown:

Acceptance Criteria and Reported Device Performance (Physical Surgical Face Mask)

The acceptance criteria for the Durio Surgical Face Mask are defined by specific ASTM standards and a CFR regulation, demonstrating its barrier performance and safety. The "study" proving these criteria involved various bench performance tests as opposed to a study on an AI/ML algorithm.

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

Note on ASTM F2100 Levels: The document states that the 3-ply model meets Level 2, and the 4-ply model meets Level 3 of ASTM F2100. This standard defines performance requirements for medical face masks by classifying masks at Level 1, 2, or 3 based on their barrier properties (BFE, PFE, fluid resistance, differential pressure). By stating "Level 2 (3-Ply) and Level 3 (4-Ply)", it implicitly means the masks met the criteria for these specific levels for BFE, PFE, and synthetic blood penetration.

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

• Sample Size: The document does not specify the exact sample size for each bench performance test. It broadly states that "Durio Surgical Face Masks passed the testing in accordance with internal requirements and standards." Regulatory submissions for physical devices typically rely on accredited lab testing according to the relevant standard. These standards often dictate minimum sample sizes for testing (e.g., a certain number of masks for BFE or PFE testing).
• Data Provenance: The tests were conducted by an "applicant's internal" process ("Durio completed various bench performance tests... passed the testing in accordance with internal requirements and standards") likely in Malaysia (given the sponsor's location in Johor Bahru, Malaysia). The nature of these tests for a physical device is inherently "prospective" as new masks are manufactured and tested.

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

• Not Applicable. This is a physical non-AI device. "Ground truth" for these performance tests is established by the specified ASTM and ISO standards' methodologies, not by human expert consensus or labeling.

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

• Not Applicable. This relates to human expert review and consensus for data labeling, which is not relevant for a physical device's bench testing.

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:

• Not Applicable. This is specific to AI/ML device evaluations.

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

• Not Applicable. This is specific to AI/ML device evaluations.

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

• Defined by Standardized Test Methods: The "ground truth" for the device's performance is determined by adherence to standardized, quantitative testing methods defined in documents like ASTM F2101, ASTM F2100, ASTM F1862/F1862M, 16 CFR 1610, and ISO 10993. For example:
  • BFE: Percentage of bacteria filtered.
  • PFE: Percentage of sub-micron particles filtered.
  • Synthetic Blood Penetration: Pass/fail based on blood penetration.
  • Biocompatibility: Absence of toxic, irritating, or sensitizing responses in preclinical models.

8. The sample size for the training set:

• Not Applicable. This is a physical device, not an AI/ML model that requires a training set.

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

• Not Applicable. This is a physical device, not an AI/ML model.

In summary, the document demonstrates the substantial equivalence of the Durio Surgical Face Mask to a predicate device based on standardized, non-clinical bench testing for its physical and barrier properties, addressing safety and effectiveness without involving AI/ML components or associated data science methodologies.

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