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The Disposable 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, provided non-sterile and sterile.

The proposed device, Disposable Surgical Mask is a three-layer, flat-folded mask with ear strap and nose clip. The ear straps are held in place over the users' mouth and nose by two elastic ear straps welded to the facemask. The color of the surgical mask is blue. The disposable surgical mask is available in three different specifications: 17.59.5cm, 14.59.5cm and 14.5*8cm. The device is single use and provided non-sterile and sterile.

The provided document is a 510(k) Pre-market Notification for a Disposable Surgical Mask, primarily focusing on proving substantial equivalence to a predicate device based on non-clinical performance standards. It explicitly states, "No clinical study is included in this submission." Therefore, it does not contain the information requested regarding acceptance criteria and a study proving a device meets acceptance criteria using clinical or AI-driven performance metrics.

The document outlines acceptance criteria for non-clinical tests (e.g., fluid resistance, particulate efficiency, bacterial filtration, differential pressure, flammability, biocompatibility) to demonstrate the proposed device performs as well as or better than the predicate surgical mask. However, these are physical and biological performance metrics for the mask itself, not measures of a complex device's performance in a clinical setting or where AI is involved.

Here's a breakdown of why I cannot fulfill your request based on the provided text, and what information is available related to performance:

Information NOT available in the provided document:

1. A table of acceptance criteria and the reported device performance related to a clinical or AI-driven study: The document presents a comparison table (page 5) of the proposed and predicate device's specifications and physical performance parameters, not clinical performance against acceptance criteria for a device involving AI or complex decision-making.
   • For example, it states "Fluid Resistance: Pass at 160 mmHg" as a characteristic, which is a physical test result, not a clinical outcome or AI performance metric.
2. Sample size used for the test set and data provenance: Not applicable as no clinical or AI test set is mentioned.
3. Number of experts used to establish ground truth and qualifications: Not applicable as no ground truth for a clinical or AI study is established.
4. Adjudication method for the test set: Not applicable.
5. If a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done: "No clinical study is included in this submission." (page 7)
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable.
8. The sample size for the training set: Not applicable, as no AI/machine learning component is described.
9. How the ground truth for the training set was established: Not applicable.

Information related to "acceptance criteria" and "performance" that IS in the document, but for physical/biological mask properties:

The document lists performance measures for the physical characteristics of the surgical mask:

1. A table of acceptance criteria and the reported device performance (for physical/biological properties):

2. Sample sizes for these physical/biological tests: Not explicitly stated in the document. The standards (e.g., ASTM, ISO) typically define the sample sizes for these types of tests.

3. Data provenance for these non-clinical tests: The tests were "conducted to verify that the proposed device met all design specifications" (page 6). This implies they were prospective tests performed on samples of the manufactured surgical masks. The country of origin for the manufacturer is China.

4. Experts involved in establishing ground truth for these non-clinical tests: Not applicable in the context of expert review of data/images. These are typically lab-based, standardized tests performed by qualified technicians following established protocols.

5. Adjudication method for these non-clinical tests: Not applicable.

6. Effect size of human readers improving with AI vs. without AI assistance: Not applicable as this is a surgical mask submission, not an AI device.

7. Standalone performance (algorithm only): Not applicable.

8. Type of ground truth for these non-clinical tests: For these physical and biological tests, the "ground truth" is defined by the objective results of the standardized test methods outlined (e.g., ASTM F1862/F1862M-17 for fluid resistance, ASTM F2101-2017 for BFE). These are laboratory measurements against accepted thresholds.

9. Sample size for the training set (for these non-clinical tests): Not applicable, as there's no machine learning training set involved.

10. How ground truth for training set was established (for these non-clinical tests): Not applicable.

In summary, the provided document is for a medical device (surgical mask) that is cleared based on its equivalence in physical and biological performance to a predicate device, as demonstrated through established non-clinical laboratory tests. It does not describe an AI-driven device or a clinical study that would involve the kind of acceptance criteria, test sets, and expert involvement you've asked about for an AI/clinical diagnostic device.

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