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Disposable Surgical Gown (Level 3, MF2103 Series)/ Disposable Surgical Gown (Level 3, MF2104 Series)/ Disposable Surgical Gown (Level 4, MF2105 Series) is intended to be worn by operating room personnel during surgical procedures to protect both the surgical patient and the operating room personnel from transfer of microorganisms, body fluids, and particulate material. It is single use, disposable and supplied sterile.

Per ANSI/AAMI PB70:2012 Liquid barrier performance and classification of protective apparel and drapes intended for use in health care facilities, MF2103 Series and MF2104 Series met the requirements for Level 3 classification, MF2105 Series met the requirements of Level 4 classification.

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This document is a 510(k) premarket notification from the FDA, specifically concerning the substantial equivalence determination for Disposable Surgical Gowns. It clarifies that the device is found substantially equivalent to legally marketed predicate devices.

However, the provided text does not contain any information about an AI/ML-based medical device, its acceptance criteria, or the study that proves it meets those criteria. The device in question is a physical product (surgical gowns), and the assessment is based on physical barrier performance standards (ANSI/AAMI PB70:2012), not on AI/ML algorithm performance.

Therefore, I cannot fulfill the request to describe the acceptance criteria and the study that proves the device meets the acceptance criteria, including details like:

1. A table of acceptance criteria and the reported device performance: Not applicable as the "performance" here refers to physical barrier properties, not algorithmic output.
2. Sample sizes used for the test set and the data provenance: Not applicable. Testing for surgical gowns involves physical property tests (e.g., liquid barrier, particulate material transfer), not data sets.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for a physical product like a surgical gown is established by standardized physical testing methods, not expert consensus on data.
4. Adjudication method: Not applicable.
5. If a multi reader multi case (MRMC) comparative effectiveness study was done: Not applicable. This is relevant for AI-assisted image interpretation, not physical products.
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: Not applicable in the context of AI/ML. The "ground truth" for the gowns is their adherence to ANSI/AAMI PB70:2012 standards.
8. The sample size for the training set: Not applicable.
9. How the ground truth for the training set was established: Not applicable.

The document focuses on the regulatory clearance for a medical device (surgical gowns) based on their physical barrier performance against industry standards, not on an AI/ML algorithm's diagnostic or analytical performance.

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The 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 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.

The proposed devices are single use, three-layer, flat masks with straps and nose piece. The Surgical Masks 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 proposed device is held in place over the user's mouth and nose by two elastic ear loops or four ties welded to the face mask. The elastic ear loops are not made with natural rubber latex. The nose piece contained in the proposed device is in the layers of face mask to allow the user to fit the face mask around their nose, which is made of Malleable aluminum wire. The proposed devices are sold non-sterile and are intended to be single use, disposable devices.

The document provided is a 510(k) summary for a Disposable Surgical Mask, outlining its substantial equivalence to a legally marketed predicate device. As such, the information contained focuses on the device's physical properties, material composition, and performance against established safety and efficacy standards for surgical masks.

This type of submission and the associated testing do not involve AI/ML components, human reader studies, or complex ground truth establishment methods typical for AI/ML-driven medical devices. The tests described are laboratory-based evaluations of the mask's material properties and fluid/particulate blocking capabilities.

Therefore, many of the specific questions posed in your request (e.g., sample size for test set, number of experts for ground truth, adjudication method, MRMC studies, standalone performance, training set details) are not applicable to this disposable surgical mask submission.

Here's the relevant information based on the provided text:

Acceptance Criteria and Device Performance

The acceptance criteria are established based on recognized industrial standards for performance of materials used in medical face masks.

Table of Acceptance Criteria and Reported Device Performance

Study Details

1. Sample size used for the test set and the data provenance:
   The document does not specify the exact sample sizes (number of masks or material samples) used for each test. The tests are laboratory-based, performed on material samples of the mask. Data provenance is implied to be from the manufacturer's internal testing. There is no indication of patient data or clinical data; the study is entirely non-clinical. The origin of the testing would be where the manufacturer is located, which is Dongguan, China. The studies are retrospective laboratory tests on manufactured samples.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
   Not applicable. Ground truth for these performance tests is established by adhering to widely accepted international standards (e.g., ASTM, EN, ISO) and their specified testing methodologies and acceptance criteria. There are no "experts" in the human perception or diagnostic sense involved in establishing ground truth for these physical properties.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
   Not applicable. This concept pertains to expert review of data (e.g., medical images). The "ground truth" for these tests is the quantitative measurement of physical properties against a standard.

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 a simple medical device (disposable surgical mask), not an AI-assisted diagnostic tool. No human reader studies were performed.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
   Not applicable. There is no algorithm or AI component in this device. The "standalone performance" refers to the mask's inherent physical characteristics as tested in the laboratory.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
   The ground truth is defined by the acceptance criteria established by standardized, validated test methods (e.g., ASTM F1862 for fluid resistance, ASTM F2299 for particulate filtration efficiency), which are internationally recognized and specified in the FDA's guidance document for surgical masks. These are objective, measurable physical properties, not subjective interpretations or clinical outcomes.

7. The sample size for the training set:
   Not applicable. This device does not involve machine learning; therefore, there is no "training set."

8. How the ground truth for the training set was established:
   Not applicable, as there is no training set.

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