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Medical Protective Masks is intended to be worn to protect both the patient and healthcare persomel 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 mask materials include four layers, the inner and outer layers are made of spun-bond polypropylene, and the two middle layers are melt-blown polypropylene and non-woven polypropylene filters, respectively. The ear straps are held in place over the users' mouth and nose by two elastic ear straps welded to the facemask. The elastic ear straps are not made with natural rubber latex. The nose piece on the layers of facemask is to allow the user to fit the facemask around their nose, which is made of PE (polyethylene) with dual-Galvanized wire. The mask is a single use, disposable device, provided non-sterile in black color.

The provided document (K223686) is a 510(k) Premarket Notification for Medical Protective Masks. This document focuses on demonstrating substantial equivalence to a predicate device, primarily through non-clinical performance testing and material comparisons.

It is crucial to understand that this document describes the regulatory acceptance of a medical mask device, NOT an AI/ML-driven device. Therefore, many of the requested fields regarding AI/ML-specific details (like training/test sets, ground truth establishment for AI, expert adjudication, MRMC studies, and standalone AI performance) are not applicable to this submission. The "study" referenced in the prompt (as proving the device meets acceptance criteria) refers to the non-clinical performance testing of the physical mask, not a clinical or AI-based study.

Here's the information extracted from the document relevant to the performance of the Medical Protective Masks:

1. Table of Acceptance Criteria and Reported Device Performance (Non-Clinical)

Biocompatibility Testing:

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

• Sample Size: Not explicitly stated as a single "test set" size. For performance tests:
  • For Resistance to penetration by synthetic blood: "32 of 32 test articles" were used for each test (Test 1-3).
  • For other non-clinical tests (BFE, Differential Pressure, PFE, Flame Spread), the specific number of masks/samples tested is not explicitly detailed in the provided summary, but the results are reported as ranges or singular outcomes.
• Data Provenance: The document does not specify a country of origin for the non-clinical test data beyond the manufacturer being in China. The data would be from laboratory testing.
• Retrospective or Prospective: Not applicable in the context of mask performance testing. These are laboratory characterizations.

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

• Not applicable. The "ground truth" for physical mask performance is established by recognized international standards (e.g., ASTM F2100-19, ISO 10993 series). These are objective, quantitative measurements performed by accredited laboratories, not subject to expert consensus in the way an AI model's ground truth for medical images would be.

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

• Not applicable. As noted above, these are objective physical and chemical tests, not subjective interpretations requiring adjudication.

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:

• No. This is a medical mask, not an AI-assisted diagnostic device. Therefore, no MRMC study or AI assistance evaluation was performed.

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

• Not applicable. This is a physical medical mask, not an algorithm.

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

• The "ground truth" is based on objective, quantitative measurements defined by recognized international standards (e.g., ASTM F2100-19 for performance, ISO 10993 for biocompatibility). For example, Bacterial Filtration Efficiency is measured precisely by an instrument, not by an expert's opinion.

8. The sample size for the training set:

• Not applicable. This is a physical device, not an AI model requiring a training set.

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

• Not applicable, for the same reason as above.

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Medical Protective Masks are 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 mask materials include four layers, the inner and outer layers are made of spun-bond polypropylene, and the two middle layers are melt-blown polypropylene and non-woven polypropylene filters, respectively.

The ear straps are held in place over the users' mouth and nose by two elastic ear straps welded to the facemask. The elastic ear straps are not made with natural rubber latex. The nose piece on the layers of facemask is to allow the user to fit the facemask around their nose, which is made of PE (polyethylene) with dual-Galvanized wire. The mask is a single use, disposable device, provided non-sterile in white color.

The provided text is a 510(k) summary for Medical Protective Masks, a Class II medical device. It does not describe an AI medical device. Therefore, it is not possible to answer the questions about acceptance criteria and study proving device performance for an AI device.

The document focuses on non-clinical performance testing of the medical masks to demonstrate substantial equivalence to a predicate device, as required for 510(k) clearance by the FDA. The tests are for physical properties and biocompatibility of the mask, not for an AI algorithm's performance.

Here's an analysis of what is provided regarding acceptance criteria and performance, as it relates to the physical mask device, to clarify why it doesn't fit the AI-focused query:

1. Table of Acceptance Criteria and Reported Device Performance (for the physical mask):

Biocompatibility Testing:

Remaining questions from the prompt cannot be answered from the provided text as they pertain to AI/software device evaluation, not physical medical devices:

• Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
• Adjudication method (e.g. 2+1, 3+1, none) for the test set
• 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
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
• The type of ground truth used (expert consensus, pathology, outcomes data, etc)
• The sample size for the training set
• How the ground truth for the training set was established

The document explicitly states: "No clinical study implemented for the Medical Protective Masks." (Section 8.0). This further emphasizes that the evaluation framework is for a physical product's performance against recognized standards, not an AI algorithm.

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Disposable surgical mask (non sterile) 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 Disposable surgical mask (non sterile) is single use, three-layer, flat-pleated style with ear straps and nose piece. The mask is manufactured with three layers, the inner and outer layers are made of nonwoven fabrics, and the middle layer is made of melt blown fabrics. The ear straps are held in place over the users' mouth and nose by two elastic ear straps welded to the facemask. The elastic ear straps are not made with natural rubber latex. The nose piece on the layers of facemask is to allow the user to fit the facemask around their nose, which is made of PE (polyethylene) with dual-Galvanized wire. The Disposable surgical mask (non sterile) will be provided in blue. The masks are sold non-sterile and are intended to be single use, disposable devices.

The provided text describes a 510(k) premarket notification for a Disposable Surgical Mask (non-sterile), K211451, submitted by Qinhuangdao Taizhi Medical Technology Co., Ltd. The document primarily focuses on demonstrating substantial equivalence to a predicate device (K182515, Surgical Face Mask by Wuhan Dymex Healthcare Co., Ltd.) through non-clinical testing.

Based on the provided text, the device in question is a Disposable Surgical Mask, not an AI or imaging device. Therefore, many of the requested criteria (e.g., sample size for test set/training set, expert qualification, adjudication method, MRMC study, standalone performance, type of ground truth) typically associated with the development and validation of AI/ML-based medical devices or diagnostic tools are not applicable.

The acceptance criteria and study proving the device meets them are related to the physical and performance characteristics of a surgical mask, as per established standards and guidance for such products.

Here's the information extracted from the document, tailored to the nature of this device:

Acceptance Criteria and Device Performance for Disposable Surgical Mask (K211451)

The device demonstrates conformance to recognized standards and requirements for surgical face masks through non-clinical performance and biocompatibility testing. The testing was conducted to establish substantial equivalence to a predicate device (K182515).

1. Table of Acceptance Criteria and Reported Device Performance

The following table summarizes the performance testing results against established acceptance criteria for surgical masks (specifically, Level 2, ASTM F2100-19):

Biocompatibility Testing:

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

• Sample Size: The exact sample size for each specific non-clinical test (e.g., number of masks tested for BFE, PFE, etc.) is not explicitly stated, beyond "32 of 32 test articles" for synthetic blood penetration. However, the testing was conducted according to recognized standards (e.g., ASTM F2100-19), which would prescribe appropriate sample sizes for each test.
• Data Provenance: The origin of the data is from required non-clinical performance and biocompatibility testing performed on the manufactured device. The location of the test facility is not specified beyond the manufacturer's location in China. This is prospective testing conducted specifically for regulatory submission.

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

• This is not applicable for a surgical mask. "Ground truth" in this context refers to objective scientific measurements and conformance to standardized performance criteria, not expert consensus on diagnostic interpretations. The tests themselves are the "ground truth." These tests are performed by qualified laboratory personnel following established protocols.

4. Adjudication Method for the Test Set

• Not applicable. Performance metrics are objectively measured against pre-defined thresholds.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• Not applicable. This device is a surgical mask, not an AI or diagnostic tool requiring human reader studies.

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

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

7. The Type of Ground Truth Used

• The "ground truth" for this device is based on objective measurements obtained through standardized laboratory testing for physical and performance characteristics (e.g., filtration efficiency, differential pressure, fluid resistance, flame spread) and biocompatibility, as defined by recognized standards like ASTM F2100-19.

8. The Sample Size for the Training Set

• Not applicable. This device is not an AI/ML product developed using training datasets.

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

• Not applicable.

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