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510(k) Data Aggregation
(302 days)
Surgical Mask (Procedure Mask) (Models: OH02-01 (Bandage)) 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.
The proposed Surgical Mask (Procedure Mask) body is a multi-layer construction of a Spun- bond polypropylene outer layer, meltblown middle layer and Spun-bond polypropylene inner layer. Nose clip is made of Aluminum plastic wire. There are two models of the proposed Surgical Mask (Procedure Mask), OH02-01 and OH02-01(Bandage). The main difference between each model is their wearing method, i.e. mask band part. For OH02-01 model, mask band part is made of non-woven fabric ear loops. For OH02-01(Bandage) model, mask band part is made of non-woven fabric ties. However, the raw materials of the two models devices are exactly the same. And have been tested according to ASTM F2100-19 Standard Specification For Performance Of Materials Used In Medical Face Masks. Surgical Mask (Procedure Mask) is a single use, disposable medical device that will be provided in non-sterile packaging configurations.
The provided document is a 510(k) Pre-market Notification for a Surgical Mask (Procedure Mask). It describes the device, its intended use, and the non-clinical tests performed to demonstrate its substantial equivalence to a predicate device.
Crucially, this document is for a physical medical device (a surgical mask), not an AI/software medical device. Therefore, the concepts of "acceptance criteria" and "study that proves the device meets acceptance criteria" in the context of an AI/software medical device (e.g., using a test set, ground truth established by experts, MRMC studies) do not apply here.
Instead, the "acceptance criteria" for a surgical mask relate to its physical performance characteristics and biocompatibility, as defined by recognized standards (e.g., ASTM F2100-19, ISO 10993). The "study" proving the device meets these criteria involves laboratory testing of physical samples.
Here's the information extracted from the document, re-interpreting the "acceptance criteria" for a physical medical device:
1. Acceptance Criteria and Reported Device Performance
The device is a surgical mask, and its performance is evaluated against the ASTM F2100-19 standard for materials used in medical face masks, which defines levels of protection based on specific physical tests. The reported device performance is based on fulfilling the requirements for "Level 3".
| Item (Performance Requirement) | Acceptance Criteria (ASTM F2100-19 Level 3) | Reported Device Performance |
|---|---|---|
| Fluid Resistance (ASTM F1862) | Fluid resistant claimed at 160 mm Hg | 32 out of 32 Pass at 160 mmHg (Average of 3 lots with 32 samples per lot) |
| Particulate Filtration Efficiency (PFE) (ASTM F2299) | ≥ 98% (for 0.1 µm polystyrene latex spheres) | Pass at 99.12% (Average of 3 lots with 32 samples per lot) |
| Bacterial Filtration Efficiency (BFE) (ASTM F2101) | ≥ 98% | Pass at 99.79% (Average of 3 lots with 32 samples per lot) |
| Differential Pressure (Delta P) (MILM-36954C) | < 6.0 mm H2O/cm² | 3.57 mmH2O/cm² (Average of 3 lots with 32 samples per lot) |
| Flammability (16 CFR 1610) | Class 1 | Class 1 (Average of 3 lots with 32 samples per lot) |
| Cytotoxicity (ISO 10993-5:2009) | Non-Cytotoxic | Non-Cytotoxic |
| Irritation (ISO 10993-10:2010) | Non-Irritating | Non-Irritating |
| Sensitization (ISO 10993-10:2010) | Non-Sensitizing | Non-Sensitizing |
2. Sample Size Used for the Test Set and Data Provenance
For the performance tests:
- Sample Size: "Average of 3 lots with 32 samples per lot" was used for each performance test (Fluid Resistance, PFE, BFE, Differential Pressure, Flammability). This refers to physical samples of the surgical masks.
- Data Provenance: The document implies these tests were conducted by the manufacturer, Ningbo Ouhan Medical Device Co., Ltd., which is based in Zhejiang, China. The document does not specify if the testing was retrospective or prospective in nature regarding data collection, but it is typically prospective testing of manufactured batches.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
This concept is not applicable to this type of medical device (surgical mask). "Ground truth" in this context is established by the standardized test methods themselves and the quantitative results obtained from laboratory equipment, not by human expert consensus or clinical interpretation.
4. Adjudication Method for the Test Set
This concept is not applicable. Test results for physical properties are objective measurements.
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
This concept is not applicable. This is a physical medical device, not an AI or software device that would assist human readers in interpretation.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done
This concept is not applicable. This is a physical medical device, not an algorithm.
7. The type of ground truth used
The "ground truth" for the performance of the surgical mask is defined by the quantitative results from standardized physical and biological laboratory tests, performed according to the specified ASTM and ISO standards. This is an objective, empirical ground truth based on measurable physical and chemical properties, rather than expert consensus, pathology reports, or outcomes data typically associated with AI/software medical devices.
8. The Sample Size for the Training Set
This concept is not applicable. This is a physical medical device. There is no "training set" in the context of an AI algorithm. The manufacturing process of a surgical mask is optimized through quality control and adherence to specifications, not machine learning training.
9. How the Ground Truth for the Training Set was Established
This concept is not applicable. As there is no training set for an AI algorithm.
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