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The Dukal AAMI Level 4 Open-Back Protective Gowns are intended to protect health care personnel and patients from the transfer of microorganisms, body fluids, and particulate material. These gowns are not intended for use in the operating room.

The Dukal AAMI Level 4 Open-Back Protective Gown meets the barrier protection requirements of AAMI Level 4 per ANSI/A AMI PB70, Liquid Barrier Performance and Classification of Protective Apparel and Drapes Intended for Use in Health Care Facilities, but has an open back which is non-protective.

These gowns are single use, disposable medical devices provided non-sterile.

Dukal AAMI Level 4 Open-Back Protective Gowns are intended to protect health care personnel and patients from the transfer of microorganisms, body fluids, and particulate material These gowns are not intended - for use in the operating room. The gowns are available on Regular and X-Large sizes.

Dukal AAMI Level 4 Open-Back Protective Gowns are made with laminate material (SMS nonwoven material with polyethylene film).

All gowns are blue, with neck removal feature, belt ties, and thumb hook cuff sleeves.

These gowns are single use, disposable devices, that are provided non-sterile.

The provided document is a 510(k) premarket notification for a medical device: Dukal AAMI Level 4 Open-Back Protective Gown. This document focuses on demonstrating substantial equivalence to a predicate device through non-clinical testing rather than clinical studies involving human patients or complex AI algorithms. Therefore, many of the requested categories related to clinical trials, multi-reader multi-case studies, and AI-specific ground truth are not applicable.

Here's the breakdown of the acceptance criteria and the study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: For the Liquid Barrier Performance Classification test (ASTM F1671), the testing was performed using 3 lots and 32 samples per lot in each critical zone area (body/sleeve and heat-sealed seams). For other tests, specific sample sizes are not explicitly stated within the provided text, but the results are reported as meeting the requirements.
• Data Provenance: The nature of these tests (material properties) suggests they were conducted in a laboratory setting. There is no information provided regarding the country of origin of the data or whether it was retrospective or prospective, as these terms are not typically applicable to physical product testing of this kind.

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

Not applicable. These are non-clinical, objective material property tests and do not involve expert interpretation or ground truth establishment by medical professionals. The "ground truth" is determined by the standardized test methods and their defined pass/fail criteria.

4. Adjudication method for the test set

Not applicable. Adjudication methods (like 2+1, 3+1) are used for dispute resolution or consensus building among human readers, typically in image interpretation or clinical diagnosis. These tests involve objective physical measurements against established standards.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done

No, an MRMC comparative effectiveness study was not done. This type of study is relevant for evaluating the performance of AI-assisted diagnostic tools with human readers. The device in question is a protective gown, which is not an AI-powered diagnostic device.

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

No, a standalone algorithm performance study was not done. This device is not an algorithm or AI model.

7. The type of ground truth used

For this device, the "ground truth" is the objective, measurable performance against established industry standards (e.g., AAMI Level 4 requirements, ASTM, ISO, AATCC standards). There is no "expert consensus," "pathology," or "outcomes data" in the typical sense for a protective gown's material performance. The ground truth is embedded in the standardized test methods and their pass/fail criteria.

8. The sample size for the training set

Not applicable. This device does not involve a training set as it is not an AI/machine learning model.

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

Not applicable, as there is no training set for this device.

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The Dukal Level 1 Pediatric Face Masks are intended to be worn by the patient/child to cover the nose and mouth to provide a barrier for the respiratory tract from microorganisms, body fluids, and particulate material. Recommended ages are 4-12. The face mask is specifically for use with patients whose age or illness may prevent them from taking necessary precautions in situations where transfer of microorganisms, body fluids, and particulates can occur. The face masks are recommended for use in a healthcare setting with appropriate adult supervision. The Dukal Level 1 Pediatric Face Masks are single use, disposable devices that are provided non-sterile.

Pediatric face masks are intended to be worn by the patient/child to cover the nose and mouth to provide a barrier for the respiratory tract from microorganisms, body fluids, and particulate material. The mask is available in the following product sizes: Regular Size (14.5x9cm) and Extra Small Size (12.5x8cm). The Dukal Level 1 Pediatric Face Mask (both sizes) are composed of three layers of materials and pleated to form the mask. The inner layer is composed of Polypropylene (Spunbond), the middle layer is Polypropylene (Meltblown) filter material, and the outer layer is polypropylene (Spunbond). The masks (both sizes) are held in place on wearer with knitted polyester/spandex elastic ear loop and contain a malleable aluminum nosepiece strip. The masks (both sizes) have decorative patterns, printed with colored inks.

This document describes the performance testing for the Dukal Level 1 Pediatric Face Mask. The device is a medical face mask and the acceptance criteria and performance are based on established standards for such devices.

Here's the breakdown of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The device performance is compared to the requirements specified by various test standards, primarily ASTM F2100-19 Level 1 requirements for medical face masks.

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

The document does not explicitly state the sample sizes used for each specific test (e.g., number of masks tested for BFE). However, the testing conducted is standard for medical devices and typically follows the sample size requirements prescribed by the referenced ASTM, ISO, EN, and CFR standards.

The data provenance is from non-clinical testing conducted according to recognized international and US standards. The data is prospective in the sense that the tests were performed specifically for this device to assess its performance against predefined criteria. The location of the testing is not specified, but these are generally conducted by certified third-party laboratories.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

This submission is for a medical face mask, and "ground truth" as typically defined for AI/ML models (e.g., expert consensus on medical images) is not applicable here. The "ground truth" for the test set is established by the specified performance requirements of the referenced international and national standards (ASTM, ISO, EN, CFR). These standards are developed by committees of experts in relevant fields (materials science, microbiology, toxicology, engineering, etc.).

4. Adjudication Method for the Test Set

Not applicable. The tests are laboratory-based and yield quantitative results against pre-defined thresholds. There is no human interpretation or adjudication in the traditional sense of medical image analysis for AI/ML.

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

No. An MRMC study is relevant for evaluating the impact of AI on human reader performance in diagnostic tasks, typically involving medical imaging. This submission concerns a physical medical device (face mask) and its material properties and safety, not an AI/ML diagnostic algorithm.

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

Yes, in a conceptual sense. The performance tests reported are for the device itself ("algorithm only" performance in a metaphorical sense), independent of human use or interpretation during the test. The tests measure the intrinsic properties of the mask.

7. The Type of Ground Truth Used

The ground truth used is the performance specifications and safety limits defined by established industry and regulatory standards (e.g., ASTM F2100-19 for masks, ISO 10993 for biocompatibility, etc.). These standards are based on scientific evidence and consensus among experts to ensure the safety and efficacy of medical devices.

8. The Sample Size for the Training Set

Not applicable. This is not an AI/ML device that requires a training set. The device is a physical product whose performance is assessed through laboratory testing.

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

Not applicable, as this is not an AI/ML device.

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The Dukal Sterile AAMI Level 3 Reinforced Surgical Gown, Sterile AAMI Level 3 Surgical Gown, Sterile AAMI Level 4 Surgical Gown, and Sterile AAMI Level 4 Splicing Surgical Gown are 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. Per ANSI/AAMI PB70:2012 Liquid barrier performance and classification of protective apparel and drapes intended for use in health care facilities, Dukal AAMI Level 3 Reinforced Surgical Gown and Sterile AAMI Level 3 Surgical Gown met the requirements for Level 3 classification; Dukal Sterile AAMI Level 4 Surgical Gown and Sterile AAMI Level 4 Splicing Surgical Gown met the requirements for Level 4 classification.

The Dukal Sterile AAM Level 3 Reinforced Surgical Gown, Sterile AAMI Level 3 Surgical Gown, Sterile AAMI Level 4 Surgical Gown, and Sterile AAMI Level 4 Splicing Surgical Gown are single use, disposable medical devices, and are provided sterile.

Surgical Gowns are 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. The Dukal Sterile AAMI Level 3 Reinforced Surgical Gown, Sterile AAMI Level 3 Surgical Gown, Sterile AAMI Level 4 Surgical Gown, and Sterile AAMI Level 4 Splicing Surgical Gown are single use, disposable medical devices, and are provided sterile. Each type of Surgical Gown is available in six product sizes: M, L, XL, XXL, XXXL and XXXL-XL.

Dukal Sterile AAMI Level 3 Reinforced Surgical Gown and Sterile AAMI Level 3 Surgical Gown are made with SMS nonwoven material. The Dukal Sterile AAMI Level 4 Surgical Gown and Sterile AAMI Level 4 Splicing Surgical Gown are made with BVB material.

Only the Dukal Sterile AAMI Level 3 Reinforced Surgical Gown is reinforced with laminated material. Dukal Sterile AAMI Level 3 Surgical Gown, Sterile AAMI Level 4 Surgical Gown, Sterile AAMI Level 4 Splicing Surgical Gown are non-reinforced.

Only the Sterile AAMI Level 4 Splicing Surgical gown has a different material for the back of the gown (SMS nonwoven fabric). The back of the gown for the Sterile AAMI Level 4 Surgical gown is made with BVB material.

All gowns are blue, with hook and loop closures, belt ties, and knitted cuff sleeves.

This document describes a 510(k) premarket notification for Dukal Sterile AAMI Level 3 Reinforced Surgical Gown, Sterile AAMI Level 3 Surgical Gown, Sterile AAMI Level 4 Surgical Gown, and Sterile AAMI Level 4 Splicing Surgical Gown. The submission establishes substantial equivalence to a predicate device (K192290: Xuchang Zhengde Environstar Medical Products Co., Ltd SMS Standard Surgical Gown (level 3), SMS Surgical Gown with Reinforcement (level 3), BVB Surgical Gown (level 4), BVB Splicing Surgical Gown (level 4)).

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are generally "Same" as the predicate device or "Meets requirement" based on specific test standards. The reported device performance consistently meets these criteria.

AAMI Level 3 Surgical Gowns:

AAMI Level 4 Surgical Gowns:

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

The document does not explicitly state the sample sizes used for each specific test in the "Test Results of Subject Device" columns. However, it indicates that the tests conform to recognized standards (e.g., AATCC, ASTM, ISO). These standards typically specify minimum sample sizes for each test.

The data provenance is not explicitly stated in terms of country of origin or whether it's retrospective or prospective. It is assumed to be prospective testing conducted on the finished device to demonstrate compliance with the referenced standards.

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

This is a submission for a physical medical device (surgical gowns) and not an AI/software device. Therefore, the concept of "experts used to establish ground truth" (e.g., radiologists) in the context of diagnostic interpretation is not applicable here. The "ground truth" is established by direct measurement and testing against established physical and chemical standards.

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

Not applicable. This is not a study involving human interpretation or adjudication of diagnostic findings. The tests are based on objective physical and chemical measurements following established laboratory protocols.

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. An MRMC study is relevant for AI-powered diagnostic devices, which is not the case for surgical gowns.

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

Not applicable. This is not an AI/algorithm-based device.

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

The ground truth is based on recognized consensus standards for physical and chemical testing of surgical apparel, such as:

• ANSI/AAMI PB70:2012 Liquid Barrier Performance and Classification of Protective Apparel and Drapes
• 16 CFR Part 1610 Standard for the Flammability of Clothing Textiles
• AATCC 127:2017 Water Resistance: Hydrostatic Pressure Test
• AATCC 42:2017 Water Resistance: Impact Penetration Test
• ASTM F1671/F1671M-13 Standard Test Method for Resistance to Materials Used in Protective Clothing to Penetration by Blood-Borne Pathogens
• ISO 9073-10:2003 Textiles-Test Methods for Nonwovens-Part 10: Lint and Other Particles Generation in the Dry State
• ASTM D1683/D1683M-17:2017/(R)2018 Standard Test Method for Failure in Sewn Seams of Woven Fabrics
• ASTM D5733-99 Standard Test Method for Tearing Strength of Nonwoven Fabrics by the Trapezoid Procedure
• ASTM D5034-09:2017 Standard Test Method for Breaking Strength and Elongation of Textile Fabrics (Grab Test)
• ASTM D737-18:2018 Standard Test Method for Air Permeability of Textile Fabrics
• ASTM F88/F88M-15 Standard Test Method for Seal Strength of Flexible Barrier Materials
• ASTM F1929-15 Standard Test Method for Detecting Seal Leaks in Porous Medical Packaging by Dye Penetration
• ISO 10993-7:2008 Biological Evaluation of Medical Devices-Part 7:Ethylene Oxide Sterilization Residuals
• ISO 10993-5:2009 Biological Evaluation of Medical Devices-Part 5: Tests for in Vitro Cytotoxicity
• ISO 10993-10:2010 Biological Evaluation of Medical Devices-Part 10: Tests for Irritation and Skin Sensitization

8. The sample size for the training set

Not applicable. This is not a machine learning or AI device that requires a training set.

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

Not applicable. As this is not an AI/ML device, there is no training set or ground truth in that context.

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The Dukal Corporation Level 3 Surgical Face Masks are 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.

Face Masks intended to protect health care patients and personnel from the transfer of microorganisms, body fluids and particulate material.

The provided document is a 510(k) summary for the Dukal Corporation Level 3 Surgical Face Masks. This type of document is for regulatory clearance of medical devices, demonstrating substantial equivalence to a predicate device, rather than proving that a device meets particular acceptance criteria through a clinical study. Therefore, much of the requested information about a study is not available in this text, as it describes performance testing results for a medical device (surgical face masks).

However, I can extract the acceptance criteria (defined by ASTM F2100-19 Level 3 requirements for the subject device and ASTM F2100-11 Level 3 requirements for the predicate) and the reported device performance.

Acceptance Criteria and Reported Device Performance

Device: Dukal Corporation Level 3 Surgical Face Masks with Ear Loop and Level 3 Surgical Face Masks with Tie On

As indicated in the table above, the Dukal Corporation Level 3 Surgical Face Masks meet or exceed the performance requirements for ASTM F2100-19 Level 3 surgical face masks.

The following information is not available in the provided document, as it pertains to a clinical study design, which is not typically part of a 510(k) submission for this type of device:

2. Sample sizes used for the test set and the data provenance: Not applicable. The tests are laboratory-based performance tests, not studies on a test set of data.
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 performance tests is established by industry standards (ASTM, EN).
4. Adjudication method: Not applicable.
5. Multi-reader multi-case (MRMC) comparative effectiveness study: Not applicable. This is not a diagnostic imaging or AI-assisted device.
6. Standalone (algorithm only without human-in-the-loop performance): Not applicable. This is a physical medical device (face mask), not an algorithm.
7. Type of ground truth used: For the performance data, the ground truth is established by the specified ASTM and EN standards for each test. For biocompatibility, it's defined by ISO 10993 acceptance criteria.
8. Sample size for the training set: Not applicable.
9. How the ground truth for the training set was established: Not applicable.

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The Dukal Corporation Level 2 Surgical Mask with Ear Loops, Dukal Corporation Level 2 Surgical Mask with Ear Loops and Face Shield, and Dukal Corporation Level 2 Surgical Mask with Adjustable Tie Ons 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.

Face Masks intended to be worn to protect both the patient and healthcare personnel from transfer of microorganisms, body fluids, and particulate material.

The provided text does not describe an AI/ML device but rather a surgical mask (Dukal Corporation Level 2 Surgical Mask with Ear Loops, Dukal Corporation Level 2 Surgical Mask with Ear Loops and Face Shield, and Dukal Corporation Level 2 Surgical Mask with Adjustable Tie Ons). Therefore, the information requested for AI/ML devices, such as sample sizes for test sets, data provenance, expert qualifications, adjudication methods, MRMC studies, standalone performance, and training set details, is not applicable and cannot be extracted.

However, I can provide the acceptance criteria and the reported device performance for the surgical masks based on the provided text, as these are relevant to non-AI/ML medical devices.

1. A table of acceptance criteria and the reported device performance (for the Surgical Mask):

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document mentions specific test standards for surgical masks. While it indicates that 32 test articles were used for the ASTM F1862 Fluid Resistance test, it does not explicitly state the sample sizes for the other tests, nor does it specify the data provenance (e.g., country of origin or whether the tests were retrospective or prospective). The tests are non-clinical, meaning they are performed on the device itself, not on human subjects.

3. 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)

This information is not applicable as the device is a surgical mask and not an AI/ML device requiring expert interpretation for ground truth establishment. The testing involves standardized laboratory procedures.

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

This information is not applicable as the device is a surgical mask and not an AI/ML device requiring adjudication of human expert interpretations.

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 information is not applicable as the device is a surgical mask and not an AI/ML device.

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

This information is not applicable as the device is a surgical mask and not an AI/ML device. The performance reported is that of the device itself under specified test conditions.

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

For the surgical mask, the "ground truth" is established by objective measurements and adherence to international and national standard test methods for material performance, such as bacterial filtration efficiency, particulate filtration efficiency, differential pressure, fluid resistance, and flammability. These are laboratory-based, quantitative assessments, not subjective interpretations by experts or clinical outcomes data.

8. The sample size for the training set

This information is not applicable as the device is a surgical mask and not an AI/ML device that requires a training set.

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

This information is not applicable as the device is a surgical mask and not an AI/ML device that requires a training set and associated ground truth.

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The Dukal Sterile Lubricating Jelly is a medical device intended for medical purposes, to lubricate body orifices to facilitate entry of diagnostic devices when a sterile field is required.

The Dukal Sterile Lubricating Jelly is a clear, greaseless, water soluble jelly.

The provided document, K113689, describes a 510(k) premarket notification for the Dukal Sterile Lubricating Jelly. This is not for a software or AI-powered medical device, but rather a physical medical device. Therefore, many of the requested criteria (like sample size for test set, number of experts for ground truth, MRMC studies, standalone algorithm performance, training set size, etc.) are not applicable in this context, as they relate to studies of diagnostic accuracy or performance of AI/software in interpreting data.

However, I can extract information related to the acceptance criteria and the study that proves the device meets those criteria, aligning with the information that is present in the 510(k) submission for a non-AI device.

Acceptance Criteria and Device Performance for Dukal Sterile Lubricating Jelly

The submission for the Dukal Sterile Lubricating Jelly demonstrates substantial equivalence to a predicate device (Dynarex Sterile Lubricating Jelly, K092488) based on comparable technological characteristics, intended use, and safety/effectiveness profiles. The "acceptance criteria" here are implicitly the criteria for demonstrating substantial equivalence, primarily through direct comparison of features and performance to a legally marketed predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

For this non-AI medical device, the acceptance criteria are based on exhibiting similar technological characteristics and performance to the predicate device.

2. Sample Size for Test Set and Data Provenance

For physical medical devices undergoing a 510(k) submission based on substantial equivalence, the "test set" does not typically refer to a dataset of patient cases. Instead, it refers to samples of the device itself undergoing various physical, chemical, and biological tests.

• Sample Size for Test Set: Not specified in terms of number of units, but implies a sufficient number of samples of the Dukal Sterile Lubricating Jelly were used for the listed biocompatibility and sterilization validation tests.
• Data Provenance: The data arises from internal testing performed on the Dukal Sterile Lubricating Jelly and comparison to the publicly available characteristics of the predicate device (Dynarex Sterile Lubricating Jelly). It is retrospective in the sense that the predicate device's characteristics are already established.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

This criterion is not applicable for this type of medical device submission. Ground truth, in the context of interpretation by experts, is relevant for diagnostic devices interpreting outputs like images or signals. For a lubricating jelly, "ground truth" is established through standardized laboratory testing (e.g., ISO standards for biocompatibility) and direct chemical/physical characterization, rather than expert interpretation of a test set.

4. Adjudication Method for the Test Set

This criterion is not applicable. Adjudication methods (like 2+1, 3+1) are for resolving discrepancies in expert interpretations of data in diagnostic studies. For a physical device, performance is evaluated against objective standards and test results.

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

No, an MRMC comparative effectiveness study was not done. These types of studies are for evaluating the performance of diagnostic systems (often AI-assisted) and human readers in interpreting clinical cases. This device is a lubricating jelly, not a diagnostic system.

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

No, this is not applicable. This applies to software algorithms without human intervention. The Dukal Sterile Lubricating Jelly is a physical product directly applied by a human user as part of a medical procedure.

7. The Type of Ground Truth Used

The "ground truth" for this device's performance relies on:

• Objective Laboratory Testing: Adherence to established standards for biocompatibility (ISO 10993 series for In-Vitro Cytotoxicity, Implantation, Irritation & Hypersensitivity, Systemic Toxicity).
• Sterilization Validation: Conformance to ISO/AAMI 11137 requirements for gamma irradiation sterilization with a specified Sterility Assurance Level (SAL).
• Chemical and Physical Characterization: Verification of ingredients and properties (e.g., water-soluble, non-staining, alcohol/fragrance-free) as listed on the label and compared to the predicate.

8. The Sample Size for the Training Set

Not applicable. This criterion refers to the dataset used to train an AI model. This submission is for a physical medical device and does not involve AI or machine learning.

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

Not applicable. As there is no training set for an AI model, this criterion does not apply.

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