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The Humask Pro Vision 3000 mask is single use disposable intended to be worn in the operating room as well as dental, isolation, and other medical procedures to protect both the patient and healthcare personnel from transfer of microorganisms, body fluids, and particulate material.

The Humask Pro Vision 3000 is a clear window surgical mask. A Medical grade face mask with a transparent window.

The provided text is a 510(k) Summary for the Humask Pro Vision 3000, a surgical mask. It primarily focuses on demonstrating substantial equivalence to a predicate device, not on proving device performance against specific acceptance criteria for an AI/ML-based medical device. Therefore, much of the requested information regarding AI/ML device testing (e.g., sample sizes for training/test sets, expert adjudication, MRMC studies, effect sizes, ground truth establishment) is not present in this document.

However, I can extract information related to the performance data provided for the physical mask itself and present what is available, while clearly indicating what information is not available based on the provided text.

Here's an attempt to answer your request based on the provided document, with many sections marked as "Not Applicable" or "Not Provided" due to the nature of the document.

Understanding the Device: The Humask Pro Vision 3000 is a physical surgical mask, not an AI/ML-based diagnostic or prognostic device. The performance data mentioned pertains to the physical characteristics and safety of the mask itself, such as its filtration efficiency and biocompatibility.

Acceptance Criteria and Reported Device Performance

The document describes performance tests conducted on the Humask Pro Vision 3000 to demonstrate its safety and effectiveness, similar to the predicate device. These tests align with FDA guidance for surgical mask submissions.

Table of Acceptance Criteria and Reported Device Performance (as detailed for a physical mask):

Study Proving Device Meets Acceptance Criteria:

The document states: "The following performance data support the substantial equivalence of the Humask Pro Vision 3000." and "The subject devices met all specified criteria and did not raise new safety or performance questions. Based on the performance testing, the Humask Pro Vision 3000 was found to have a safety and effectiveness profile that is similar to the predicate device."

This indicates that various standardized tests (ASTM, EN, ISO, CFR) were performed on the Humask Pro Vision 3000, and the results demonstrated compliance with the requirements outlined in those standards, thus proving it meets the implied acceptance criteria for a surgical mask of its type.

Regarding AI/ML-specific Study Information (Not applicable to this device, but addressing your prompt structure):

1. Sample size used for the test set and the data provenance: Not applicable. This is a physical mask.

   • Provenance: Not specified for the physical test samples (e.g., number of masks tested for BFE, PFE).

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 a physical mask is established by validated laboratory test methods and equipment, not human expert consensus.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done: Not applicable. This is not an AI/ML device that assists human readers.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Ground truth for the physical mask's performance is established by standardized laboratory testing procedures (e.g., the specific methodologies outlined in ASTM F2101 for BFE, ASTM F2299 for PFE, etc.). These methods are designed to objectively measure the physical properties and performance of the mask.

7. The sample size for the training set: Not applicable. This is not an AI/ML device.

8. How the ground truth for the training set was established: Not applicable. This is not an AI/ML device.

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ProVision is an independent diagnostic viewing and processing workstation. It is based on a Silicon Graphics Workstation running under Unix. ProVision communicates with imaging systems of different modalities (currently CT, MRI, CR, RF, NM) utilizing the DICOM - 3 standard. Connection may also be made to any other DICOM device. ProVision functions include :Archiving, displaying, manipulation, filming, 2and 3-dimensional processing. ProVision employs a graphical multi -Window, icon and mouse driven user interface. It is designed to ensure maximum flexibility on the one hand, and intuitive operation on the other.

The ProVision is a multi-modality diagnostic Workstation for processing and archiving radiological images. It is based on off-the shelf Silicon Graphics UNIX based computers that comply with the accepted international standards for computer systems. The systems also comprises software developed and validated by Algotec Systems Ltd.

The provided text does not contain detailed information about specific acceptance criteria or an analytical study proving the device meets those criteria. The document is a 510(k) summary for a software update to a diagnostic workstation (ProVision) and focuses on demonstrating substantial equivalence to a predicate device.

However, based on what is provided, here's a breakdown of the requested information, acknowledging the limitations of the input text:

1. A table of acceptance criteria and the reported device performance

The document states: "The new updates simply provide quicker methods to reach results that would have been time consuming on the previous version of Pro Vision software. The addition of these software updates raises no new issues of safety or effectiveness."

This implies that the key performance criteria for the update are related to efficiency/speed and maintaining safety and effectiveness at least equivalent to the predicate device. However, no quantitative acceptance criteria or specific performance metrics are reported in this document.

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

This information is not provided in the given text.

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 provided in the given text. The document refers to the system's intended use "by radiologists as an interactive tool for analyzing radiological data," but doesn't detail any expert involvement in a study.

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

This information is not provided in the given text.

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

There is no indication that a multi-reader multi-case (MRMC) comparative effectiveness study was done. The document focuses on software updates for an existing diagnostic workstation, emphasizing efficiency improvements and substantial equivalence, not the comparative performance of human readers with vs. without AI assistance. The device is described as "an interactive tool for analyzing radiological data," implying human-in-the-loop, but no comparative study is mentioned.

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

The device, ProVision, is described as an "independent diagnostic viewing and processing workstation" and an "interactive tool for analyzing radiological data." This inherently implies a human-in-the-loop system. Therefore, a standalone (algorithm only) performance study would likely not be relevant or described for this type of device, and there is no mention of one.

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

This information is not provided in the given text.

8. The sample size for the training set

This information is not provided in the given text.

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

This information is not provided in the given text.

In summary: The provided document is a 510(k) summary focused on demonstrating substantial equivalence for software updates to an existing diagnostic workstation. It asserts that the updates improve efficiency and do not introduce new safety or effectiveness issues. However, it lacks detailed information regarding specific study designs, acceptance criteria, sample sizes, expert involvement, or ground truth establishment that would typically be found in a comprehensive performance study report. The claims are high-level and refer to the inherent nature of the updates within the existing system.

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Ask a specific question about this device