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The MA-40 and MA-112 are intended for use as room recirculating air cleaner. The systems are used for filtering out and inactivating airborne particles from the air for medical purposes.

The MA-40 has been demonstrated to reduce the following airborne microorganisms by >99.99% in a test chamber of 83ft3 (580 ft3)

The MA-112 has been demonstrated to reduce the following airborne microorganisms by >99.99% in a test chamber of 83ft3 (580 ft3)

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This document is a 510(k) premarket notification from the FDA, indicating that the device being reviewed is a Medical Recirculating Air Cleaner. Therefore, the device in question is not an AI/ML powered device. The information requested in your prompt (e.g., acceptance criteria for AI algorithms, sample sizes for AI model training/testing, expert adjudication, MRMC studies) is typically associated with the evaluation of AI/ML-driven medical devices.

Since the document provided is for a physical air cleaner and not an AI/ML device, most of the requested information does not apply. However, I can extract what is provided in the document related to acceptance criteria and performance of this physical medical device.

Here's an analysis based on the provided document:

The acceptance criteria for the MA-40 and MA-112 air cleaners are based on their ability to reduce airborne microorganisms. The study proves that the devices meet these criteria by demonstrating a reduction of specific bioaerosol types by >99.99% within certain timeframes in a test chamber.

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

The acceptance criterion for both devices is to reduce airborne microorganisms by >99.99%. The tables provided show the time taken to achieve this reduction for different bioaerosol types and device fan speeds. The device performance (achieving >99.99% reduction) is reported for each scenario.

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

• Sample Size: The test "sample" here refers to the conditions under which the device was tested. The performance was demonstrated "in a test chamber of 83ft³ (580 ft³)". It's not a biological sample size in the sense of patient data. The document does not specify the number of individual tests or replicates run for each bioaerosol type and fan speed.
• Data Provenance: The document does not specify the country of origin of the data or whether the study was retrospective or prospective. It is a report of performance in a controlled test chamber.

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 question is not applicable as this is a physical device being tested for its ability to filter and inactivate microorganisms, not an AI/ML algorithm requiring expert interpretation of medical images or data. The "ground truth" is measured by the reduction of bioaerosols in a controlled environment.

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

Not applicable. This is not an adjudication process for human interpretation or AI output. The performance is based on quantitative measurements in a laboratory setting.

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. This is not an AI-powered device, and therefore no MRMC study involving human readers and AI assistance would be conducted.

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

Not applicable, as this is not an AI/ML algorithm. The "standalone" performance is the device's ability to purify air without human intervention beyond turning it on and setting the fan speed.

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

The "ground truth" is the measured reduction of specific bioaerosol concentrations within the test chamber. This is determined through laboratory methods suitable for quantifying microorganisms, which is an objective measurement rather than a subjective expert assessment.

8. The sample size for the training set

Not applicable. This is a physical device, not an AI model that requires a training set.

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

Not applicable. There is no training set for this type of device.

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