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This nebulizer compressor is an AC-powered air compressor intended to provide a source of compressed air for medical purposes for use in home health care. This device is used in conjunction with a pneumatic nebulizer to produce a fine aerosol mist of medication for respiratory therapy, for both children and adults suffering from respiratory disorders such as asthma, allergies, etc.

This piston-driven nebulizer contains vents in the two sides and bottom of the case providing ventilation for the motor. The compressor has only one control a double pole toggle switch to turn the compressor on and off. The unit has a two-wire power cord with polarized plug, an internal fuse, and no exposed metal that is likely to become energized (two screws that hold the case together are greatly recessed on the bottom surface and are not likely to become energized). In use, the compressor is placed on a flat surface and the cover is opened to reveal an outlet hose barb to which the oxygen (air) delivery tubing and nebulizer are connected. Inlet air to the compressor passes through a replaceable filter.

Here's an analysis of the provided text regarding the acceptance criteria and supporting studies for the John Bunn Neb-U-Lite II™ Medication Compressor:

1. Table of Acceptance Criteria and Reported Device Performance:

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

The document states, "None of the testing demonstrated any design characteristics that violated the requirements of the Reviewer Guidance or resulted in any safety hazards. It was CITECH's conclusion that the John Bunn Neb-U-Lite II™ Medication Compressor with Disposable Nebulizer, Model #JBO-112-009 device sample tested met all relevant requirements..."

• Sample Size: The exact number of units tested is not specified, but it refers to a "device sample." This typically implies a small, representative number of production units.
• Data Provenance: The testing was conducted by CITECH, an external testing laboratory. The country of origin is not explicitly stated, but the submission is to the FDA, implying the data would be accepted within a US regulatory context. The testing described is prospective in nature, as it involves physical and electrical testing of the newly manufactured device.

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

This information is not provided in the document. The tests described are primarily engineering and performance tests against established standards and guidelines, not clinical evaluations requiring expert interpretation of patient data.

4. Adjudication Method for the Test Set:

This information is not applicable as the tests are non-clinical, objective measurements against pre-defined engineering standards established by organizations like UL, DCRND, and IEC. There is no mention of subjective expert agreement or a need for adjudication in these types of tests.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:

No, an MRMC comparative effectiveness study was not done. The submission explicitly states: "8. Discussion of Clinical Tests Performed: Not Applicable". This device is a compressor; clinical studies involving human readers and interpretation are not relevant to its type of evaluation for 510(k) clearance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

This question is not applicable to the device described. This is a physical medical device (compressor), not an algorithm or AI software. Therefore, the concept of "standalone algorithm performance" does not apply.

7. The Type of Ground Truth Used:

The "ground truth" for this device's evaluation is primarily established by:

• Engineering Standards and Guidelines: UL1431, FDA DCRND Reviewer Guidance (November 1993 for Premarket Notification Submissions and for Home Use Respiratory Devices), IEC 801-4, and IEC-801-5.
• Predicate Device Performance: The DeVilbiss Model 4650D Compressor Nebulizer (K#931015) serves as a benchmark for comparable performance characteristics (compressor operating pressure, liter flow ranges).

These standards and predicate device performance effectively serve as the "ground truth" to which the new device's performance is compared for demonstrating substantial equivalence.

8. The Sample Size for the Training Set:

This question is not applicable. The device is a hardware compressor, not a machine learning model. Therefore, there is no "training set" in the context of AI/ML.

9. How the Ground Truth for the Training Set Was Established:

This question is not applicable for the same reason as point 8.

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For projecting the sounds associated with the heart, arteries, veins and other internal organs, using the principles of electronic amplification to enhance the acoustical sounds of the device.

This electronic stethoscope electronically amplifies sound with fingertip control; it is turned on by pressing and holding a button on the back of the diaphragm housing. The stethoscope has a volume control and a two position frequency response switch.

The provided text describes a 510(k) summary for the Labtron Electromax Electronic Stethoscope (K961857). This submission is for a traditional medical device, not an AI/ML-driven device. As such, many of the requested categories in the prompt are not applicable or cannot be extracted from this type of document, which focuses on substantial equivalence to a predicate device based on non-clinical performance and intended use, rather than complex clinical studies or AI algorithm performance.

Here's an analysis based on the information provided:

1. A table of acceptance criteria and the reported device performance
   • Acceptance Criteria: The document does not explicitly state quantitative clinical acceptance criteria. Instead, the "acceptance criteria" for substantial equivalence are inferred from the comparison to the predicate device and adherence to general medical device safety and performance standards.
   • Reported Device Performance: The primary "performance" reported is its electronic amplification capability and its functional similarity to the predicate. No quantitative clinical performance metrics (e.g., sensitivity, specificity for detecting specific heart sounds) are provided.

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

   • Test Set Sample Size: Not applicable. This summary does not describe a clinical test set with human subjects/data for evaluating performance metrics.
   • Data Provenance: Not applicable. The "testing" referred to is non-clinical engineering and performance testing of the device itself, not data from patients.

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)

   • Not applicable. No ground truth was established by experts for a clinical test set as part of this submission.

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

   • Not applicable. No clinical test set requiring adjudication was performed.

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-assisted device, and no MRMC study was conducted.

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

   • Not applicable. This is a standalone medical device (electronic stethoscope), but not an algorithm. Its performance is inherent to its physical and electronic design.

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

   • Not applicable. For a traditional medical device like this, "ground truth" in the clinical sense (e.g., for disease detection or diagnosis) is not established within the 510(k) summary. The "ground truth" for the device's function is its ability to electronically amplify sounds as per its design specifications.

8. The sample size for the training set

   • Not applicable. This device does not have a "training set" in the context of machine learning.

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

   • Not applicable. There is no training set for this device.

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