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Intended use for the Flowmate LTE is for Pulmonary Function Testing (measuring a person's ability to move air into and out of their lungs). The volume and flow rates measured for the entire effort and then reported as numerical results with a printout of the effort if selected.

Flow sensing pneumotach and base that measures differential pressure as air passes through it (the same principle as the Flowmate, PC Flow +, Renaissance, and Flash devices)
Compact design molded from high impact UL rated ABS plastic. rechargeable Ni-Cad batteries for portable operation,
Automatic calculations of test results, and best test selection based on ATS criteria
Testing capabilities include; FVC, FEV1, FEV3, FEV1/FVC%, FEF.2-1.2, FEF25-75%, FEF75-85, PEF, FEF50, FIVC, FEF50/FIF50, PIF, MVV, Methacholine Challenge, and Lung Age
Testing Capabilities: Capacity 10 Liters
Volume Accuracy +/- 3% Max. Flow Rate +/- 16 L/s
Back Pressure Less than 1.5 cm H2O
Population Predicted Normals included: Composite Knudson Crapo (ITS) ECCS HSU
Customization allows user to change report format, input temperature and barometric readings, and select printer driver for printer being used
Reports printed on standard 8 1/2 " x 11" paper
Field calibration with a 1 to 9 Liter calibration syringe
Cross-contamination control via external filter system

Here's an analysis of the provided text regarding the acceptance criteria and study for the Flowmate LTE device:

Acceptance Criteria and Device Performance

Study Information

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

• Test Set Sample Size:
  • Waveform Performance Testing: 24 waveforms were used.
  • MVV Testing: A 2-liter volume at flow rates from 40 to 245 L/M was used. The number of specific tests or readings performed is not specified, but it refers to a range of flow rates.
• Data Provenance: The document does not explicitly state the country of origin or if the data was retrospective or prospective. The testing appears to be laboratory-based performance testing rather than human clinical data.

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

• The ground truth for the test set (waveforms and MVV) was established based on "ATS recommendations" and "delivered values" from a 2-liter calibration syringe. No human experts are mentioned as directly establishing the ground truth for these performance tests.

4. Adjudication method for the test set:

• Not applicable, as the ground truth was based on pre-defined standards (ATS recommendations) and objective measurements from a calibration syringe, not on human interpretation requiring adjudication.

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:

• No, a multi-reader multi-case comparative effectiveness study was not done. This device is a diagnostic spirometer, which directly measures physiological parameters, not an AI-powered image analysis or diagnostic support tool that would typically involve human readers.

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

• Yes, the performance testing described for the Flowmate LTE is a standalone performance study. The device's measurements (volume, flow rates) are directly compared against established standards (ATS recommendations) and known delivered values (from a calibration syringe). There is no "human-in-the-loop" aspect to the performance evaluation itself, though the device is intended for human use.

7. The type of ground truth used:

• The ground truth used was based on expert consensus standards (ATS recommendations for FVC, FEV1, FEF 25-75, PEF, MVV accuracy and precision) and objective measurements from a calibrated device (a 1 to 9 Liter calibration syringe for field calibration, and a 2-liter volume for MVV testing).

8. The sample size for the training set:

• The document does not describe a "training set" in the context of machine learning or AI. This device appears to be a hardware-based medical device with integrated software for calculations, not a learning-based algorithm that requires a training set.

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

• Not applicable, as there is no mention of a training set for a machine learning algorithm.

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