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The intended use of the Vitalograph Model 6000 Alpha is the simple assessment of respiratory function through the measurement of dynamic lung volumes i.e. spirometry. The device measures patient respiratory parameters including FVC, FEV1, FEV6, PEF, MVV and VC. The device is designed to be operated by medical professionals trained in respiratory and lung function testing on adults and pediatrics, 5 years and older, in a variety of professional healthcare environments, e.g. primary care, hospitals and occupational health centers.

The Vitalograph Alpha Model 6000 is a desktop spirometer which measures the following lung function parameters FVC, FEV1, FEV6, PEF, MVV and VC in professional healthcare environments, e.g., primary care, hospitals and occupational health centers. It is externally powered from a Class II, IEC 60601-1 compliant medical power supply. It contains a rechargeable battery powered from the external supply. The device also contains an integral 4 inch thermal printer. The device has a USB port for connection to other devices and an SD card slot for backup of stored data. The device also has wired ethernet and Wi-Fi for connection to a hospital network. Its primary functions and technology are: - Spirometry measurements using single breath and multiple-breath testing techniques, the display and recording of measured lung volumes and flow rates (including FVC, FEV1, FEV6, PEF, MVV and VC) are identical to the predicate device - Record subject data - Storage of data and test results on unit for later printing or export to Spirotrac software which was cleared under 510(k) K201562. The Flowhead utilizes a Fleisch Pneumotachograph. The operating principle is identical to the predicate K200550 - User Interface navigation via touch screen display

The provided text describes the regulatory clearance of the Vitalograph Model 6000 Alpha spirometer and details its comparison to a predicate device. It primarily focuses on the device's technical specifications, regulatory compliance, and non-clinical performance testing rather than a study proving the device meets acceptance criteria in the context of an AI/ML model for clinical decisions.

Based on the provided document, here's an analysis of the acceptance criteria and study that proves the device meets them:

This document is for a diagnostic spirometer, which is a physical medical device that measures lung function. It is not an AI/ML device for clinical decisions. Therefore, many of the typical "acceptance criteria" and "study types" associated with AI/ML devices (like MRMC studies, ground truth establishment by experts, adjudication, sample size for training sets, etc.) do not apply in this context.

The "acceptance criteria" for a physical diagnostic device like a spirometer primarily revolve around its technical performance specifications, electrical safety, EMC, and compliance with relevant international standards.

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

The document doesn't present a formal "acceptance criteria" table in the AI/ML sense. Instead, it provides a "Comparison of Subject and Predicate Devices" (Table 1) which implicitly serves as a comparison against established performance benchmarks and standards for spirometers. The performance data section further details the testing performed to demonstrate compliance.

Here's an attempt to derive "acceptance criteria" from the Specifications reported in the comparison table and the Performance Data section:

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

• Test Set Sample Size: Not applicable in the context of patient data or clinical test sets for AI/ML validation. The testing described is bench testing using standardized methods and controlled inputs (e.g., flow/volume simulators, environmental chambers).
• Data Provenance: Not applicable as it's not a data-driven AI/ML study. The "data" here comes from direct measurements by the device itself under test conditions.

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 device performance (e.g., whether a spirometer accurately measures volume) is established by calibration against known, traceable standards and instruments, not by human expert interpretation of results. The "ground truth" for spirometry measurements comes from the physical and engineering principles of the measurement itself and the standards against which it is calibrated and tested.

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

• Not applicable. Adjudication is a process used in studies where human interpretation or clinical judgment is involved, particularly for establishing a consensus "ground truth" from multiple readers. This is a technical device performance test, not a reader study.

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. An MRMC study is relevant for AI/ML diagnostic aids where human readers interpret medical images or data. This is a fundamental diagnostic device, not an AI assistance tool for human readers.

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

• The device itself is a "standalone" diagnostic instrument. Its performance is evaluated intrinsically through bench testing against specified standards and its predicate, rather than being an "algorithm only" being evaluated for clinical decision support. Its core function is to measure parameters directly, not to provide an automated clinical interpretation that would fall under "algorithm only" performance in the AI/ML sense.

7. The type of ground truth used:

• The "ground truth" for the device's technical performance is based on established engineering standards and reference measurements, such as those defined by ATS/ERS (2019), ISO 23747, and ISO 26782. These standards specify how spirometers should measure flow and volume and define the acceptable accuracy limits. For electrical safety and EMC, the ground truth is compliance with the relevant IEC/AAMI standards.

8. The sample size for the training set:

• Not applicable. This device does not use machine learning, so there is no "training set."

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

• Not applicable. As there is no training set for machine learning.

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