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The Model 9160 VitaloQUB is a whole-body plethysmograph device, when used with the Vitalograph Model 9100, is designed for lung function testing on adults and pediatrics, 6 years and older, by trained medical healthcare professionals in a variety of professional healthcare environments e.g., primary care, hospitals, and pharmaceutical research centers.

The proposed Model 9160 VitaloQUB incorporates the cleared Model 9100 (K221030) with integrated LCD display and ComPAS2 software (K213872).

The ComPAS2 software controls valves and reads unprocessed data from the sensors in the Model 9100 and from Model 9160. The ComPAS2 software then determines respiratory parameters including the 2 new parameters.

The ComPAS2 software is unchanged from K213872. The Model 9160 and Model 9100 firmware does not determine any respiratory parameters.

The Model 9160 is adding 2 additional parameters:

• TVG – Thoracic Gas Volume
• Raw Airway resistance

The provided text describes the regulatory clearance for the VitaloQUB Model 9160, a whole-body plethysmograph device. However, it does not contain specific details on the acceptance criteria or a dedicated study report that demonstrates the device explicitly meets numerical acceptance criteria. The text focuses on establishing substantial equivalence to predicate devices.

Here's an analysis based on the information available and what is missing:

The submission states that "Performance testing demonstrated that the subject device met its acceptance criteria," and then lists the types of testing performed. However, it does not provide the specific numerical acceptance criteria or the reported device performance for these criteria.

1. Table of acceptance criteria and the reported device performance:

Missing Information: For VTG and Raw, while the document states performance testing was done, the specific acceptance criteria and the results demonstrating compliance are not provided. The accuracy values listed in the table are copied directly from the "Subject Model 9160" column, indicating that these are the device's inherent specifications, and the comparison section implies they are similar to or meet expectations based on the predicate.

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 document. The text mentions "Bench testing" but does not detail the sample sizes for any of the performance tests, nor does it specify if any clinical data with human subjects (and thus data provenance) was used for direct performance evaluation of VTG and Raw measurements. The comparison tables focus on technological characteristics and principle of operation similarities to predicates.

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 document. The device outputs objective physiological measurements, rather than interpretations requiring expert consensus as ground truth. If clinical studies were performed for the new parameters (VTG, Raw), the method of establishing ground truth would depend on the study design. However, the document provided does not detail such clinical studies or the involvement of experts in establishing ground truth.

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

This information is not provided in the document. Given the nature of the device (measuring physiological parameters rather than rendering diagnoses or classifications), an adjudication method for a test set as described is unlikely to be directly applicable in the same way as for image-based diagnostic AI. If human subject studies were conducted to compare measurements, adjudication of patient conditions might be relevant, but this is not detailed.

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:

This is not applicable to this device. The VitaloQUB Model 9160 is a pulmonary function measurement device. It measures physiological parameters and does not involve "readers" or "AI assistance" in the diagnostic interpretation sense for which MRMC studies are typically performed. The device itself performs the measurements and calculations (via the ComPAS2 software), it does not assist human interpretation of complex data (like images) in a way that would be quantified by an MRMC study.

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

The device is inherently a standalone measurement system in terms of calculating the parameters. The ComPAS2 software, which is part of the system, outputs the respiratory parameters. This is the "algorithm only" performance. The document states: "The ComPAS2 software controls valves and reads unprocessed data from the sensors in the Model 9100 and from Model 9160. The ComPAS2 software then determines respiratory parameters...". The performance testing mentioned ("Bench testing", "ATS / ERS (2002, 2005, 2013, 2017 and 2019) Static condition") assesses the accuracy of these measurements directly from the device/software. Specific performance for VTG and Raw measurements would have been assessed in this standalone manner, but the numerical results are not provided.

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

For physiological measurement devices, the "ground truth" is typically established by physical standards, calibration gases, and established reference methods or simulated physiological conditions that adhere to recognized industry standards (e.g., ATS/ERS standards). The document mentions compliance with various ISO and ATS/ERS standards, which dictate the methods and accuracy requirements for such measurements. For example, gas concentrations for DLCO are compared against medical-grade gas mixes, and flow/volume against calibrated instruments.

8. The sample size for the training set:

This information is not provided. As the device is a measurement instrument incorporating software (ComPAS2) for calculations based on physical readings, it's not described as an AI/ML device that requires a "training set" in the conventional sense (e.g., for pattern recognition or classification). The software implements algorithms for physiological calculations rather than learning from data in a machine learning paradigm.

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

This information is not provided, and likely not applicable as the device is not described as an AI/ML system requiring a training set with ground truth in the context of machine learning. The algorithms are based on established physiological principles and equations.

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The Model 9100 PFT/DICO is a pulmonary function testing device which uses Morgan Scientific's ComPAS2 software to measure subject respiratory parameters including FVC, SVC, MVV, CPF, RMS, SNIP, DLCO, MBN2 and SBN2.

The device is PC-based and designed for lung function testing on adults and pediatrics, 6 years and older, in a variety of professional healthcare environments e.g., primary care, hospitals, pharmaceutical research centers and physicians' offices.

The Model 9100 PFT/DICO is intended for the assessment of respiratory function through the measurement of dynamic lung volumes i.e., spirometry and other lung functions i.e., diffusing capacity.

The Model 9100 PFT/DICO is composed of various sensors and valves with associated low level firmware. The firmware interfaces with the Morgan Scientific's ComPAS2 software (K213872) that resides on an on-board computer. The Model 9100 also provides for user input and present resulting data on an integral display.

The ComPAS2 software controls valves and reads unprocessed data from the sensors in the Model 9100then determines respiratory parameters including FVC, SVC, MVV, CPF, RMS (MIP and MEP), SNIP, DLCO, MBN2 and SBN2. The Model 9100 PFT/DICO firmware does not determine any respiratory parameters.

The ComPAS2 software uses flow and volume from the Vitalograph pneumotachograph spirometer to display the flow and volume information measured directly from patient effort. ComPAS2 also utilizes gas analyzer readings from the Model 9100 patient test benchmark to display dilution lung volume data and single / multi breath diffusion data measured directly from patient effort. This information is then provided in a report format.

The provided text describes the regulatory clearance of the Vitalograph Model 9100 PFT/DICO, a pulmonary function testing device, and its substantial equivalence to a predicate device. However, it does not contain information about a study proving the device meets acceptance criteria related to a machine learning or AI model's performance.

The document outlines performance testing conducted for the device's electrical, mechanical, and biocompatibility aspects, as well as software verification and validation. It explicitly states that the device uses "Morgan Scientific's ComPAS2 software to measure subject respiratory parameters," but there's no indication that this software includes an AI or machine learning component that would require a study with human-in-the-loop performance, expert ground truthing, or MRMC studies typically associated with AI/ML medical devices.

Therefore, many of the requested details about acceptance criteria for an AI model's performance and associated study specifics (sample size for test/training, number of experts, adjudication, MRMC, standalone performance, ground truth type) cannot be extracted from this document.

Instead, the document focuses on demonstrating substantial equivalence to a predicate device based on similar indications for use, technological characteristics, and principles of operation, supported by standard bench testing and software validation.

Here's an attempt to answer the request based only on the provided text, highlighting the absence of AI/ML-specific details:

Device: Vitalograph Model 9100 PFT/DICO

Study Type: This document describes a 510(k) premarket notification for substantial equivalence, supported by bench testing, software verification/validation, and compliance with various standards. It is not an AI/ML performance study. The "study that proves the device meets the acceptance criteria" refers to the entire body of evidence submitted for 510(k) clearance, rather than a specific AI model's performance study.

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

The document defines performance specifications and states that testing supported the safety and performance, implying these specifications were met. The specific "acceptance criteria" for the overall device's performance are embedded in the compliance with standards and the "similar" comparisons to predicate/reference devices.

Note on "Acceptance Criteria" for AI: The document does not describe acceptance criteria for an AI or machine learning model. The stated accuracies (e.g., flow, volume, gas sensors) are for the physical measurement components of the device, not a predictive algorithm based on complex data interpretation.

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

• Sample Size for Test Set: Not specified for any performance testing, other than the implication that tests were sufficient to meet specific standards (e.g., ATS/ERS waveforms, drop tests, cleaning validations). There is no test set in the context of an AI/ML model's performance.
• Data Provenance: Not applicable in the context of typical AI/ML data provenance (e.g., country of origin, retrospective/prospective clinical data). The performance tests are largely bench-based or simulated.

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

• Not applicable. There is no mention of human experts establishing ground truth for a test set, as would be done for an AI/ML interpretation task. Ground truth for the device's measurements would be established by reference standards or highly accurate laboratory equipment.

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

• Not applicable. No expert adjudication process is described.

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 comparative effectiveness study was not done. The device measures physiological parameters; it does not "assist" human readers in interpreting complex medical images or data.

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

• Not applicable in the context of an AI/ML algorithm. The device itself is the "standalone" entity that performs measurements. The software (ComPAS2) controls the device and processes the raw sensor data, but there's no indication of it being a standalone AI/ML interpreter.

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

• The ground truth for the device's performance relies on calibration standards, reference instruments, and established engineering/medical device testing protocols (e.g., ATS/ERS guidelines for spirometry, ISO standards for gas analysis accuracy, and various electrical/mechanical standards). There is no "expert consensus," "pathology," or "outcomes data" used for performance validation in the AI/ML sense.

8. The sample size for the training set

• Not applicable. There is no mention of an AI/ML model that would require a training set. The ComPAS2 software and device firmware are likely developed using traditional software engineering and embedded system development methods, not machine learning model training.

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

• Not applicable, as there is no AI/ML training set.

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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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The Vitalograph Spirotrac Model 7000 is a PC-based software application intended to be used as a spirometer or connect to compatible Vitalograph or third party devices to acquire, view, store and print the device output. The product is designed for use on adults and pediatrics, 5 years and older, in a variety of professional healthcare environments, e.g. primary care, hospitals and occupational health centers under the supervision of a healthcare provider.

The Vitalograph Spirotrac Model 7000 provides a secure PC based medical device software application for creating, adding and recalling subjects and performing Spirometry testing on those subjects. Spirotrac will also link to compatible third party devices to read and display the output from these devices to allow the information to be retained with the subject.

Spirotrac integrates and reads / displays information from compatible Pulse Oximetry devices, Blood Pressure and Weight measurements devices, and ECG test devices.

Its primary functions are:

Spirometry measurements using single breath and multiple-breath testing techniques, the display and recording of measured lung volumes and flow rates (including VC, FIVC, FVC) and its subdivisions. The unit also allows for the measurements of Inspiratory and Expiratory Flow rates (PEF, FEFx, etc.), indirect measures (e.g. MVV) and Pre-post testing (e.g. Challenge, work shift).

Record subject demographic data as input.

Interact with existing Vitalograph and compatible third party devices via standard PC communication methods for download of data for storage within the Spirotrac database.

Navigation is allowed via the use of a standard PC keyboard and mouse or touchscreen.

The Vitalograph Spirotrac Model 7000 is a PC-based software application intended to function as a spirometer or connect to compatible devices to acquire, view, store, and print device output. It is designed for use on adults and pediatrics (5 years and older) in professional healthcare settings.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

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

The provided document does not explicitly list acceptance criteria in a table format with specific quantitative thresholds. Instead, it states that "Performance testing demonstrated that the subject device met its acceptance criteria" and details the standards and guidance documents followed. The acceptance criteria can be inferred from the compliance with these standards and regulatory guidance.

The device's performance is reported as meeting the requirements of:

• ATS/ERS (2005) waveform simulator testing
• ISO 23747:2015 - Anaesthetic and respiratory equipment -- Peak expiratory flow meters
• ISO 26782:2009 - Anaesthetic and respiratory equipment - Spirometers intended for the measurement of time forced expired volumes in humans.

Inferred Acceptance Criteria & Reported Performance:

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

The document describes non-clinical testing involving waveform simulator testing (ATS/ERS) and compliance with ISO standards (ISO 23747, ISO 26782). These are typically bench tests using standardized simulated waveforms, not patient data. Therefore, there is no sample size of human subjects mentioned for the test set, nor is there information on data provenance (country, retrospective/prospective), as the testing appears to be entirely technical/engineering in nature (software V&V, bench testing against standards).

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)

Since the testing involved bench testing against international standards and simulated waveforms, there were no human experts establishing ground truth in the typical sense of clinical interpretation. The "ground truth" for these tests is implicitly defined by the parameters and specifications of the ATS/ERS and ISO standards themselves.

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

As the testing was non-clinical and involved compliance with technical standards and simulated waveforms, there was no adjudication method described or likely needed for human discrepancies. The performance was measured against predefined technical specifications.

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 MRMC comparative effectiveness study was done. The device is a diagnostic spirometer software, not an AI-assisted diagnostic tool that aids human readers in interpreting complex images or data. Its primary function is to acquire, display, store, and print spirometry data, and interface with existing medical devices. The document does not suggest any AI components that perform diagnostic interpretation requiring human-in-the-loop studies.

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

The entire performance evaluation described is essentially a standalone (algorithm/software only) assessment against technical standards and simulator outputs. The V&V of the software itself and its ability to process and display data in compliance with ATS/ERS and ISO standards represents its "standalone performance."

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

The ground truth used for the performance testing was based on international technical standards and simulated waveforms:

• ATS/ERS (2005) waveform simulator
• ISO 23747:2015 specifications
• ISO 26782:2009 specifications

These standards define the expected correct output for various spirometry maneuvers and parameters.

8. The sample size for the training set

This device did not undergo a training phase in the context of machine learning or AI. It is a software application whose functionality is based on established algorithms for spirometry and data management. Therefore, there is no training set sample size described.

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

As there was no training set in the context of machine learning, there is no information on how ground truth for a training set was established. The software's "knowledge" or "rules" are based on the implementation of well-established medical and engineering principles for spirometry and data processing, not on learning from a labeled dataset.

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The Vitalograph Model 2120 In2itive eDiary device is a battery-operated spirometer which measures three basic patient respiratory parameters (FVC, MVV and VC). The Vitalograph Model 2120 In2itive eDiary is a handheld spirometer designed for lung function testing in a variety of environments such as hospital wards, health centres and private homes. It is intended for Adults and pediatrics, 5 years and older.

The Vitalograph Model 2120 In2itive eDiary can be configured as a stand-alone spirometer or connected to a printer.

The Vitalograph Model 2120 In2itive eDiary is a hand-held, battery-operated spirometer which measures the following lung function parameters FVC, MVV and VC in hospital, clinical and home settings.

It can be configured as a standalone spirometer or connected to a printer. 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, VC, ● MVV) and other parameters which are subsets of these measured parameters.
• . Record subject data. Storage of data and test results on unit for later printing or export to Spirotrac software
• The Flowhead utilizes a Fleisch Pneumotachograph.
• User Interface navigation via five buttons (Up, Down, Enter/Select, Cancel/Esc and . Power On/Off) or an optional touch screen.

The Vitalograph Model 2120 In2itive eDiary is a diagnostic spirometer. The device's acceptance criteria and the study proving it meets these criteria are outlined below.

1. Acceptance Criteria and Reported Device Performance:

The acceptance criteria are established by various international standards for spirometry and medical device safety. The reported device performance is compared to these standards and the predicate device (Vitalograph Model 2120).

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

The document does not explicitly state a 'sample size' in terms of patient data for the performance testing of the spirometer itself. The performance testing was primarily bench testing against established standards.

The provenance is implied to be laboratory/bench testing data, not patient data in the context of clinical trials. There is no mention of country of origin for any data or whether it was retrospective or prospective, as it appears to be primarily device performance verification against standards.

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

Not applicable. The ground truth for this device's performance is established by international technical standards (e.g., ATS/ERS, ISO 23747, ISO 26782) for spirometry, and electrical safety standards (e.g., IEC 60601 series). These are objective measurements performed on the device.

4. Adjudication method for the test set:

Not applicable. The performance testing involves objective measurements against predefined technical standards, not subjective interpretations requiring expert 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:

Not applicable. This device is a diagnostic spirometer, not an AI-assisted diagnostic imaging or interpretation tool that would involve "human readers" or AI assistance in that context. The device directly measures respiratory parameters.

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

The performance evaluation described is for the device as a standalone measuring instrument. This is implicitly how the "Bench Testing" for performance (peak flow, timed forced expired volume) was conducted against ISO standards. The product is a diagnostic spirometer that provides direct measurements, so its standalone performance is its primary function.

7. The type of ground truth used:

The ground truth used for performance validation is based on established international technical standards for spirometry (e.g., ATS/ERS, ISO 23747, ISO 26782) for parameters like volume accuracy, flow accuracy, and linearity, and electrical safety and EMC standards (e.g., IEC 60601 series). For biocompatibility, the ground truth is defined by ISO 10993 standards.

8. The sample size for the training set:

Not applicable. This device does not use machine learning or AI that would require a "training set" in the traditional sense. Its core functionality relies on physical measurement principles (Fleisch Pneumotachograph) and established algorithms for calculating spirometry parameters.

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

Not applicable, as no training set for machine learning was used.

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