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Ascent Cardiorespiratory Diagnostic Software is intended to be used for measurements, data collection and analysis of lung function (PFT) parameters, and cardiopulmonary testing (CPET) parameters, aiding in the diagnosis of related conditions.

All the measurements are performed via a mouthpiece or a mask.

The results of the test can be viewed on-line with the help of a computer screen and can be printed after the test. The test results can be saved for further referral or report generation purposes.

For use of the Bronchial Challenge option, the medical director of the laboratory, physician, or person appropriately trained to treat acute bronchoconstriction, including appropriate use of resuscitation equipment, must be close enough to respond quickly to an emergency.

The product can be utilized for patients from 4 years old and older as long as they can cooperate in the performance -- no special limit to patient's sex or height.

Measurements will be performed under the direction of a physician in a hospital environment, physician's office or similar settings.

Ascent™ Cardiorespiratory Diagnostic Software ("Ascent") is a stand-alone software application which can be used with several hardware devices in the Medical Graphics Corporation product line.

The core purpose of the software for measurement, data collection and analysis of testing in patients who may be suffering from pulmonary illnesses like chronic obstructive pulmonary disease (COPD), asthma, exercise intolerance, heart failure and/or cardiorespiratory concerns where diagnosis and prognosis needs to be determined.

In conjunction with diagnostic hardware, Ascent is used to collect data pertaining to the patient's degree of obstruction, lung volumes, and diffusing capacity. It is also used to present the collected lung diagnostic information so that it can be checked for quality and interpreted by a qualified physician, often a pulmonologist or cardiologist.

All the measurements are performed via a mouthpiece or a face mask.

The provided text is a 510(k) summary from the FDA, which outlines the substantial equivalence determination for a medical device. This type of document focuses on comparing a new device to existing legally marketed predicate devices rather than providing detailed acceptance criteria and the results of a specific clinical study with granular performance metrics. As such, the document does not contain the specific information needed to fulfill all aspects of your request, particularly regarding detailed performance metrics, sample sizes for test sets (beyond general validation statements), expert qualifications, ground truth establishment methods for test sets, MRMC studies, or training set details.

However, I can extract the information that is present and highlight what is missing based on your request.

Missing Information:

• Detailed Acceptance Criteria Table with Specific Performance Metrics: The document states that the software was "extensively validated per medical device software standards and guidance" and that "Testing results support that Ascent fulfills its intended use/indications for use." It also mentions "Performance tests included FEV1, MVV, FRC, SVC, DLCO, VA, TGV, VO2, VCO2, and VE." However, it does not provide specific quantitative acceptance criteria (e.g., "FEV1 must be within X% of ground truth") or the reported performance for these metrics.
• Sample Size for Test Set and Data Provenance: The document does not specify the sample size for the test data used for performance validation, nor does it detail the provenance (country, retrospective/prospective) of this data.
• Number of Experts and Qualifications for Ground Truth: The document does not describe how ground truth for the test set was established, including the number or specific qualifications of experts involved.
• Adjudication Method for Test Set: No information is provided regarding adjudication methods (e.g., 2+1, 3+1).
• Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study: The document does not indicate that an MRMC study was performed or provide any effect size for human reader improvement with AI assistance.
• Standalone Performance (Algorithm Only): While the document states "Ascent Cardiorespiratory Diagnostic Software is a stand-alone software application," it describes validation as "Performance validation testing was done with the subject software device and recommended hardware devices working together." It does not provide specific performance metrics for the algorithm only without human interaction in a diagnostic capacity beyond its intended function of measuring, collecting, and analyzing parameters. The software aids diagnosis, implying human interpretation.
• Type of Ground Truth Used (for Test Set): The document implicitly refers to "performance tests" for various physiological parameters (FEV1, DLCO, etc.), suggesting comparison to a reference standard for these measurements. However, it does not explicitly state the nature of this "ground truth" (e.g., expert consensus, pathology, outcome data) beyond reference to ATS/ERS guidelines for standardization.
• Sample Size for Training Set: No information on training data or its size is provided. This is typical for a 510(k) for software that calculates and analyzes data from hardware, rather than an AI/ML model that learns from large datasets.
• How Ground Truth for Training Set was Established: Not applicable as training data details are not provided.

Information that can be extracted from the document:

The provided document is a 510(k) summary for the "Ascent Cardiorespiratory Diagnostic Software" (K242809). It details the device's substantial equivalence to predicate devices, focusing on its intended use, technological characteristics, and conformity to relevant standards and guidelines.

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

As noted above, specific quantitative acceptance criteria and reported performance metrics are NOT provided in this document. The document generally states that "Ascent Cardiorespiratory Diagnostic Software was extensively validated per medical device software standards and guidance. Testing results support that Ascent fulfills its intended use/indications for use..."

It mentions that "Performance tests included FEV1, MVV, FRC, SVC, DLCO, VA, TGV, VO2, VCO2, and VE." However, no numerical results or thresholds are given. The validation was done referencing the following guidelines/standards for "acceptability and repeatability":

• ATS/ ERS Standardisation of Spirometry (2019)
• ERS/ ATS Standardisation of the Measurements of Lung Volumes (2023)
• 2017 ERS/ ATS Standards for Single-Breath Carbon Monoxide Uptake in the Lung
• ERS Technical Standard on Bronchial Challenge Testing (2017)
• 2017 ATS Guidelines for a Standardized PF Report
• ATS/ ACCP Statement on Cardiopulmonary Exercise Testing (2003)

Summary of available information for a table format (conceptual, as specific numerical data is missing):

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

• Sample Size: Not specified. The document only states "Software validation testing involved system level tests, performance tests and safety testing based on hazard analysis. Performance validation testing was done with the subject software device and recommended hardware devices working together."
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

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

• This information is not provided in the document.

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

• This information is not provided in the document.

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 study is mentioned or implied. The software is described as
  "aiding in the diagnosis of related conditions" and presenting "diagnostic information so that it can be checked for quality and interpreted by a qualified physician." This device is a "Predictive Pulmonary-Function Value Calculator" and performs measurements and analysis, but it is not described as an AI system assisting human readers in image interpretation or diagnosis in a comparative effectiveness study context.

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

• The device is indeed described as a "stand-alone software application." However, the performance validation was done "with the subject software device and recommended hardware devices working together." Its output (measurements and analysis) is intended to be "displayed to the user" and "interpreted by a qualified physician."
• The document does not provide performance metrics for the algorithm only in a way that suggests a diagnostic output without human interpretation or hardware interaction. It's a software that processes data from hardware to provide measurements and analysis, not, for example, a diagnostic image analysis AI.

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

• The document implies that the ground truth for the performance tests (FEV1, DLCO, etc.) would be established by the standardized measurement techniques defined by governing bodies like ATS/ERS/ACCP. These typically involve comparing the device's calculated values against accepted reference methods for deriving those physiological parameters, often involving highly calibrated equipment and expert technicians following strict protocols. However, the exact nature of this "ground truth" (e.g., what gold standard was used for comparison) is not explicitly detailed beyond referencing the standards themselves.

8. The sample size for the training set:

• This information is not provided in the document. This type of device (a calculator/analyzer) is typically engineered based on established physiological formulas and algorithms, rather than being "trained" on large datasets in the way a deep learning AI model would be.

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

• Not applicable, as training set details are not provided and the device functions as a calculator based on established science, not a machine learning model that learns from a labeled training set.

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The SpiroSphere is a compact device inspiratory and expiratory lung function parameters in adults and children aged 4 years and older. It can be used by physicians in the office or hospital.

With the option ECG, electrocardio measurements can be made under resting conditions. For this purpose a 12channel surface electrocardiogram can be measured and recorded. The acquired ECG can be displayed on the screen or printed on paper. The interpretation software is intended to support the physician in evaluation of the resting ECG in terms of morphology and rhythm. Automatic interpretation of the ECG is not possible for pediatric subjects with an age below 16 years and for pacemaker subjects. It is not intended for intra-cardial use.

A qualified physician has to reassess all ECG measurements. An interpretation by SpiroSphere ECG is only significant if it is considered in connection with other clinical findings. ECG interpretation statements made by the SpiroSphere ECG represent partial qualitative and quantitative information on the patient's cardiovascular condition and no therapy or drugs can be administered based solely on the interpretation statements. The SpiroSphere ECG is not intended for use in an EMS environment (Emergency Medical Services Environment).

The minimum age for ECG application is 4 years.

It can be used by physicians in the office or hospital.

With the CardioSphere, electrocardio measurements can be made under resting conditions. For this purpose a 12channel surface electrocardiogram can be measured and recorded. The acquired ECG can be displayed on the screen or printed on paper. The interpretation software is intended to support the physician in evaluation of the resting ECG in terms of morphology and rhythm. Automatic interpretation of the ECG is not possible for pediatric subjects with an age below 16 years and for pacemaker subjects. It is not intended for intra-cardial use.

A qualified physician has to reasurements. An interpretation by CardioSphere is only significant if it is considered in connection with other clinical findings. ECG interpretation statements made by the CardioSphere represent partial qualitative and quantitative information on the patient's cardition and no therapy or drugs can be administered based solely on the interpretation statements. The CardioSphere is not intended for use in an EMS environment (Emergency Medical Services Environment).

The minimum age for ECG application is 4 years.

It can be used by physicians in the office or hospital.

SpiroSphere is a compact spirometry device. Its Sensor Unit is batterypowered. The Main Unit can be powered by battery or power supply. The SpiroSphere / SpiroSphere ECG is used to measure inspiratory and expiratory lung function parameters in adults and children 4 years and older. The measured data is saved to the device and can be read out at any time.

SpiroSphere ECG is a compact spirometry device. Its Sensor Unit is battery-powered. The Main Unit can be powered by battery or power supply. The SpiroSphere / SpiroSphere ECG is used to measure inspiratory and expiratory lung function parameters in adults and children 4 years and older. The measured data is saved to the device and can be read out at any time.

With the ECG option (subject of this 510(k)), 12-channel surface electrocardiogram can be measured and recorded.

The Main Unit can be powered by battery or power supply. The Main Unit is wirelessly connected to an ECG amplifier via Bluetooth.

With the ECG option (subject of this 510(k)), 12-channel surface electrocardiogram can be measured and recorded.

A printer can be connected and data (e.g. reports, screenshots) can be printed. Moreover, it is possible to transfer data by USB. Wifi. 3G. and Ethernet connections.

Pulmonary function assessments

• Slow Spirometry .
• Forced Spirometry .
• Flow-Volume loop and Volume-Time tracing, pre/post tests ●
• Trending capabilities ●

Cardiovascular assessments

• 12 Lead Electrocardiogram ●

The provided text does not contain information about acceptance criteria and the study that proves the device meets the acceptance criteria. The document is a 510(k) premarket notification letter from the FDA, along with a device description and comparison tables to predicate devices. It discusses general information about the device, its intended use, technological characteristics, and a summary of device testing (e.g., software verification, risk analysis, electrical safety, EMC, human factors, cybersecurity, reprocessing validation). However, it does not specify quantitative acceptance criteria for performance metrics (such as sensitivity, specificity, accuracy) for its interpretation software, nor does it detail a specific study proving these criteria were met.

Therefore, I cannot provide the requested information based on the given text.

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The Pulmonary Function Tester (Model: A9) is intended to be used for measurement and data collection of lung function parameters. The system performs cooperation-dependent flow-volume measurements shall be performed under the direction of a physician in the clinic, doctors' office or hospital. The Pulmonary Function Tester (Model: A9) is intended for use in patients from 4 years of age and older who can understand and perform instructions of the physician.

The Pulmonary Function Tester (Model: A9) is a hand-held pulmonary function testing device which can be used by patients older than 4 years old under the direction of a physician in the clinic, doctors' office or hospital. The Pulmonary Function Tester (Model: A9) is comprised of the main unit (display screen, sensor and communication module), USB cable, application software (computer software, mobile software), and a power supply. The device use is very simple, it adopts mouth blowing method. The device uses a flow sensor to measure the air flow and volume of the patient's exhaled or inhaled air. According to the volume-time curve and the flow-volume curve, the pulmonary ventilation indicators of human respiratory physiology, such as slow vital capacity, maximum minute ventilation and forced vital capacity, are estimated and reported on the LCD screen and computer/mobile software. The pulmonary Function Tester is powered by lithium battery (DC 3.7V, 1500mAh) or tablet/computer USB port, or the equipped power supply.

The provided text is a 510(k) Summary for a Pulmonary Function Tester. It describes the device, its intended use, and its substantial equivalence to a predicate device. However, it does not contain the detailed acceptance criteria for specific performance metrics nor the results of a specific clinical study used to prove the device meets those criteria.

Instead, the document states that "Non-clinical tests were conducted to verify that the Pulmonary Function Tester (Model: A9) meets all design specifications which supports the conclusion that it's Substantially Equivalent (SE) to the predicate device." It then lists the standards the device complies with. While these standards define acceptable performance ranges and test methods, the actual acceptance criteria and the device's reported performance against these criteria for each specific lung function parameter are not explicitly detailed in a table or narrative form in the provided text.

Therefore, I cannot populate the requested table and answer questions 2-9 with specific information from the provided document, as that level of detail is not present. The document focuses on demonstrating compliance with recognized standards rather than presenting a detailed study report of the device's performance against specific, pre-defined acceptance criteria.

Key Missing Information for Your Request:

• Specific Acceptance Criteria: While compliance with standards like ISO 26782 and ISO 23747 implies certain performance expectations, the document does not list explicit numerical acceptance criteria for parameters like FVC accuracy, FEV1 accuracy, PEF accuracy, etc., for the subject device itself.
• Reported Device Performance: Relatedly, the document does not provide a table or narrative of the subject device's measured performance (e.g., "FVC accuracy achieved X%") against these (unspecified) acceptance criteria.
• Detailed Study Design: There is no description of a clinical or non-clinical study with details about sample size, ground truth establishment, expert involvement, or adjudication methods. The "non-clinical tests" are mentioned as being conducted to verify design specifications and compliance with standards, but the specifics of these tests are not provided beyond the standards themselves.

Based on the provided text, here is what can and cannot be stated:

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

Cannot be fully provided as the specific acceptance criteria and reported device performance (e.g., FVC accuracy = X%, PEF accuracy = Y%) are not explicitly stated in a table or narrative form in the provided text.

The document references standards that contain performance requirements. For example, under "Flow (PEF) Accuracy" it states for the subject device: "<============================================================================================================================================================= Similar, conforms to ISO 23747". For "Volume (FEV1, FEV6 and FVC) Accuracy" it states for the subject device: "< ± 2.5% Similar, conforms to ATS 2019 and ISO 26782."

This suggests that the acceptance criteria for these parameters are derived from the referenced standards, and the device's performance is stated to conform to these standards, but the exact numerical reading from a specific test showing "X% accuracy was achieved" is missing.

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

Not available in the provided text. The document mentions "Non-clinical tests were conducted," but offers no details about sample size (if applicable to non-clinical tests in this context, which often involves equipment calibration/verification rather than biological samples), data provenance, or study design.

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/Not available in the provided text. The document describes non-clinical tests for device performance verification against standards. There is no mention of human experts establishing ground truth for a test set, as would be common in diagnostic AI studies.

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

Not applicable/Not available in the provided text. As there's no mention of a test set with human-established ground truth, an adjudication method is irrelevant here.

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. This device is a "Pulmonary Function Tester" (a medical device for measuring lung function), not an AI-powered diagnostic imaging tool or a system designed to assist human readers. Therefore, an MRMC comparative effectiveness study involving AI assistance for human readers is not relevant to this device's function and is not mentioned.

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

Yes, in essence. The "non-clinical tests" described are essentially standalone performance evaluations of the device's ability to measure physiological parameters according to engineering specifications and international standards like ISO 26782 and ISO 23747. The device itself performs the measurements and calculates the parameters. Performance is assessed on its ability to accurately and precisely perform these measurements, not on an algorithm's diagnostic output.

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

For the "non-clinical tests," the ground truth would be established by reference standards and calibrated instruments used to generate known flow and volume inputs for the device. For example, precisely controlled flow generators and volume syringes would be used, and the device's measurements would be compared against these known inputs to determine accuracy. The specific methods are implied by the referenced ISO standards.

8. The sample size for the training set

Not applicable/Not available in the provided text. This device is a hardware-based pulmonary function tester with associated software; it is not presented as an AI/ML device requiring a training set in the conventional sense of machine learning for pattern recognition or prediction. Its fundamental operation relies on physical principles of flow and volume measurement described under "Technology."

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

Not applicable/Not available in the provided text. See answer to #8.

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The Virtus Metabolic Monitor is indicated for the measurement of Resting Energy Expenditure (REE) for mechanically ventilated patients, who are non-spontaneous breathing, and who are at least 18 years of age.

The Virtus Metabolic Monitor is intended to be used in Intensive Care Units (ICUs) in professional healthcare facilities only.

The Virtus Metabolic Monitor is for prescription use only.

Virtus Metabolic Monitor is a metabolic monitors designed for the measurement of resting energy expenditure (REE) for mechanically ventilated adult patients in the Intensive Care Unit (ICU).

The device is enclosed in a metal casing with a color touch screen on front for user interaction and measurement results. On the left-hand side pneumatic connections to the flow and gas sensor (Flow Sensor) and on the rear the mains power inlet, on-switch, charging indicator and USB-connector for export of results.

The Virtus Metabolic Monitor is portable and is supplied from the built-in battery or from mains, where it is automatically charged.

Here's a breakdown of the acceptance criteria and study information for the Virtus Metabolic Monitor, based on the provided text:

1. Acceptance Criteria and Reported Device Performance

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

The document describes a "side-by-side" test against the predicate device. However, the specific sample size for this test set (number of measurements, number of patients) is not provided. The data provenance (country of origin, retrospective/prospective) is also not explicitly stated. It's implied to be a testing scenario rather than a clinical study on patients, given the wording.

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

The document does not mention the use of experts to establish ground truth for this non-clinical, side-by-side comparison test. The comparison was made against the measurements of a cleared predicate device (Cosmed Q-NRG+).

4. Adjudication Method for the Test Set

Since the ground truth was established by comparison to a predicate device's measurements in a laboratory setting, there was no expert adjudication method described.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was Done

No, an MRMC comparative effectiveness study was not done. The described testing is a non-clinical side-by-side comparison of device measurements, not a study involving human readers or their improvement with AI assistance.

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

Yes, the described testing is a standalone performance evaluation of the Virtus Metabolic Monitor. It directly compares the device's measurements (V'O2, V'CO2, REE) against those of a predicate device in a controlled setting, without a human-in-the-loop context.

7. The Type of Ground Truth Used

The "ground truth" for the non-clinical side-by-side test was the measurements provided by the predicate device (Cosmed Q-NRG+). The study aimed to show "acceptable agreement of measurements between the subject device and the predicate device."

8. The Sample Size for the Training Set

The document does not mention any training set or related sample size. The device calculates metabolic parameters using the standard Weir formula and relies on measured V'O2 and V'CO2. There's no indication of a machine learning-based algorithm requiring a training set in this summary.

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

As no training set is mentioned or implied, the method for establishing its ground truth is not applicable.

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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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MasterScope WSSU is a medical device to measure inspiratory and expiratory lung function parameters. With the option ECG a 12-channel surface electrocardiogram (ECG) can be measured and recorded. It is not intended for intracardial use. Automatic interpretation of the ECG is not possible for pediatric patients with an age below 16 years and for pacemaker patients.

A qualified physician has to reassess all MasterScope/MasterScope ECG measurements. An interpretation by MasterScope/MasterScope ECG is only significant if it is considered in connection with other clinical findings. ECG interpretation statements made by the MasterScope/MasterScope ECG represent partial quantitative information on the patient's cardiovascular conditions and no therapy or drugs can be administered based solely on the interpretation statements.

It can be used by physicians in the office or hospital.

The MasterScope spirometry and ECG application is intended to measure adults and children aged 4 years and older. The patients must be able to understand and perform instructions of the physician.

The MasterScope is a portable device, which can collect spirometry and ECG data.

With the option Spirometry, inspiratory and expiratory lung function measurements can be performed with a wired sensor (Digital Handle USB) or wireless spirometry sensor unit (WSSU Bluetooth). Both spirometry sensors work with a pneumotach (Lilly Type Pneumotachograph).

The Wireless Spirometry Sensor Unit is battery-powered and can be charged with a dedicated charging station.

With the option ECG, a 12-channel surface electrocardiogram (ECG) can be measured and recorded. It is not intended for intracardial use.

The interpretation software is intended to support the physician in evaluation the ECG in terms of morphology and rhythm.

The MasterScope software allows protocol-driven workflows and can be customized for use in clinical trials (e.g. individual access rights).

MasterScope provides automated and secure data transmission to a centralized data base.

The measured data is saved into the MasterScope software and can be read out at any time.

A printer can be connected with the notebook and all needed data can be printed. Moreover it is possible to transfer data by USB, Wifi, and Ethernet.

The MasterScope WSSU is powered from 100 - 240V / 50 - 60Hz wall outlets. No energy is transferred to the patient.

The provided text describes the MasterScope WSSU device and its substantial equivalence to predicate devices, focusing on spirometry and ECG functions. The information relevant to acceptance criteria and study proving device performance is primarily found in the "Summary Table of Comparison" (pages 7-8) and "Summary of Device Testing" (page 12).

Here's an analysis of the provided information against the requested points:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance:

The document provides performance specifications for the pulmonary function measurements, which serve as acceptance criteria.

Pulmonary Function Performance Comparison

Note: The MasterScope WSSU is stated to have identical performance specifications to the reference device (SpiroSphere K173937), meaning it meets the established acceptance criteria. The document explicitly states "The Sensor Unit used in MasterScope WSSU device is cleared under SpiroSphere (K173937). The Sensor Unit is supplied in its final finished form and is identical to the reference predicate device." and lists the exact same performance specifications for the MasterScope WSSU as for SpiroSphere in the comparison table.

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

The document does not explicitly state the sample size used for the test set in the context of clinical performance evaluation (e.g., number of patients or spirometry maneuvers). Instead, it refers to

• "Tests were performed to confirm that the MasterScope WSSU meets the recommendations for accuracy and precision for Spirometry of the American Thoracic Society (ATS) according to ATS/ERS standards 2005." (page 12)
• "The Sensor Unit used in MasterScope WSSU device is cleared under SpiroSphere (K173937). The Sensor Unit is supplied in its final finished form and is identical to the reference predicate device." (page 7)

This suggests that the performance evaluation relies heavily on the prior clearance of the identical sensor unit within the SpiroSphere device, which would have undergone its own testing.
The document does not specify the country of origin of data or whether it was retrospective or prospective for the current submission's testing. It seems to leverage existing data/clearance.

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

This information is not provided in the document. The spirometry performance is based on ATS/ERS standards, which are objective technical standards rather than expert consensus on individual cases. For the ECG interpretation, the device uses the "Hannover ECG system (HES)", but the document notes: "A qualified physician has to reassess all MasterScope/MasterScope ECG measurements. An interpretation by MasterScope/MasterScope ECG is only significant if it is considered in connection with other clinical findings. ECG interpretation statements made by the MasterScope/MasterScope ECG represent partial quantitative information on the patient's cardiovascular conditions and no therapy or drugs can be administered based solely on the interpretation statements." This indicates that the device's ECG interpretation is supportive, not definitive, and requires physician oversight. Therefore, direct expert ground truth establishment for a test set, as might be done for an AI diagnostic device, is not thoroughly described here.

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

This information is not provided. Given the nature of objective performance testing against standards rather than subjective human interpretation, a formal adjudication method as typically seen in image-based AI studies (e.g., 2+1 radiologist reads) is generally not applicable or described for this type of device.

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:

A multi-reader multi-case (MRMC) comparative effectiveness study involving human readers with and without AI assistance was not mentioned or described. This device appears to be a measurement tool with an interpretation assist (for ECG), not a primary AI diagnostic tool where human-AI interaction is the core subject of evaluation. The primary focus of the submission is demonstrating substantial equivalence through technical performance and component identity.

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

The device's performance specifications (Accuracy and Resolution) for spirometry are for the device itself, implying standalone performance. For ECG, the device uses the "Hannover ECG system (HES)" which provides "interpretation statements." However, the text explicitly states that "Automatic interpretation of the ECG is not possible for pediatric patients with an age below 16 years and for pacemaker patients," and "A qualified physician has to reassess all MasterScope/MasterScope ECG measurements." This suggests that even for ECG, the algorithm's output is not intended to be "standalone" without human-in-the-loop, emphasizing a supportive role. The technical performance metrics listed for spirometry are inherent to the device's measurement capabilities.

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

For spirometry, the ground truth is against ATS/ERS standards 2005 for accuracy and precision. These are widely accepted objective technical standards for pulmonary function testing equipment.
For ECG, the "Hannover ECG system (HES)" provides interpretation statements. The implicit ground truth for such systems would typically involve a large dataset of ECGs with confirmed diagnoses (often by expert cardiologists or based on outcomes), but the document does not detail how HES's accuracy was validated or what constituted its ground truth in this submission. The submission focuses on the HES being part of the predicate device.

8. The sample size for the training set:

The document does not provide information regarding the sample size for a training set. This is not a typical AI/ML device where specific training datasets are detailed in this manner. The device is primarily a measurement instrument, and its software (including HES) is presumably developed based on established algorithms rather than contemporary machine learning models that require large labeled training sets as a distinct part of the clearance process for new devices.

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

Since no specific "training set" and its size are mentioned, the method for establishing its ground truth is also not provided. The device is categorized as a "Predictive pulmonary-function value calculator" but operates on established physiological measurement principles and computations. The ECG interpretation component (HES) is a "black box" in terms of its internal development and training, leveraging its prior clearance within the predicate device.

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The Q-NRG & Q-NRG+ Portable Metabolic Monitors are indicated for the measurement of Resting Energy Expenditure (REE) for spontaneously breathing and (Q-NRG+ only) ventilated patients, within the following populations:

• Spontaneously breathing subjects >15 kg (33 lb) when using a canopy;

• Spontaneously breathing subjects age >6 and > 10 kg (22 lb) when using a face mask;

• Ventilated subjects age >10 and >10 kg (22 1b).

The Q-NRG & Q-NRG+ Portable Metabolic Monitors are intended to be used in professional healthcare facilities only (Iimited to ICUs for ventilated patients).

The Q-NRG and Q-NRG+ devices are Portable Metabolic Monitors, designed for the measurement of resting energy expenditure (REE) in both spontaneously breathing and mechanically ventilated patients.

The provided text describes the Cosmed Q-NRG & Q-NRG+ Portable Metabolic Monitors, a medical device for measuring Resting Energy Expenditure (REE). The document highlights the device's technical specifications and comparisons to a predicate device (Quark RMR Metabolic Cart) to establish substantial equivalence.

However, the provided text does not contain the specific information required to answer your questions regarding acceptance criteria and the study proving the device meets those criteria in the context of an AI/human comparative effectiveness study. The document focuses on regulatory clearance and device performance and safety against general medical device standards.

Here's a breakdown of why the information you're looking for is not present:

• No mention of AI/machine learning: The device is described as a "Portable Metabolic Monitor," suggesting direct measurement or calculation based on sensor data, not an AI or machine learning algorithm requiring human-in-the-loop performance studies.
• No stated acceptance criteria for an AI algorithm: The "Accuracy Validation" section mentions internal validation protocols for mask, canopy, and ventilator measurement accuracy, with specific percentages for Ventilation, VO2, and VCO2 (e.g., Ventilation: <2% or 100 ml/min). These are likely the device's performance specifications, but not "acceptance criteria" in the context of an AI's performance against a specific clinical task.
• No details on a "test set" for an AI model: The accuracy validation refers to internal protocols, but not a defined test set for evaluating an AI model.
• No information on experts for ground truth, adjudication, or MRMC studies: These elements are specific to AI performance validation, which is not described for this device.
• No mention of training set size or ground truth for training set: Again, these are AI-specific details that are not in the document.

Based on the provided text, I can only provide the device's stated performance specifications, not acceptance criteria for an AI model or details of an AI performance study.

Here's the closest information I can extract regarding performance:

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

(Note: These are performance specifications, not explicitly "acceptance criteria" in the AI validation sense. The document states accuracy validation was done "according to internal validation protocol," implying these are the targets the device met.)

The following information cannot be found in the provided text:

• Sample size used for the test set and the data provenance.
• Number of experts used to establish the ground truth for the test set and their qualifications.
• Adjudication method for the test set.
• If a multi-reader multi-case (MRMC) comparative effectiveness study was done, or the effect size of human readers improving with AI vs. without AI assistance.
• If a standalone (algorithm only without human-in-the-loop performance) was done.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc.) for an AI model.
• The sample size for the training set.
• How the ground truth for the training set was established.

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Ascent Cardiorespiratory Diagnostic Software is intended to be used for measurements, data collection and analysis of lung function (PFT) parameters, aiding in the diagnosis of related conditions. All the measurements are performed via a mouthpiece or a mask. The test can be viewed on-line with the help of a computer screen and can be printed after the test results can be saved for further referral or report generation purposes.

For use of the Bronchial Challenge option, the medical director of the laboratory, physician, or person appropriately trained to treat acute bronchoconstriction, including appropriate use of resuscitation equipment, must be close enough to respond quickly to an emergency.

The product can be utilized for patients from 4 years old and older as long as they can cooperate in the performance -- no special limit to patient's sex or height.

Measurements will be performed under the direction of a physician in a hospital environment, physician's office or similar settings.

In conjunction with pulmonary diagnostic hardware, Ascent software, a Windows desktop application developed using Microsoft's NET Framework, is used to collect data pertaining to the patient's degree of obstruction, lung volumes, and diffusing capacity. It is also used to present the collected lung diagnostic information so that it can be checked for quality and interpreted by a qualified physician, usually a pulmonologist.

The core purpose of the software is to facilitate pulmonary function testing in patients who may be suffering from pulmonary illnesses like Chronic Obstructive Pulmonary Disease and asthma. The software will interact with connected Medical Graphics Corporation diagnostic devices to perform the desired pulmonary function tests on the patient by a professional pulmonary function technologist. The application will display both actual and derived test variables to the user after a test is performed for purposes of data review.

Here's a breakdown of the acceptance criteria and study information for the Ascent Cardiorespiratory Diagnostic Software, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly list "acceptance criteria" in a typical quantitative pass/fail format within a table. Instead, it describes general validation and performance testing. However, we can infer the performance objectives and how they were met.

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

• Sample Size for Test Set: 38 test sessions.
• Data Provenance: The subjects for these test sessions were "from seven to sixty-three years old." The document does not specify the country of origin of the data or whether it was retrospective or prospective.

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

The document does not specify the number of experts or their qualifications used to establish ground truth for the test set. It mentions that the software presents data to a "qualified physician, usually a pulmonologist, for diagnostic interpretation," but this refers to the intended use, not the ground truth establishment for the validation study itself.

4. Adjudication Method for the Test Set:

The document does not describe any specific adjudication method (e.g., 2+1, 3+1). The "physiologic performance validation testing" implies direct measurement comparison rather than expert consensus on interpretive tasks.

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

No, an MRMC comparative effectiveness study was not explicitly mentioned or described as being performed. The study focused on the performance of the software and integrated hardware, not on the impact of the software on human reader performance.

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

Yes, the "physiologic performance validation testing" and "software validation testing" described seem to represent a standalone assessment of the device's ability to measure and process pulmonary function parameters. The device's core function is data collection and analysis, which occurs without direct human interpretation assistance during the measurement phase. The overall intention is for a "qualified physician" to interpret the results generated by the algorithm.

7. The Type of Ground Truth Used:

The document doesn't explicitly state "ground truth" in the context of reference standards. However, the nature of the device (pulmonary function software) implies that the ground truth for "physiologic performance validation testing" would be the physical measurements obtained from the connected hardware devices for various pulmonary function parameters (e.g., airflow, volume, gas concentrations). The accuracy of these measurements would be compared against expected values or highly accurate reference standards, though these specific reference standards are not detailed in the provided text.

8. The Sample Size for the Training Set:

The document does not mention any training set sample size or details about a training set. This is likely because the device, as described, is not an AI/machine learning model that typically requires a discrete training phase with labeled data. It appears to be a software system for data acquisition, processing, and display based on established physiological algorithms and hardware interaction.

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

As no training set is discussed, there is no information on how its ground truth might have been established.

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The SpiroSphere is a compact device to measure inspiratory and expiratory lung function parameters in adults and children aged 4 years and older.

It can be used by physicians in the office or hospital.

The SpiroSphere is a compact spirometry device. It's Sensor Unit is battery-powered. The Main Unit can be powered by battery or power supply. The SpiroSphere is used to measure inspiratory and expiratory lung function parameters in adults and children 4 years and older. The measured data is saved into the device and can be read out at any time.

A printer can be connected with the SpiroSphere and all needed data (e.g. reports, Screenshots) can be printed. Moreover it is possible to transfer data by USB, Wifi, 3G, and Ethernet.

Pulmonary functions

• Slow Spirometry
• Forced Spirometry
• Flow-Volume loop and Volume-Time tracing, pre/post tests
• Trending capabilities

The provided text describes the SpiroSphere, a device for measuring lung function, and its substantial equivalence to a predicate device (SpiroPro). However, it does not contain the detailed information required to answer your specific questions about acceptance criteria and a study proving the device meets them.

The document is a 510(k) summary for premarket notification to the FDA. It focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than providing the detailed clinical or performance study results you're asking for.

Here's a breakdown of why the requested information is absent:

• Acceptance Criteria and Reported Device Performance (Table): The document states adherence to ATS/ERS standards (2005) for spirometry accuracy and precision and lists various parameters measured (FEV1, FVC, PEF, etc.). However, it does not provide a specific table of acceptance criteria (e.g., +/- x% accuracy) or numerical reported device performance against those criteria. It only generally states that "Tests were performed to confirm that the SpiroSphere meets the recommendations for accuracy and precision for Spirometry of the American Thoracic Society (ATS) according to ATS/ERS standards 2005."
• Sample Size, Data Provenance, Ground Truth Experts, Adjudication Method, MRMC Study, Standalone Performance, Training Set Sample Size/Ground Truth: These details are typically found in a dedicated clinical or performance study report, which is not included in this summary. The 510(k) summary only mentions general verification and validation activities and conformance to standards.

In summary, based only on the provided text, I cannot complete the table or answer the specific questions about the study details because that level of detail is not present in this FDA 510(k) summary.

The document discusses:

• Device Name: SpiroSphere
• Predicate Device: SpiroPro (K000648) and SpiroPro with BT (K092324)
• Intended Use/Indications for Use: To measure inspiratory and expiratory lung function parameters in adults and children aged 4 years and older, usable by physicians in office or hospital.
• Technological Characteristics: Uses a pneumotachograph (Lilly Type) for pressure to flow conversion, measures various parameters (FEV1, FVC, PEF, FEF25-75, VC, IC, ERV) conforming to ATS/ERS Spirometry Standards (2005).
• Testing Mentioned: Software verification and validation (IEC 62304), Risk analysis (ISO 14971), electrical safety (IEC 60601-1:2012), EMC (IEC 60601-1-2:2014), wireless technology (FDA Guidance 2013), Human Factors/Usability (IEC 62366), Biocompatibility (ISO 10993-1:2009, ISO 18562:2017), Cybersecurity (FDA Guidance 2014), Reprocessing (FDA Guidance 2015).

To answer your questions thoroughly, a complete performance study report would be required, which typically contains the specific data and methodologies.

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EasyOne Pro/LAB is designed for conducting lung function measurements in general or specialist practices or in hospitals.

EasyOne Pro/LAB can also be used in clinical settings in occupational medicine for performing lung function screenings or measurements.

EasyOne Pro/LAB is used to conduct lung function measurements on adults and children starting at age 4, except measurements of diffusing capacity of the lung based on CO (DLCO), which can be performed on adults and children starting at age 6.

The EasyOne Pro Respiratory Analysis System consists of two different device models: EasyOne Pro and EasyOne Pro LAB.

The EasyOne Pro Respiratory Analysis System devices are pulmonary function testing devices. Both EasyOne Pro Respiratory Analysis System device models provide Spirometry and Single Breath CO Diffusion (DLCO) tests including Lung Volume Parameters. The device model EasyOne Pro LAB additionally provides the Nitrogen Multiple Breath Washout (MBW) method. The EasyOne Pro Respiratory Analysis System devices meet the ATS recommendations for accuracy and precision for Spirometry, DLCO and MBW tests.

The EasyOne Pro Respiratory Analysis System devices can be used as a stand-alone system and can be connected to a network.

The EasyOne Pro Respiratory Analysis System devices use the following sensors: An ultrasonic flow sensor to measure flow velocity, volume and molar mass of the gases that the patient inhales and exhales; a molar mass sensor which determines the helium content of the respired air for the DLCO test and the nitrogen concentration for the multiple-breath washout (MBW) test; a CO sensor (EasyOne Pro) or a combined CO/CO₂ sensor (EasyOne Pro LAB) to determine CO and CO2 content in breathing gas; a temperature and humidity sensor to collect environmental data.

The EasyOne Pro Respiratory Analysis System devices are used in combination with test gases (DLCO gas for DLCO tests and O2 for MBW tests).

The EasyOne Pro Respiratory Analysis System devices are used in combination with the single-patient use breathing tube ndd Spirette. For DLCO and MBW tests, the additional single-patient use accessories, the ndd DLCO or FRC Barriettes, are used.

The single-patient use accessories prevent cross-contamination between patients. The Spirette prevents the passage of microorganisms into the inside of the flow sensor; the Barriettes prevent the passage of microorganisms into gas supply tubing.

The provided document is a 510(k) premarket notification for the EasyOne Pro Respiratory Analysis System. It describes the device, its intended use, and compares it to predicate devices to demonstrate substantial equivalence. The document summarizes the testing performed to ensure the modified device meets performance and safety standards.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document states that the EasyOne Pro Respiratory Analysis System devices "meet the ATS recommendations for accuracy and precision for Spirometry, DLCO and MBW tests." While specific numerical acceptance criteria (e.g., ±X% accuracy) for each parameter are not explicitly listed in the provided text, the general acceptance criterion is compliance with ATS recommendations.

Note: The document states that "For the modified devices, some of the labelled specifications were updated to reflect the technical capabilities. No changes were made to the components the specifications relate to." This implies there might be specific numerical specifications in other documentation not provided here.

2. Sample Size and Data Provenance for Test Set

• Sample Size: The document mentions "testing with humans" for Spirometry, DLCO, and MBW, but does not specify the numerical sample size for the human test set. It also mentions "Simulated Spirometry, DLCO and MBW testing."
• Data Provenance: Not specified in the provided text (e.g., country of origin, retrospective or prospective).

3. Number of Experts and Qualifications for Ground Truth (Test Set)

Not applicable / Not specified in the provided text. This device is a measurement system and its "ground truth" would generally be established by calibrated reference standards and established physiological measurements, rather than expert consensus on interpretive tasks.

4. Adjudication Method for Test Set

Not applicable / Not specified in the provided text. As noted above, the "ground truth" for this type of device (a physiological measurement system) is based on reference standards and calibrated instruments, not on human interpretation or adjudication.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, an MRMC comparative effectiveness study was not done. This type of study assesses how human readers improve with AI assistance, which is not relevant for a device that measures lung function clinically. The EasyOne Pro is a diagnostic measurement device, not an AI-based interpretive aid for human readers.

6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance

Yes, the testing described appears to be a standalone performance evaluation of the device's measurement capabilities. The "Simulated Spirometry, DLCO and MBW testing" would assess the algorithm and hardware performance independently, and the "testing with humans" would validate its clinical measurement accuracy against the ATS recommendations. The device itself is designed to provide direct physiological measurements.

7. Type of Ground Truth Used

The ground truth for the EasyOne Pro Respiratory Analysis System is established by technical standards and physiological recommendations, specifically the "ATS recommendations for accuracy and precision for Spirometry, DLCO and MBW tests." This would involve:

• Calibrated reference standards: For simulated testing (e.g., known flow and volume signals, known gas concentrations).
• Established physiological measurement methods: When testing with human subjects, the device's measurements would be compared against expected values or other validated methods to confirm compliance with ATS standards.

8. Sample Size for Training Set

Not applicable / Not specified. This device is a measurement system, not a machine learning algorithm that requires a "training set" in the conventional sense of AI models. Its underlying principles are based on known physics and physiological models, not data-driven learning from previous cases.

9. How Ground Truth for Training Set Was Established

Not applicable. As explained above, the device does not use a "training set" in the context of an AI/ML model. Its design and calibration are based on established scientific principles and engineering practices to meet specific performance standards.

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