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LTV2 model 2200 and 2150 ventilators are intended to provide continuous or intermittent ventilator support for the care of the individuals who require mechanical ventilation. The use environment is for institutional use. Institutional use includes ICU or other hospital environments including intra-hospital transport. The model 2200 can operate with high pressure O2. The model 2150 operates with low pressure oxygen.

TheLTV2 Series Ventilators are intended to provide continuous or intermittent ventilatory support for the care of the individuals who require mechanical ventilation. The ventilator is a restricted medical device intended for use by qualified, trained personnel under the direction of a physician. Specifically, the ventilator is applicable for adult and pediatric patients weighing at least 10 kg (22 lbs.), who require the following types of ventilatory support:

• . Positive Pressure Ventilation, delivered invasively or non-invasively (via mask).
• . Assist/Control, SIMV, CPAP, and NPPV modes of ventilation.
  The ventilator is suitable for use in professional healthcare facilities, including during intrahospital transport.

The LTV2 Series ventilator supports adult and pediatric patients weighing at least 10 kg (22 lb) in professional healthcare facilities response with invasive or noninvasive ventilation presets. These settings can be easily refined using the touch-turn-touch interface on the LED display. The ventilator also provides a wide range of ventilation therapies to meet demanding patient needs, including volume control, pressure control, pressure support and spontaneous breath types. Combined with the spontaneous breathing trial function, the ultra-sensitive flow trigger facilitates weaning patients weighing at least 10 kg (22lb).

The provided document is an FDA 510(k) premarket notification for the LTV2 Series Ventilators. This type of submission focuses on demonstrating substantial equivalence to legally marketed predicate devices, rather than proving the device meets specific acceptance criteria through a study with a test set, expert ground truth, and statistical analysis as would be done for a novel AI/ML device.

Therefore, the information typically requested in your prompt regarding acceptance criteria, study design for performance metrics (like sensitivity, specificity, F1 score), sample sizes for test and training sets, expert adjudication, MRMC studies, and ground truth establishment is not present in this document because it's not relevant to a 510(k) submission for a non-AI/ML medical device like a ventilator.

The document describes the device's technical specifications and compares them to predicate devices, along with listing applicable performance standards and verification testing (e.g., waveform, alarms, endurance). The conclusion explicitly states: "Based on the similarities in the safety and effectiveness profiles of the subject, predicate and reference devices, no clinical studies were deemed to needed to support this submission."

To address your request, I will explain why your specific questions are not applicable to this document and provide the information that is available.

Reasoning for Inapplicability of AI/ML-focused Questions:

The LTV2 Series Ventilator is a mechanical device, not an AI/ML diagnostic or prognostic tool. Therefore, the regulatory pathway (510(k)) and the evidence required to demonstrate safety and effectiveness for this type of device differ significantly from what would be expected for an AI/ML device.

• Acceptance Criteria & Reported Performance (Table A): For a mechanical device, performance is evaluated against engineering specifications, industry standards (e.g., IEC 60601 series, ISO 80601-2-12 for ventilators), and comparison with predicate devices. There isn't a "test set" in the sense of a dataset of medical images or patient records, or metrics like sensitivity/specificity for a diagnosis. Performance is about meeting physical parameters (e.g., tidal volume range, breath rate range, pressure control range, alarm specifications, electrical safety).
• Sample Sizes, Data Provenance, Experts, Adjudication, MRMC, Standalone Performance, Ground Truth (Questions 2-7): These questions are highly specific to the validation of AI/ML algorithms, particularly in medical imaging or diagnostic contexts. They relate to how a model's output is compared against a clinical "truth" established by human experts or other definitive sources. For a ventilator, "performance" is demonstrated through non-clinical testing (e.g., mechanical testing, software V&V, endurance testing) confirming it adheres to its design specifications and relevant safety/performance standards.
• Training Set & Ground Truth Establishment (Questions 8-9): These apply to the development and training of machine learning models. A mechanical ventilator does not have "training data" in this sense. Its design and functionality are based on engineering principles and verified through physical testing.

Information Available from the Document (Addressing Relevant Aspects):

The document demonstrates the device's safety and effectiveness by showing substantial equivalence to existing predicate devices and compliance with recognized performance standards.

1. Table of "Acceptance Criteria" (derived from technical specifications and standards) and "Reported Device Performance" (implied by meeting standards and equivalence to predicates):

For a mechanical ventilator, "acceptance criteria" are typically defined by engineering specifications and compliance with relevant international standards. "Reported device performance" is confirmed through design verification and validation testing, ensuring these specifications and standards are met. The document states:

"Successful test results (electrical safety testing, mechanical testing, software V&V, and waveform verification tests) ensured the proposed ventilator does not raise any different questions of safety and effectiveness."

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

• Not Applicable in the AI/ML sense. For this mechanical device, performance is evaluated through design verification and validation testing, and compliance with standards. There is no "test set" of patient data or images. Performance is based on physical property testing, software validation, and electrical safety testing.
• The data provenance for such tests are typically in-house lab reports and compliance certificates from testing bodies, not clinical data from specific countries or retrospective/prospective studies.

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

• Not Applicable. "Ground truth" established by experts is a concept for AI/ML diagnostic or prognostic devices. For a ventilator, the "truth" is whether the device meets its engineered specifications and performs reliably according to intended use and recognized standards. This is assessed by engineers, quality control personnel, and regulatory specialists.

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

• Not Applicable. Adjudication methods are used in studies involving human interpretation or labeling of data, typically for AI/ML validation. This is a mechanical 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:

• Not Applicable. MRMC studies are for evaluating the impact of AI assistance on human performance in diagnostic tasks. This is a mechanical ventilator.

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

• Not Applicable. This refers to the performance of an AI algorithm on its own. While the ventilator has internal algorithms for control, its "performance" is inherently tied to its mechanical function, and its safety/effectiveness is not evaluated as a standalone AI system.

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

• Not Applicable in the AI/ML sense. The "ground truth" for a mechanical ventilator's performance is its adherence to its design specifications, established engineering principles, and compliance with international performance and safety standards for ventilators. This is verified through objective measurements, calibrations, and stress tests.

8. The sample size for the training set:

• Not Applicable. A mechanical ventilator does not have a "training set" in the context of machine learning.

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

• Not Applicable. As there's no training set, this question is not relevant.

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The Vyntus BODY 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, a mask or nasal adapters. The results of the test can be viewed online with the help of a computer screen and can be printed after the test. The test results can be saved for future reference or report generation purposes.

The products can be utilized with patients aged 4 years and older as long as they can cooperate in the performance - no special limit to patient's sex or height exists.

Measurements will be performed under the direction of a physician in a hospital environment, physician's office or similar setting (professional healthcare facilities).

A qualified physician has to reassess all Vyntus BODY measurements. An interpretation by SentrySuite is only significant if it is considered in connection with other clinical findings.

The Vyntus BODY is a whole-bodyplethysmograph and consists of the Vyntus BODY cabin, an ultrasonic flow sensor (USS), and a shutter. The Vyntus BODY system allows the determination of a subjects' pulmonary function status. It includes the determination of the subjects' ventilatory flows and volume by means of the USS. The measurement of the lung diffusion by the DLCO technique is accomplished with the supply of test gas and the gas analyzers for methane (CH4) and carbone monoxide (CO).

All variants are stationary and not battery operated. The sensor data is sent to a host computer system via cable connection for processing, storage, and reporting. The host computer can be networked via LAN, WLAN, or Internet.

All measurements are performed with the use of the Windows based operating software SentrySuite (SeS). The SeS software also stores the measurement results and provides output capabilities.

The host computer, monitor, and printer are mounted on the Vyntus Cart. Use of the cart is optional; computing equipment may be placed on other furniture.

The provided text is a 510(k) summary for the Vyntus BODY, a pulmonary-function data calculator. It outlines the device's characteristics, its comparison to predicate devices, and the performance data submitted to support its substantial equivalence. However, it does not contain a detailed study report that proves the device meets specific acceptance criteria with reported device performance metrics and specific sample sizes for test sets.

The document focuses on demonstrating substantial equivalence to predicate devices, rather than an independent performance study against predefined criteria.

Therefore, many of the requested details about acceptance criteria, reported performance, sample sizes, ground truth establishment, expert involvement, and MRMC studies are not explicitly provided in this document.

Here's what can be extracted and what is missing:

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

The document mentions "Accuracy testing" as one of the performance data provided. In the "Patient User Interface Specifications" table (Page 7-8), it lists performance specifications for flow and volume accuracy which can be considered acceptance criteria. However, it does not explicitly provide the reported device performance against these criteria in a clear table format. The table below presents the acceptance criteria as listed:

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

• Sample size for the test set: Not mentioned.
• Data provenance: Not mentioned. The document primarily refers to "Accuracy testing" as a type of performance data provided, but without details on the studies or datasets used for this testing.

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. The device is a "Pulmonary-function data calculator," suggesting that its accuracy would likely be evaluated against established physiological measurement standards, not necessarily against expert human interpretation in the same way an AI diagnostic imaging tool would be.

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

Not applicable or not mentioned. The device's primary function is to measure and calculate lung function parameters; its performance would likely be validated against metrological standards or established calibration methods, rather than through an adjudication process of expert interpretations.

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. The Vyntus BODY is a pulmonary function data calculator, not an AI-assisted diagnostic tool that aids human readers in interpreting complex medical images or data. Therefore, an MRMC study comparing human readers with and without AI assistance is not relevant to this device's function or validation.

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

The device itself is a standalone system for measurements, data collection, and analysis. It is designed to perform these functions "without human-in-the-loop performance" in terms of its direct measurement and calculation capabilities. The document states, "A qualified physician has to reassess all Vyntus BODY measurements. An interpretation by SentrySuite is only significant if it is considered in connection with other clinical findings." This indicates that while the device performs its analytical function standalone, ultimate clinical interpretation requires a physician.

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

The "Accuracy testing" mentioned would typically use calibrated instruments or known physical standards as ground truth for flow and volume measurements. The document references "ATS/ERS Task Force: Standardization of Lung Function Testing," implying that the device's performance is likely measured against these established international standards for pulmonary function testing.

8. The sample size for the training set:

Not applicable or not mentioned. The device appears to be a measurement and calculation system, not a machine learning or AI algorithm that requires a "training set" in the conventional sense. Its functionality is based on known physiological principles and instrumentation, likely calibrated against standard references.

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

Not applicable, as there's no indication of a "training set" for an AI/ML algorithm.

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