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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 Legendair XL2 is indicated for the continuous or intermittent mechanical ventilatory support of patients weighing at least 5 kg 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 who require the following general types of ventilatory support, as prescribed by an attending physician:
. Positive Pressure ventilation
Assist/Control, SIMV, or CPAP modes of ventilation .
Breath types including Volume, Pressure Control and Pressure Support. ◆
The ventilator is suitable for use in institutional, home, and transport settings. It is not intended for use as an emergency transport ventilator.

The LEGENDAIR® XL2 ventilator is composed of an airflow generator capable of supplying a range of flow rates and pressures and a valve enabling piloting of the expiration valve. The operation of the device is based on a self-adapting drive system governed by a closed loop flow generator. The speed of the flow generator (turbine) is servo-controlled to the patient pressure signal or the inspired flow signal.
The ventilation modes available are:

• -Pressure Support Ventilation
• . Pressure Support Ventilation with Back Up Rate
• CPAP mode -
• Pressure Controlled Ventilation ﮯ
• Pressure Assisted Controlled Ventilation -
• Volume Controlled Ventilation -
• Volume Assisted Controlled Ventilation -
• Synchronous Intermittent Mandatory Ventilation with either volume or pressure ﮮ targeted mandatory breaths

The provided text does not contain acceptance criteria or study details in the format typically used for AI/ML device submissions. This document is a 510(k) summary for a ventilator, a type of electro-mechanical medical device, not an AI/ML diagnostic or predictive tool.

Therefore, many of the requested fields are not applicable to the information provided in this document.

Here's an analysis based on the information available and how it relates to the questions, with an explanation of why other fields are not applicable:

1. Table of Acceptance Criteria and Reported Device Performance:

The document focuses on "substantial equivalence" to predicate devices rather than specific quantitative performance metrics against predefined acceptance criteria for an AI/ML model.

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

• Not Applicable. This is not an AI/ML device where a "test set" of data samples (e.g., images, patient records) is used to evaluate an algorithm's performance. The "testing" referred to for this ventilator would involve engineering and functional tests on the physical device itself.

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

• Not Applicable. See point 2. Ground truth in the context of an AI/ML device is typically established by medical experts reviewing data, which is not relevant here.

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

• Not Applicable. See point 2.

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. An MRMC study is relevant for AI-assisted diagnostic devices where human readers (e.g., radiologists) interpret cases with and without AI assistance. This is a ventilator, not a diagnostic AI tool.

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

• Not Applicable. This device is a ventilator, not an algorithm, so the concept of standalone performance does not apply in the AI/ML sense. Its "performance" is inherent to its mechanical and software operation.

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

• Not Applicable. The "ground truth" for a ventilator would be its adherence to specified operational parameters (e.g., delivered pressure, flow rates, alarms functioning correctly) validated through engineering tests against known standards, not expert consensus on medical data or pathology. The document explicitly states "compliance with external electrical and mechanical safety standards" and "detailed comparison of performance modes, ranges of operation."

8. The sample size for the training set:

• Not Applicable. This is not an AI/ML device that requires a "training set" of data to learn patterns. The ventilator's operation is based on coded logic and physical engineering, not machine learning.

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

• Not Applicable. See point 8.

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