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The V60 ventilator is an assist ventilator and is intended to augment patient breathing. It is intended for spontaneously breathing individuals who require mechanical ventilation: patients with spontaneous respiratory failure, chronic respiratory insufficiency, or obstructive sleep apnea in a hospital or other institutional settings under the direction of a physician.

The Ventilator is intended to support pediatric patients weighing 20kg (44 lbs) or greater to adult patients. It is also intended for intubated patients meeting the same selection criteria as the noninvasive applications. The Ventilator is intended to be used by qualified medical professionals, such as physicians, nurses, and respiratory therapists.

The ventilator is intended to be used only with various combinations of Respironics recommended patient circuits, interfaces (masks), humidifiers and other accessories.

The V60™ ventilator is a microprocessor controlled positive pressure ventilatory assist system incorporating a user interface with multifunction keys, real-time graphic displays, and integral patient and system alarms. The ventilator provides noninvasive and invasive ventilatory support for spontaneously breathing adult and pediatric patients.

The provided text describes a 510(k) submission for the Respironics V60 Ventilator. The summary states that "Bench performance testing was performed comparing the new V60 and the predicate devices, and was found to be substantially equivalent." and "Performance testing and human factors testing demonstrate that the device is as safe, as effective and performs as well as or better than the predicate device."

However, the document does not provide specific acceptance criteria, reported device performance metrics, detailed study designs, sample sizes, information on ground truth establishment, or any details about multi-reader multi-case (MRMC) comparative effectiveness studies.

Therefore, I cannot populate the table or answer most of the questions based on the provided text. The document focuses on demonstrating substantial equivalence to predicate devices through general performance and human factors testing, rather than presenting a detailed clinical study with quantitative acceptance criteria.

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

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

The document mentions "Bench performance testing" and "human factors testing" but does not specify sample sizes for any test sets, nor does it provide details about data provenance (e.g., country of origin, retrospective or prospective nature).

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 specified in the provided text. The document does not describe the establishment of a "ground truth" using experts for the performance testing.

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

Not specified in the provided text.

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. The device is a ventilator, and the testing described is performance and human factors testing of the device itself, not a study evaluating human reader performance with or without AI assistance.

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

This question is not applicable in the context of a ventilator. The described "Performance testing" is inherently standalone in the sense that it tests the device's functionality.

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

The concept of "ground truth" as typically defined for diagnostic AI studies using expert consensus or pathology is not applicable to the performance testing of a ventilator as described in this document. The "ground truth" for a ventilator's performance would be engineering specifications and established physiological parameters it is designed to achieve.

8. The sample size for the training set

Not applicable. The document describes a medical device, not an AI/ML algorithm that requires a training set.

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

Not applicable. The document describes a medical device, not an AI/ML algorithm that requires a training set.

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The intended use of the Esprit Ventilator Neonatal Option is to provide continuous or intermittent ventilatory support for the care of individuals who require mechanical ventilation. The intended patient population includes intubated neonatal patients with an ideal body weight range from 0.5 kg to 6.5 kg and an endotracheal tube I.D. range from 2.5 – 4.0 mm.

The Neonatal Option is a software modification to the currently marketed Esprit Ventilator. Once the software is enabled and the neonatal patient type is selected on the Esprit, it provides the following types of ventilatory support to neonatal patients in invasive applications only: Assist/Control, Spontaneous Intermittent Mandatory Ventilation (SIMV) or Continuous Positive Airway Pressure (CPAP) modes of ventilation. Pressure-Controlled (PC). Available in A/C and SIMV. Pressure Support (PS). Available in SIMV and SPONT. Apnea Ventilation

The provided text describes a software modification to the Esprit Ventilator, called the "Neonatal Option," but it does not contain acceptance criteria or a study proving that the device meets specific acceptance criteria in the format requested.

The document is a 510(k) summary for a medical device modification, which focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed performance study results against predefined acceptance criteria.

Here's an breakdown of what is and isn't available based on your request:

1. Table of Acceptance Criteria and Reported Device Performance:

• Comment: The document states that "performance testing was successfully completed" and "all design and system level requirements...have been met," but it does not explicitly list what those acceptance criteria (e.g., specific metrics like accuracy, precision, or reliability thresholds) were, nor does it provide the quantitative results of the performance testing against such criteria. It's a high-level statement of success.

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

• Sample Size: Not specified.
• Data Provenance: Not specified. The testing described is "Software verification and validation testing," implying internal testing rather than clinical study data from a specific country or retrospective/prospective collection.

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

• Number of Experts: Not applicable. The document is about a ventilator's software modification, not an AI or diagnostic device that typically requires expert-established ground truth from medical images or clinical observations.
• Qualifications: Not applicable.

4. Adjudication Method for the Test Set:

• Adjudication Method: Not applicable. This type of testing (software verification and validation for a ventilator) does not typically involve expert adjudication of results in the way an AI diagnostic tool would.

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

• MRMC Study: No. This document does not describe an MRMC study. The device is a ventilator, not an imaging or diagnostic AI tool that would typically involve human readers.
• Effect Size of Human Readers: Not applicable.

6. Standalone (Algorithm Only) Performance Study:

• Standalone Study: Yes, indirectly. The "Software verification and validation testing" is conducted on the device's software algorithms to ensure they meet design and system-level requirements. This inherently refers to the algorithm's performance in controlling ventilation parameters. However, specific metrics or a detailed standalone performance study report are not provided. The phrase "Breath delivery is controlled by software algorithms that are equivalent to those used on the currently marketed Respironics Esprit ventilator" suggests that the technical characteristics and performance of these algorithms were deemed equivalent to a previously cleared device.

7. Type of Ground Truth Used:

• Type of Ground Truth: "Design and system level requirements." For a ventilator, ground truth would typically be established based on engineering specifications, physiological models, and regulatory standards for delivering specific pressure, volume, and flow characteristics accurately and safely. It would not typically be pathology, expert consensus on images, or outcomes data in the way an AI diagnostic device would.

8. Sample Size for the Training Set:

• Sample Size for Training Set: Not applicable. This device is a software modification for a ventilator, not a machine learning or AI model trained on a data set.

9. How Ground Truth for Training Set was Established:

• How Ground Truth was Established: Not applicable, as there is no training set in the context of an AI model for this device.

Summary of what the document does provide regarding performance:

• It states that the Neonatal Option is a software modification to an existing ventilator.
• It claims the addition does not result in the use of any new technological characteristics.
• It explicitly states: "Breath delivery is controlled by software algorithms that are equivalent to those used on the currently marketed Respironics Esprit ventilator."
• It indicates that "Software verification and validation testing was performed per FDA's Guidance for the Content of Premarket Submissions for Software contained in Medical Devices (1998)."
• It concludes that "Performance testing was successfully completed demonstrating that all design and system level requirements for the Esprit Ventilator with Neonatal Option have been met."
• The primary method for determining substantial equivalence relies on the similarity of performance characteristics, intended use, and patient populations to predicate devices, implying that the performance meets expectations set by already approved devices.

In essence, the document serves as a regulatory submission arguing for substantial equivalence based on the software's adherence to established design requirements and equivalency to existing technology, rather than detailing a specific experimental study with statistical results against explicit numerical acceptance criteria.

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The intended use of the PLV Continuum II ventilator is to provide continuous or intermittent ventilatory support for the care of individuals who require mechanical ventilation. The intended patient population includes pediatric and adult patients who weigh at least 5 kg (11 lbs). The PLV Continuum II ventilator is intended for use in home, institutional and portable settings and may be used for invasive as well as non-invasive ventilation.

The PLV Continuum ventilator is a microprocessor controlled, compressor-based, ventilator that provides ventilatory support by delivering room air to the patient. PLV Continuum utilizes an internal compressor to generate compressed air for delivery to the patient. Breath delivery is controlled by software. The PLV Continuum has a membrane keypad with indicator Light Emitting Diodes (LED) for the selection and acceptance of patient settings. The PLV Continuum is capable of providing the following types of ventilatory support:

• Positive Pressure Ventilation, delivered either invasively (via endotracheal or tracheostomy tube) or non-invasively (via mask or mouthpiece).
• Assist Control, Synchronized Intermittent Mandatory Ventilation (SIMV) or Spontaneous Pressure (CPAP) modes of ventilation.
• Volume-Controlled (VC). Available in A/C and SIMV.
• Pressure-Controlled (PC). Available in A/C and SIMV.
• Pressure Support (PS). Available in SIMV and SPONT.

The provided document is a 510(k) Premarket Notification for a modification to the PLV Continuum Ventilator, now referred to as the PLV Continuum II Ventilator. This document concerns a medical device (ventilator) and its regulatory approval process, rather than a clinical study evaluating an AI/ML powered device. As such, many of the requested fields (e.g., sample size for test/training sets, number of experts, adjudication methods, MRMC studies, standalone performance) are not applicable to this type of submission.

The document primarily focuses on establishing substantial equivalence to predicate devices rather than proving the performance against specific acceptance criteria in the context of an AI/ML algorithm.

However, I can extract information related to the performance testing conducted for the device.

Here's a summary of the information that can be extracted, with notes for the fields that are not applicable:

1. Table of Acceptance Criteria and Reported Device Performance

Study Proving Device Meets Acceptance Criteria:

The study that proves the device meets the acceptance criteria is described as a series of performance tests, rather than a single clinical study.

• Study Name/Description: "Summary of Performance Testing" (Section 16.5)
• Study Objective: To demonstrate that all design and system requirements for the modified PLV Continuum have been met and to support substantial equivalence to predicate devices.
• Key Finding: "The results of all testing demonstrate that all design and system requirements for the modified PLV Continuum have been met." (Section 16.5) and "The technological characteristics of the modified PLV Continuum ventilator and the results of the performance testing do not raise new questions of safety and effectiveness when compared to the legally marketed predicate devices." (Section 16.6)

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

• Not Applicable. This is a hardware/software medical device modification submission, not an AI/ML study with a distinct "test set" of patient data. The "testing" refers to engineering and regulatory compliance testing of the device itself.

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. Ground truth, in the context of patient data adjudicated by experts, is not relevant to this type of device submission. Performance was assessed against engineering standards and specifications.

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

• Not Applicable. See point 3.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• Not Applicable. This is not an AI-powered device, nor is it a diagnostic device being evaluated with human readers.

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

• Not Applicable. While the device is microprocessor-controlled with software algorithms, the "standalone" performance requested typically refers to the performance of an AI algorithm in isolation from a human user, which is not the subject of this document. The device's performance was evaluated as a complete system.

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

• Not Applicable as traditionally defined for AI/ML studies. The "ground truth" here is adherence to engineering specifications and regulatory standards (e.g., flow rates, pressure delivery, safety alarms, EMC compliance).

8. The sample size for the training set

• Not Applicable. There is no "training set" in the context of an AI/ML algorithm development described in this document. The software algorithms are described as "equivalent to those used on the currently marketed Respironics PLV Continuum ventilator (K022750)".

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

• Not Applicable. See point 8.

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The intended use of the PLV Continuum ventilator is to provide continuous or intermittent ventilatory support for the care of individuals who require mechanical ventilation. The intended patient population includes pediatric and adult patients who weigh at least 5 kg (11 lbs). The PLV Continuum ventilator is intended for use in home, institutional and portable settings and may be used for invasive as well as non-invasive ventilation.

The PLV Continuum ventilator is a microprocessor controlled, compressor-based, mechanical ventilator. It is intended to control or assist breathing by delivering room air to the patient. PLV Continuum utilizes an internal compressor to generate compressed air for delivery to the patient. Breath delivery is controlled by software algorithms. The user interface on PLV Continuum has a membrane keypad with indicator Light Emitting Diodes (LED) for the selection and acceptance of patient settings and for the display of alarm conditions. PLV Continuum is capable of providing the following types of ventilatory support:

• Positive Pressure Ventilation, delivered either invasively (via endotracheal or . tracheostomy tube) or non-invasively (via mask or mouthpiece).
• Assist/Control. Spontaneous Intermittent Mandatory Ventilation (SIMV) or . Continuous Positive Airway Pressure (CPAP) modes of ventilation.
• Volume-Controlled (VC). Available in A/C and SIMV. .
• Pressure-Controlled (PC). Available in A/C and SIMV. .
• Pressure Support (PS). Available in SIMV and SPONT. ●

Here's an analysis of the provided text regarding the acceptance criteria and study for the PLV Continuum Ventilator:

Acceptance Criteria and Device Performance for PLV Continuum Ventilator

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document states that the results "demonstrate that all design and system requirements...have been met," implying successful adherence to the standards. Specific numerical performance data against acceptance thresholds from ASTM F 1100-90 and F 1246-91 are not explicitly detailed in this summary.

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

The provided summary does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective). The document refers to "Performance testing," "EMC testing," "Electrical, mechanical and environmental testing," and "Software validation testing" as being conducted, but typical details like the number of devices tested, number of patients, or specific test scenarios are not included in this high-level summary.

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

This information is not provided in the summary. Performance testing for a ventilator typically involves engineering and functional tests against specifications, rather than expert-established ground truth in the same way a diagnostic imaging AI might.

4. Adjudication Method for the Test Set

This information is not applicable or provided. The document describes performance testing against established standards and guidance, not an adjudication process of expert opinions.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This type of study is specifically relevant for diagnostic AI devices where human readers interpret medical images or data. The PLV Continuum Ventilator is a mechanical ventilator, and its evaluation focuses on its functional performance, safety, and equivalence to predicate devices, not on human-in-the-loop diagnostic accuracy.

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

Given that the device is a mechanical ventilator, the "standalone" performance would encompass the various performance tests mentioned (e.g., breath delivery accuracy, alarm functionality, power consumption) without direct human intervention in its core mechanical operation. The summary states: "Breath delivery is controlled by software algorithms." This implies algorithmic function is evaluated as part of the overall device performance. However, there isn't a separate "algorithm only" study specifically described as it would be for an AI diagnostic device. The performance testing evaluates the device's functions, which are driven by its algorithms.

7. The Type of Ground Truth Used

The "ground truth" for the PLV Continuum Ventilator's performance testing is based on established engineering standards and regulatory guidance. This includes:

• ASTM F 1100-90 and F 1246-91: These are standards for ventilators, providing the criteria against which the device's mechanical and functional performance (e.g., volume delivery, pressure control, alarm thresholds) would be measured.
• FDA Draft Reviewer Guidance for Premarket Notification Submissions (1993): This guidance dictates the requirements for electrical, mechanical, and environmental testing.
• FDA's Guidance for the Content of Premarket Submissions for Software contained in Medical Devices (1998): This guidance sets the "ground truth" for software validation.

Essentially, the "ground truth" is defined by compliance with these recognized safety and performance standards for medical devices.

8. The Sample Size for the Training Set

The concept of a "training set" is usually applicable to machine learning or AI models that learn from data. The PLV Continuum Ventilator is described as a "microprocessor controlled" device with "software algorithms" that are "equivalent to those used on the currently marketed Respironics Esprit ventilator." This suggests that the software algorithms were developed based on established engineering principles for ventilator function, possibly iteratively tested and refined, rather than being "trained" on a large dataset in the sense of a deep learning model. Therefore, a "training set" in the context of an AI model driven by data is not explicitly mentioned or directly applicable here.

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

As the device relies on established software algorithms and engineering principles rather than a data-driven AI model, the concept of a "training set" with established ground truth as it applies to AI/ML is not relevant here. The ground truth for the development of such a device's software would stem from physiological requirements for ventilation, mechanical engineering principles, and the performance characteristics of predicate devices, which were then codified into algorithms. The testing described then verifies that these algorithms, as implemented in the device, meet the specified performance and safety requirements.

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