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The AVEA is intended to provide continuous respiratory support in an institutional health care environment (e.g. hospitals). It may be used on neonatal through adult patients. It should only be operated by properly trained clinical personnel, under the direction of a physician.

The AVEA is a servo-controlled, software-driven ventilator. It has a dynamic range of breathing gas delivery that provides for neonatal through adult patients. Its graphical user interface module (UIM) has a flat panel color LCD with real time charting and digital monitoring capabilities, a touch screen for interaction, membrane kevs and a dial for changing settings and operating parameters. It also has an internal gas delivery system with servo controlled active inhalation and exhalation functions. Using internal batteries this provides inter-hospital transport as well as back up capability due to loss of AC power. The AVEA may be configured as a conventional ventilator or non-invasive positive pressure ventilator (NPPV). It has been designed to function using commonly available accessories.

The provided document is a 510(k) summary for the AVEA Ventilator, focusing on modifications for Volume Guarantee and Nasal Intermittent Positive Pressure Ventilation. It states that "Performance testing verified that the AVEA Ventilator meets its performance requirements and that this device is substantially equivalent to medical devices currently legally marketed in the United States." However, it does not provide specific details about the acceptance criteria or the study that proves the device meets those criteria, as requested in the prompt.

Therefore, I cannot provide a complete answer to your request based on the provided text. The document confirms that performance testing was done, but it omits the actual results and methodology of that testing.

Here's what I can extract and what is missing based on your request:

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

• Missing from the document. The document states that performance testing "verified that the AVEA Ventilator meets its performance requirements," but it does not list these requirements (i.e., acceptance criteria) or the specific reported device performance against them.

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

• Missing from the document. The document mentions "Performance testing" but does not specify the sample size, data provenance, or whether it was retrospective or prospective.

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 / Missing from the document. This type of information is typically relevant for AI/ML-based diagnostic devices where a "ground truth" needs to be established, often by human experts reviewing medical images or patient data. The AVEA Ventilator is a mechanical ventilator, and its performance testing would likely involve engineering and physiological measurements rather than expert review for "ground truth." Therefore, this information is not relevant in the context of this device or is completely absent.

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

• Not applicable / Missing from the document. Similar to point 3, adjudication methods are usually for resolving discrepancies in expert interpretations, which is not relevant for a mechanical ventilator's performance testing.

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 / Missing from the document. An MRMC study is relevant for diagnostic AI tools involving human readers. This is not applicable to a mechanical ventilator like the AVEA Ventilator.

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

• Not applicable / Missing from the document. This question is also focused on AI/ML algorithms. The AVEA Ventilator has software that implements Volume Guarantee and Nasal Intermittent Mandatory Ventilation, which are automated functions. The document states "software implement Volume Guarantee which is the automated requlation of inspiratory pressure," implying standalone algorithmic function within the device. However, the performance study details for these automated functions are not provided.

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

• Not applicable / Missing from the document. As mentioned in point 3, the concept of "ground truth" in the context of expert review is unlikely to apply to the performance testing of a mechanical ventilator. The ground truth for such a device would typically be derived from engineering specifications, established physiological parameters, and direct measurement against those standards. The specific methods are not detailed.

8. The sample size for the training set:

• Not applicable / Missing from the document. A "training set" typically refers to data used to train an AI/ML model. While the ventilator has "software-driven" functions, the document does not suggest an AI/ML model that would require a distinct training set in the conventional sense. The "training" would be more akin to software development and verification/validation against specifications.

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

• Not applicable / Missing from the document. For the reasons stated in points 7 and 8, this information is not provided and likely not relevant in the AI/ML context.

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The AVEA Ventilator is intended to provide continuous respiratory support in an institutional health care environment (e.g. hospitals). It may be used on adult, pediatric, and neonatal patients. It should be operated by properly trained clinical personnel, under the direction of a physician.

The AVEA Ventilator is a fourth generation servo-controlled, software-driven ventilator. It has a dynamic range of breathing gas delivery that provides for neonatal through adult patients. Its revolutionary user interface module (UIM) provides maximum flexibility wit6h simple operator interaction. It has a flat panel color LCD with real time graphic displays and digital monitoring capabilities, a touch screen for easy interaction, membrane keys and a dial for changing settings and operating parameters. A precision gas delivery engine with servo controlled active inhalation and exhalation improves performance over previous generations. The only design change is an optional Disposable Expiratory Filter / Water Trap to be used exclusively with the AVEA Ventilator.

The provided text is a 510(k) summary for the AVEA Ventilator's optional Disposable Expiratory Filter / Water Trap. It focuses on demonstrating substantial equivalence to predicate devices rather than proving a device performance claim through a clinical study with specific acceptance criteria.

The key points from the document regarding "acceptance criteria" and "study" are:

• Acceptance Criteria: The device is considered to meet acceptance criteria if it is "substantially equivalent in terms of BFE / VFE and resistance performance" to the predicate device (Clear-Guard II, K990949) and "has the same indicated use as the reusable existing filter / water trap" (integral to the AVEA Ventilator, K013642).
• Study: "Verification and Validation Testing demonstrated that the AVEA Disposable Expiratory Filter / Water Trap meets its performance requirements at both: Component Level and System Level, and, that this device is substantially equivalent to medical devices currently legally marketed in the United States."

This 510(k) summary does not provide the kind of detailed clinical study information (e.g., sample sizes, ground truth establishment, expert qualifications, MRMC studies) that would typically be associated with a device making a new performance claim or AI algorithm. It is a "substantial equivalence" review for a modification (an optional disposable filter) to an already cleared ventilator.

Therefore, the requested table and detailed information mostly cannot be extracted because the provided document does not contain that level of detail for this type of submission.

Here's a breakdown of what can be inferred or stated from the text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not specified. The document refers to "Verification and Validation Testing" but does not break down the specific sample sizes used for BFE/VFE or resistance testing.
• Data Provenance: Not specified. It's likely laboratory testing rather than patient data, given the nature of the filter.

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

• Not applicable as this is not a diagnostic device involving expert interpretation or ground truthing of clinical data in the traditional sense. Performance is based on physical and biological filtration efficiency measurements.

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

• Not applicable for the type of testing described (performance testing of a filter).

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

• No, an MRMC study was not done. This device is a ventilator filter, not an AI-assisted diagnostic tool for human readers.

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

• No, this is a hardware device (filter), not an algorithm.

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

• The "ground truth" here is the measured performance metrics (BFE, VFE, resistance) against established engineering standards and the performance of the predicate device. It's not clinical "ground truth" derived from patient outcomes or expert consensus.

8. The sample size for the training set

• Not applicable. This is not an AI/machine learning device requiring a training set.

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

• Not applicable.

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The AVEA is intended to provide continuous respiratory support in an institutional health care environment (e.g. hospitals). It may be used on adult, pediatric, and neonatal patients. It should only be operated by properly trained clinical personnel, under the direction of a physician.

The AVEA is a servo-controlled, software-driven ventilator. It has a dynamic range of breathing gas delivery that provides for neonatal through adult patients. Its graphical user interface module (UIM) has a flat panel color LCD with real time charting and digital monitoring capabilities, a touch screen for interaction, membrane keys and a dial for changing settings and operating parameters. It also has an internal gas delivery system with servo controlled active inhalation and exhalation functions. Using internal batteries this provides inter-hospital transport as well as back up capability due to loss of AC power. The AVEA may be configured as a conventional ventilator or non-invasive positive pressure ventilator (NPPV). It has been designed to function using commonly available accessories.

The provided text does not contain the information requested for acceptance criteria and the study that proves the device meets the acceptance criteria.

The document is a 510(k) premarket notification summary for modifications to the AVEA Ventilator, specifically the addition of a capnometry monitor and onboard barometric pressure sensor. While it mentions "Performance testing verified that the AVEA Ventilator meets it's performance requirements," it does not provide any specific criteria, reported performance values, or details about the studies conducted.

Therefore, I cannot populate the table or answer the specific questions about sample size, data provenance, expert involvement, adjudication methods, MRMC studies, standalone performance, ground truth details, or training set information.

Ask a specific question about this device

The AVEA is intended to provide continuous respiratory support in an institutional health care environment (e.g. hospitals). It may be used on adult, pediatric, and neonatal patients. It should only be operated by properly trained clinical personnel, under the direction of a physician.

The AVEA is a servo-controlled, software-driven ventilator. It has a dynamic range of breathing gas delivery that provides for neonatal through adult patients. Its graphical user interface module (UIM) has a flat panel color LCD with real time charting and digital monitoring capabilities, a touch screen for interaction, membrane keys and a dial for changing settings and operating parameters. It also has an internal gas delivery system with servo controlled active inhalation and exhalation functions. Using internal batteries this provides inter-hospital transport as well as back up capability due to loss of AC power. The AVEA may be configured as a conventional ventilator or non-invasive positive pressure ventilator (NPPV). It has been designed to function using commonly available accessories.

This is a 510(k) premarket notification for a medical device (AVEA Ventilator), not a study report. Therefore, much of the requested information regarding acceptance criteria and study details for device performance, ground truth, and expert evaluation is not available in the provided text.

However, based on the submission, here's what can be extracted and inferred:

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

The document does not explicitly state quantitative acceptance criteria or detailed reported device performance in a table format. It states generally that: "Performance testing verified that the AVEA Ventilator meets it's performance requirements and that this device is substantially equivalent to medical devices currently legally marketed in the United States."

The focus of this 510(k) is on modifications to an already cleared device, asserting substantial equivalence. The specific modifications are:

• "Software update encompasses a modification to the current . Non-Invasive ventilation previously cleared under K013642 of which allows for a particular mode on the Infant Flow Plus, Nasal CPAP."
• "This Nasal CPAP mode that is emulated is accomplished by a software modification only, utilizing existing AVEA hardware."
• "This Nasal CPAP mode is only for single level continuous positive airway pressure to nasal pronos."

Therefore, the "acceptance criteria" for this specific submission would implicitly be:

• The modified AVEA Ventilator, with the new Nasal CPAP software, performs equivalently to the predicate devices for continuous ventilation and specifically for the Nasal CPAP mode.
• The software modification does not adversely affect existing validated functionalities.

Given the nature of a ventilator, performance metrics would likely revolve around aspects such as:

• Accuracy of delivered pressure/volume/flow.
• Response time to patient effort.
• Safety alarms and limits.
• Biocompatibility (not directly covered by the software modification but generally a part of medical device approval).

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 does not provide details on the sample size for any specific test set, nor the data provenance (country of origin, retrospective/prospective). The submission refers to "Performance testing" in a general sense.

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. Since this is an engineering performance assertion for a ventilator and not a diagnostic device relying on expert interpretation of images or signals, the concept of "ground truth" derived from expert consensus in that context is unlikely to apply directly in the same way. Performance testing for a ventilator typically involves engineering and physiological measurements rather than expert clinical consensus on data.

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

Not applicable/not provided. Adjudication methods like 2+1 or 3+1 are typically used in studies involving expert interpretation where disagreements need to be resolved for ground truth establishment. This is not the type of study 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

Not applicable. This is a ventilator device, not an AI-assisted diagnostic tool for human readers.

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

This concept doesn't directly apply as the AVEA Ventilator is a device that interacts with a patient and is operated by trained clinical personnel. The "performance testing" mentioned would be for the device's functionality, which by its nature is "standalone" in terms of its mechanical and software operation, but in a real-world scenario, it's always "with human-in-the-loop" for patient management. The document states it is "servo-controlled, software-driven."

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

Given the device type, "ground truth" would likely be derived from:

• Engineering specifications and validated measurement equipment (e.g., flow sensors, pressure transducers) for evaluating the ventilator's physical outputs.
• Physiological models or test lung systems to simulate patient conditions.
• Compliance with recognized standards (e.g., ISO standards for ventilators).
  However, the specific methods are not detailed in the provided text.

8. The sample size for the training set

Not applicable. This device is not described as utilizing a machine learning model that requires a distinct "training set" in the context of AI/ML. The software modification is likely a rule-based or control-algorithm update, not an autonomously learning system.

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

Not applicable, as there is no mention of a machine learning training set.

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The AVEA is intended to provide continuous respiratory support in an institutional health care environment (e.g. hospitals). It may be used on adult, pediatric, and neonatal patients. It should only be operated by properly trained clinical personnel, under the direction of a physician. The AVEA is indicated for the delivery of air, oxygen or a helium-oxygen combination (Heliox).

The AVEA is a servo-controlled, software-driven ventilator. It has a dynamic breathing gas delivery that provides for neonatal through adult patients. Its module provides maximum flexibility with simple operator interaction. It has a range of user interface interaction, membrane keys and a dial for changing settings and operating parameters. It also has an internal gas delivery system with servo controlled active inhalation and exhalation functions. The AVEA may be configured as a conventional ventilator or noninvasive positive pressure ventilator (NPPV). It has been designed to function using most commonly available accessories.

The provided text describes the 510(k) summary for the AVEA Ventilator. It focuses on the device's intended use, its substantial equivalence to predicate devices, and a brief description of testing.

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

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

The submission does not explicitly provide a table of acceptance criteria with corresponding performance metrics. Instead, it states:

• Same indicated use
• Similar indication as heliox predicate
• Same operating principle
• Same basic ventilator design (except for heliox connector)
• Manufactured and packaged utilizing the same basic processes. |

Missing Information:

• Specific numerical performance requirements for "alarms, controls, and monitors" are not detailed. For example, acceptable ranges for pressure, volume, flow, response times for alarms, accuracy of delivered gas mixes, etc., are not provided.
• The actual measured performance values from the testing are not presented.

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

This information is not provided in the given text.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not provided in the given text. The device is a ventilator, not an imaging or diagnostic device requiring expert interpretation for ground truth. The "ground truth" (or functional verification) would be against engineering specifications and physical measurements, rather than expert consensus on medical images or diagnoses.

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

This information is not provided in the given text. As this is a ventilator and not a diagnostic device relying on expert interpretation, an adjudication method in the traditional sense (e.g., for medical image reading) would not be applicable. The "adjudication" would be against engineering standards and test pass/fail criteria.

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, a multi-reader, multi-case comparative effectiveness study was not done. This type of study is relevant for AI-powered diagnostic devices where human readers interpret medical images or data. The AVEA Ventilator is a treatment device, not a diagnostic one involving human interpretation of AI output.

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

The device itself is a standalone medical device (a ventilator), but it's not an AI algorithm in the context typically discussed for standalone performance studies (e.g., deep learning models making diagnoses). The "performance testing" described involved the device functioning independently, but it's not an AI algorithm in the modern sense. The device does provide "continuous respiratory support," implying its operation without constant direct human intervention once set up, which could be considered "standalone" in its functional capacity.

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

Given the nature of the device (a ventilator), the "ground truth" for performance testing would likely be:

• Engineering specifications and standards: The device's output (e.g., delivered volume, pressure, flow rates, alarm functionality, response times) would be compared against predefined engineering and medical device standards.
• Physical measurements: Using calibrated equipment to measure the actual performance parameters (e.g., gas flow, pressure, oxygen concentration) produced by the ventilator.

The text does not explicitly state the type of ground truth, but implicitly it relates to verifying compliance with performance requirements for "alarms, controls, and monitors."

8. The sample size for the training set

This information is not applicable and not provided. The AVEA Ventilator, described as a "servo-controlled, software-driven ventilator," from 2002, predates the widespread use of machine learning/AI models that would require "training sets" in the modern sense. Its software likely operates based on programmed logic and control algorithms rather than learned patterns from a "training set."

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

This information is not applicable and not provided, as there is no indication of a training set being used in the context of machine learning/AI for this device.

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