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The S9 Greenhills is indicated for the treatment of patients weighing more than 66 lb (30 kg) with obstructive sleep annea (OSA), central and/or mixed apneas, or periodic breathing.

It is intended for home and hospital use.

The hunidifier is intended for single patient use in the home environment and re-use in a hospital/institutional environment.

The S9 Greenhills is similar to the predicate device(s), using a blower based positive pressure system with an integrated heated humidifier and heater controller. The device platform is identical to the S9 VPAP Adapt (K113801) and contains a Micro-processor controlled blower system that generates controlled positive airway pressure between 3-25 cmH2O. The system comprises the flow generator, patient tubing, mask (patient interface) and humidifier.

The S9 Greenhills is a flow generator device designed to provide adaptive servo-ventilation therapy (ASV mode) or (ASVAuto mode) to stabilize a patient's ventilation during sleep. The device continually measures the patient's instantaneous ventilation, and calculates a target ventilation based on to the patient's recent average minute ventilation. It then adjusts the degree of pressure support to servo-control the patilation to at least equal the target ventilation. The same is true for ASVAuto mode except the EPAP is adjusted to address any obstructive apneas detected.

Therapy modes contained in the S9 Greenhills are CPAP, ASV, and ASVAuto and unchanged from the S9 VPAP Adapt (K102586). They are:

• . CPAP mode - the device delivers a continuous positive airway pressure throughout the entire therapy session;
• ASV mode the device automatically adjusts pressure support in response to the . patient's recent average minute ventilation; and
• ASVAuto mode the device automatically adjusts pressure support in response to . the patient's recent average minute ventilation and EPAP level for OSA events.

The functional characteristics of the S9 Greenhills system includes all the clinician and user friendly features of the predicate device which have been verified during usability studies in accordance with IEC 62366 Medical devices - Application of usability engineering to medical devices.

The provided 510(k) summary for the ResMed S9 Greenhills device describes its acceptance criteria and the study conducted to demonstrate its performance. However, it's important to note that this submission focuses on demonstrating substantial equivalence to a predicate device, not on proving overall clinical effectiveness from scratch. Therefore, the "study" is primarily comparative bench testing, and some of the requested information (like human reader performance with/without AI assistance, or expert consensus for ground truth on a large test set) is not applicable or not provided in this type of submission.

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

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not provide a direct table of specific numerical acceptance criteria with corresponding performance metrics like a typical clinical trial report. Instead, it states that "All tests (predicate bench testing) confirmed the product met the predetermined acceptance criteria." and "Side-by-Side bench testing was performed to verify that the S9 Greenhills met the predetermined pass/fail requirements of the S9 Greenhills System Specification when compared to the predicate devices."

The key performance characteristics compared, which inherently define the acceptance criteria for substantial equivalence, are:

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

• Sample Size for Test Set: The document refers to "Side-by-Side bench testing" using "a breathing machine simulates patient breathing patterns." No specific number of simulated patient breathing patterns or cases are quantified.
• Data Provenance: The testing is described as "bench testing" and is therefore synthetic data generated by a breathing machine, not human patient data. There is no country of origin of data or retrospective/prospective designation in the clinical sense, as it is a bench study.

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

This information is not applicable and not provided. The "ground truth" for this bench testing is the expected performance of a well-functioning CPAP/ASV device, primarily benchmarked against the predicate device's known performance characteristics. The tests verify engineering specifications, not clinical diagnoses that would require expert consensus.

4. Adjudication Method for the Test Set

Not applicable. This was a bench test against predetermined engineering specifications and comparison to predicate device performance. No human adjudication of clinical outcomes or images was involved.

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 ventilation device, not a diagnostic imaging device using AI to assist human readers. Therefore, an MRMC study comparing AI-assisted vs. non-AI-assisted human reader performance is not relevant to this submission.

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

The device itself is a standalone medical device that delivers therapy based on internal algorithms and sensor inputs. The "bench testing" described evaluates the performance of these algorithms and the device's hardware independently. While not explicitly framed as an "algorithm only" study in the AI context, the bench tests verify the device's automated functioning.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

The "ground truth" for this study is primarily:

• Engineering Specifications: Predetermined pass/fail criteria from the "S9 Greenhills System Specification."
• Predicate Device Performance: The known and established performance of the S9 VPAP Adapt (K113801) is used as the benchmark for comparison, establishing "substantial equivalence." The "breathing machine simulates patient breathing patterns" to test the device's response against expected physiological responses.

8. The Sample Size for the Training Set

Not applicable. This medical device primarily uses control algorithms based on established physiological principles and engineered specifications, not machine learning algorithms that require a distinct "training set" of data in the AI sense.

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

Not applicable, as there is no "training set" in the context of an AI/machine learning algorithm for this device. The device's operating principles and algorithms are based on established medical science for respiratory support.

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The VPAP Adapt is indicated for the treatment of patients weighing more than 66 lb (30 kg) with obstructive sleep apnea (OSA), central and/or mixed apneas, or periodic breathing. It is intended for home and hospital use.

VPAP Adapt System (VPAP Adapt with H5) is similar to the predicate device(s), using a blower based positive pressure system with an integrated heated humidfier and heater controller. The device platform is identical to the S9 VPAP Adapt (K102586) and contains a Micro-processor controlled blower system that generates controlled positive airway pressure between 3-25 cmHzO as required to maintain an "air splint" for effective treatment of OSA. The system comprises the flow generator, patient interface) and humidifier.

The VPAP Adapt is a flow generator device designed to provide adaptive servo-ventilation therapy (ASV) to stabilize a patient's ventilation during sleep. The device continually measures the patient's instantaneous ventilation, and calculates a target ventilation based on to the patient's recent average. It then adjusts the degree of pressure support to servo-control the patient's ventilation to at least equal the target ventilation. Therapy modes contained in the VPAP Adapt are CPAP, ASV, and ASVAuto. CPAP and ASV therapy modes come from the S9 VPAP Adapt (K102586).

The functional characteristics of the VPAP Adapt system includes all the clinician and user friendly features of the predicate device.

The provided text is a 510(k) summary for the ResMed VPAP Adapt device, a non-continuous ventilator. The document focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed study of the device's performance against specific acceptance criteria in a clinical setting.

Based on the information provided, here's a breakdown of the requested points:

Device: VPAP Adapt (Non-continuous ventilator)

1. Table of Acceptance Criteria and Reported Device Performance

The document does not provide a table of clinical acceptance criteria (e.g., AHI reduction, oxygen saturation improvement) with corresponding device performance metrics. Instead, it refers to "predetermined acceptance criteria" for bench testing demonstrating the ASVAuto algorithm's specification and the device's adherence to safety and performance standards.

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

• Test Set Sample Size: Not applicable. The "tests" mentioned are bench tests using "closed-loop and open-loop test scripts from patient models," not human subjects or a clinical test set. Therefore, there's no patient sample size.
• Data Provenance: Not applicable, as no clinical data from human subjects is mentioned for the verification activities. The "patient models" used in bench testing would be simulated data or hardware models.

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

Not applicable. The "ground truth" for the bench tests was based on the device's own specifications and the performance of the predicate device (S9 VPAP Adapt K102586). No human experts were used to establish ground truth for a test set in the clinical sense.

4. Adjudication Method for the Test Set

Not applicable. There was no clinical test set requiring human adjudication.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned or conducted. The document focuses on demonstrating substantial equivalence through bench testing to a predicate device, not on comparing human reader performance with and without AI assistance.

6. Standalone (Algorithm Only) Performance Study

Yes, in a way. The "Non-Clinical Testing" section describes "extensive bench testing using both closed-loop and open-loop test scripts from patient models designed to verify that the ASVAuto algorithm in the VPAP Adapt performs to specification." This is a form of standalone testing of the algorithm within the device against preset criteria. It's not a clinical standalone study, but an engineering verification.

7. Type of Ground Truth Used for the Testing

The ground truth for the bench testing was the design specification of the ASVAuto algorithm and the performance characteristics of the predicate device (S9 VPAP Adapt K102586). It's based on engineering requirements and existing device performance, not clinical pathology, expert consensus, or outcomes data from human subjects.

8. Sample Size for the Training Set

Not applicable. The document does not describe any machine learning training processes or a "training set" in the context of AI. The device's algorithm appears to be rule-based or pre-programmed, not learned from a dataset.

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

Not applicable, as no training set is mentioned for an AI/machine learning model. The algorithm's behavior is described as "continually measures the patient's instantaneous ventilation, and calculates a target ventilation based on to the patient's recent average. It then adjusts the degree of pressure support to servo-control the patient's ventilation." This implies a deterministic, pre-defined control algorithm, not one developed through machine learning.

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