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The Nihon Kohden NKV-550 Series Ventilator System is intended to provide continuous ventilation for adult, pediatric and neonatal patients who require invasive or noninvasive respiratory support. The NKV-550 offers mandatory and spontaneous ventilation modes as well as respiratory monitoring. The NKV-550 is intended for use in hospitals and hospital-type facilities, as well as, for in-hospital transportation.

The Nihon Kohden NKV-550 Series Ventilator System consists of a graphic user interface (GUI) and a breath delivery unit (BDU). The GUI allows clinicians to set ventilator control parameters such as tidal volume and inspiratory pressure, to set alarm limits such as high inspiratory pressure alarm, to view monitored numeric values, to view waveform and loops, and to operate various features through the apps. The BDU contains a microprocessor that receives inputs from the electronic system and controls the pneumatic system for breath delivery to the patient. It also provides various alarms, a safety valve, and other design features to maximize patient safety.

This document, K192307, is a 510(k) premarket notification for a medical device (Nihon Kohden NKV-550 Series Ventilator System). It asserts substantial equivalence to a predicate device. As such, the FDA does not require a clinical study with human patients to demonstrate device performance against specific acceptance criteria for a new AI/ML algorithm. Instead, the focus is on demonstrating that the revised device meets the same performance and safety standards as the predicate device.

Therefore, the requested information about "acceptance criteria" and "study that proves the device meets the acceptance criteria" in the context of an AI/ML algorithm (including sample sizes, ground truth establishment, expert adjudication, MRMC studies, and standalone performance) is not applicable to this submission. This submission is for a ventilator, not an AI/ML diagnostic or therapeutic device.

The document discusses non-clinical performance data and states that clinical performance data was not required to demonstrate substantial equivalence.

Here's a breakdown of why the specific questions about an AI/ML study can't be answered from this document:

• 1. A table of acceptance criteria and the reported device performance: The document does not provide a table of performance acceptance criteria in the context of an AI/ML algorithm's output. It mentions "Met ISO 80601-2-12 requirements on essential performance of critical care ventilator," which refers to international safety and performance standards for ventilators, not an AI's diagnostic accuracy.
• 2. Sample sizes used for the test set and the data provenance: Not applicable. There is no AI/ML test set.
• 3. Number of experts used to establish the ground truth for the test set and the qualifications: Not applicable. There is no AI/ML ground truth establishment.
• 4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable.
• 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-assisted device.
• 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.
• 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable.
• 8. The sample size for the training set: Not applicable. There is no AI/ML training set.
• 9. How the ground truth for the training set was established: Not applicable.

What the document does provide regarding device performance:

The document states that the performance of the Nihon Kohden NKV-550 Series Ventilator System was demonstrated by various non-clinical tests performed in compliance with Design Controls:

• Software Verification
• Electrical Safety & EMC Testing
• Device Functionality Testing
• Performance of Therapy Types and Ventilation Modes
• Environmental Testing
• Cleaning & Disinfection
• Risk Management
• Accessory Compatibility
• Regression Testing

The key "performance" criterion mentioned is meeting ISO 80601-2-12 requirements on essential performance of critical care ventilators. This is a general safety and effectiveness standard for the device's physical and functional operation, not related to an AI's analytical accuracy.

In summary, this 510(k) submission is for a conventional medical device (a ventilator) with minor modifications, and as such, the detailed clinical study and AI/ML-specific acceptance criteria information requested is not part of this type of regulatory submission.

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The bellavista 1000/1000e ventilator is intended to provide positive pressure ventilatory support to adult and pediatric patients and optionally infant and neonatal patients by qualified, trained personnel under the direction of a physician.

Environment of use: hospitals, sub-acute care facilities and intra-hospital transfer

When used on neonatal patients: The environment of use is the Neonatal Intensive Care Unit (NICU).

bellavista is an electronically controlled pneumatic ventilation system. It is powered by AC or DC and also provided with an internal battery. The bellavista pneumatic system supplies respiratory gas whilst the electrical systems control the pneumatics and provides the power supply.

The user can enter values or parameters in the bellavista via the touch screen. These inputs entail instructions for bellavista's pneumatic system to ventilate the patient with a precisely controlled gas mixture. bellavista gathers readings from the proximal flow sensor within the ventilator.

I am sorry, but the provided text does not contain the information required to answer your request. The document is an FDA 510(k) clearance letter and summary for a medical device (ventilator). It describes the device, its intended use, a comparison to predicate devices, and the basis for substantial equivalence.

However, it does not include details about acceptance criteria, specific device performance numerical results, sample sizes for test or training sets, data provenance, expert qualifications, ground truth establishment, or any details about a multi-reader multi-case (MRMC) study or standalone algorithm performance, as these are typically part of a clinical validation study and not usually found in a 510(k) summary for a ventilator.

The document discusses the ventilatory support capabilities and patient populations for which the device is intended, but not the kind of detailed performance metrics usually required for AI/algorithm-based diagnostic devices.

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The Unity Network ID is indicated for use in data collection and clinical information management through networks with independent bedside devices. The Unity Network ID is not intended for monitoring purposes, nor is the Unity Network ID intended to control any of the clinical devices (independent bedside devices/ information systems) it is connected to.

The Unity Network ID system communicates patient data from sources other than GE Medical Systems Information Technologies, Inc. equipment to a clinical information system, central station, and/or GE Medical Systems Information Technologies Inc. patient monitors.

The Unity Network ID acquires digital data from eight serial ports, converts the data to Unity Network protocols, and transmits the data over the monitoring network to a Unity Network device such as a patient monitor, clinical information system or central station.

The provided document is a 510(k) summary for the GE Healthcare Unity Network ID V7. It describes a data collection and clinical information management system.

Here's an analysis of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly list specific quantitative acceptance criteria (e.g., accuracy, sensitivity, specificity) for the Unity Network ID V7, nor does it present device performance against such. Instead, it focuses on demonstrating that the device meets design specifications and complies with applicable voluntary standards.

The "Determination of Substantial Equivalence: Summary of Non-Clinical Tests" section indicates: "The Unity Network ID V7 and its applications were tested to, and comply with, applicable voluntary standards. The Unity Network ID V7 was tested to assure that the device meets its design specifications."

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

The document does not specify a "test set" in the context of clinical or performance data from a specific dataset of patients or cases. The testing described is primarily software and hardware verification and validation, rather than an evaluation against a clinical dataset. Therefore, there is no mention of sample size or data provenance (country of origin, retrospective/prospective).

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

Since there is no "test set" based on patient data, there is no mention of experts needed to establish ground truth or their qualifications. The "ground truth" in this context refers to the successful functionality and compliance of the device against its specifications and standards.

4. Adjudication Method for the Test Set

Not applicable, as there is no mention of a test set requiring adjudication by experts.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and Effect Size

No, an MRMC comparative effectiveness study was not done. The device is a data collection and management system, not an interpretive diagnostic tool that involves human readers interpreting clinical output. The document explicitly states: "The subject of this premarket submission, Unity Network ID V7, did not require clinical studies to support substantial equivalence."

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

The "testing included all new or modified features" and involved various quality assurance measures like unit-level testing, integration testing, performance testing, and safety testing. These tests would evaluate the algorithm's functionality and accuracy in its intended role of data conversion and transmission. So, in essence, the "standalone" performance of the data conversion and routing algorithms was assessed as part of these non-clinical tests. However, it's not a "standalone performance study" in the typical sense of evaluating diagnostic accuracy.

7. The Type of Ground Truth Used

The "ground truth" for the non-clinical tests appears to be:

• Design specifications: The device's output and functionality were compared against predefined technical and functional specifications.
• Voluntary standards: Compliance with relevant engineering and medical device standards (though specific standards are not listed in this summary, they are implied).
• Expected behavior: For simulated use testing and verification, the "ground truth" would be the expected correct data conversion and transmission as per the device's design and independent bedside device protocols.

8. The Sample Size for the Training Set

Not applicable. This device is a data integration and conversion system, not an AI/ML model that requires a "training set" in the typical sense of machine learning for image analysis or diagnostics. Its functionality is based on established communication protocols and data mapping.

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

Not applicable, as there is no training set for this type of device.

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The INOmax DS delivery system delivers INOmax® (nitric oxide of inhalation) therapy gas into the inspiratory limb of the patient breathing circuit in a way that provides a constant concentration of nitric oxide (NO), as set by the user, to the patient throughout the inspired breath. It uses a specially designed injector module, which enables tracking of the ventilator waveforms and the delivery of a synchronized and proportional dose of NO. It may be used with most ventilators.

The INOmax DS provides continuous integrated monitoring of inspired O2, NO3, and NO, and a comprehensive alarm system.

The INOmax DS incorporates a battery that provides up to 6 hours of uninterrupted NO delivery in the absence of an external power source.

The INOmax DS includes a backup NO delivery capability that provides a fixed flow of 250 mL/min of NO which along with user supplied 10 L/min of oxygen provides 20 ppm in the gas flow to a patients breathing circuit. It may also use the INOblender for backup.

The target patient population is controlled by the drug labeling for INOmax and is currently neonates. The primary targeted clinical setting is the Neonatal Intensive Care Unit (NICU) and secondary targeted clinical setting is the transport of neonates.

The INOmax DSIR uses a "dual-channel" design to ensure the safe delivery of INOmax. The first channel has the delivery CPU, the flow controller and the injector module to ensure the accurate delivery of NO. The second channel is the monitoring system, which includes a separate monitor CPU, the gas cells (NO, NO2, and O2 cells) and the user interface including the display and alarms. The dual-channel approach to delivery and monitoring permits INOmax delivery independent of monitoring but also allows the monitoring system to shutdown INOmax delivery if it detects a fault in the delivery system such that the NO concentration could become greater than 100 ppm.

The provided document describes the INOmax DSIR delivery system for nitric oxide and its compatibility with additional ventilators. The study discussed is a non-clinical test to establish substantial equivalence for the updated device rather than an AI-powered diagnostic tool, therefore many of the requested criteria are not applicable.

Here's an analysis of the provided information, focusing on the relevant sections and explicitly stating when information is not applicable (N/A) for this type of device and study:

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)

• Sample Size for Test Set:
  • Five INOmax DSIR settings were used: 0 (baseline), 1, 5, 20, 40, and 80 ppm.
  • These settings were applied for each setting and mode of ventilation/patient cannula for the five additional respiratory care devices.
  • The "Backup mode" was also tested for each.
  • (Note: The document doesn't specify the number of times each setting/mode combination was tested or the duration of each test).
• Data Provenance: Not specified, but implied to be from laboratory testing related to the submitter (INO Therapeutics/Ikaria). The study is prospective in that it was conducted specifically for this submission.

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)

N/A. This was a non-clinical engineering and performance study of a medical device, not a diagnostic study requiring expert human interpretation of data for ground truth. The "ground truth" was the physical performance of the device against predefined technical specifications.

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

N/A. As this was a non-clinical performance study, there was no need for human adjudication of results in the way it's applied in diagnostic studies. The results were measured and recorded against established device specifications.

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

N/A. This study is not an MRMC comparative effectiveness study, nor does it involve AI assistance for human readers. It's a non-clinical compatibility and performance test of a medical device.

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

N/A. This device does not have an "algorithm only" component in the diagnostic sense that would be tested for standalone performance. The device itself is the "system" being tested.

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

The "ground truth" for this non-clinical study was the published specifications of the INOmax DSIR and the compatibility requirements for its operation with the selected ventilators. The testing aimed to demonstrate that the device performed within these established specifications.

8. The sample size for the training set

N/A. There is no mention of a training set as this is a non-clinical device performance study, not an AI or machine learning model development.

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

N/A. There is no training set for this type of non-clinical device performance study.

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