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Use of the KION Anesthesia Workstation is indicated in order to allow for the provision of anesthesia to the neonatal to adult patient populations in all ventilation modes except for Volume Control in the Circle System, where it is not intended for use on neonates. It is intended to be used in an environment where patient care is provided by Healthcare professionals, trained in the administration of anesthesia, when the professional determines that a device is required to assist the breathing of a patient undergoing anesthesia. The device can be used to administer anesthesia while controlling the entire ventilation for patients without any ability to breath, as well as supporting patients with reduced ability to breath.

The KION Anesthesia Workstation - Extended Modes Functionality, adds primarily new functions for Pressure Support and Non-rebreathing to the already cleared KION Anesthesia Workstation with Pressure Control Functionality.

The provided text is related to a 510(k) summary for the KION Anesthesia Workstation - Extended Modes Functionality. This document, like many 510(k) submissions for medical devices, focuses on demonstrating substantial equivalence to a predicate device rather than presenting a performance study with detailed acceptance criteria and expert-adjudicated ground truth data typically found in AI/ML device submissions.

Therefore, most of the requested information regarding acceptance criteria, study details, sample sizes, expert involvement, and ground truth establishment cannot be extracted from the provided text because it describes a traditional medical device submission, not an AI/ML diagnostic system. The document does not describe a study involving algorithms, human readers, or performance metrics like sensitivity, specificity, or AUC against a ground truth dataset in the way an AI/ML device would.

Here's an analysis based on the information that is present:

Acceptance Criteria and Device Performance

The concept of "acceptance criteria" in this document is related to demonstrating substantial equivalence to legally marketed predicate devices, rather than meeting specific quantifiable performance metrics against a clinical ground truth as would be seen for an AI/ML device. The device's "performance" is assessed in terms of its clinical performance being "equivalent" to the predicate devices.

Study Details

The document describes a 510(k) submission for the KION Anesthesia Workstation - Extended Modes Functionality, claiming substantial equivalence to predicate devices. It does not describe a clinical study in the typical sense of evaluating diagnostic performance with a test set, ground truth, or human-AI comparison.

1. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable. The document does not describe a "test set" for performance evaluation in the context of an AI/ML device. The comparison is against predicate devices based on technological characteristics and intended use.
2. 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. There is no mention of establishing ground truth by experts for a test set. This is a traditional medical device submission.
3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
4. 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 device is an anesthesia workstation, not an AI-assisted diagnostic tool for human readers.
5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an algorithmic diagnostic device.
6. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable. The "ground truth" for this submission is implicitly the established safety and effectiveness of the legally marketed predicate devices, against which equivalence is claimed.
7. The sample size for the training set: Not applicable. This is not an AI/ML device requiring a training set.
8. How the ground truth for the training set was established: Not applicable.

In summary, the provided text concerns a traditional 510(k) submission for an anesthesia workstation, focusing on demonstrating substantial equivalence to predicate devices based on design, intended use, and technological characteristics. It does not provide the type of performance study details requested for an AI/ML diagnostic or assistive device.

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The Servo' Ventilator System is intended for treatment and monitoring of patients in the range of neonates, infants and adults with respiratory failure or respiratory insufficiency. Servo' is a ventilator system to be used only by health care providers in hospitals or health care facilities and for in-hospital transport.

The ventilator is a ventilator with several selectable modes to individually monitor and treat patients whom needs respiratory assistance. The ventilator is the same as described in the notification K010925 and K022132 (addition of BiVent mode and CO2 module).

The provided text is a 510(k) summary for a Special 510(k) submission for the Servo-i Ventilator System. It focuses on a minor modification to the device, specifically changing the low minute volume alarm for infants. Crucially, the document does not contain information about acceptance criteria, device performance studies, sample sizes, ground truth establishment, or expert involvement as requested.

The 510(k) submission is for a modification to an existing device (Servo-i Ventilator System) and claims substantial equivalence to previously cleared devices (K010925 and K022132 for Servo-i, and K970839 for Servo Ventilator 300 A). The core of this particular submission is that the low minute volume alarm for infants has been changed from 0.10 l/min to 0.06 l/min, to match that of the predicate device, the SV300 ventilator.

Therefore, I cannot provide the requested information from the given text.

This document is a regulatory submission for a minor device change, not a detailed study report on novel device performance or AI/algorithm effectiveness. Such a document would typically not contain the robust clinical study data requested.

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The Siemens Medical Information Bus (MIB/MIB II and MIB Duo) Protocol Converters are intended for use in an environment where patient care is provided by healthcare professionals (Physician, Nurse, Technician) when the professional determines that a third party medical device that provides data, such as: Siemens SV 300 ventilator, Siemens Servoi Ventilator, Baxter Vigilance blood gas/continuous cardiac output monitor, Siemens SV900 ventilator, Draeger Evita II ventilator, Draeger Evita IV ventilator, Draeger Babylog ventilator, Puritan Bennett 7200 ventilator, Draeger Narkomed II Anesthesia System, Draeger Narkomed IV Anesthesia System, Draeger Julian Anesthesia Machine, Ohmeda 7900 Anesthesia Machine, Abbott Oximetrix 3 Blood Gas Analyzer, AVL Medical Instruments: Opti Critical Care Analyzer Portable Blood Gas Analyzer, Optical Sensors Inc.: OSI - Optical CAM, VIA Medical: VIA V-ABG1 Blood Gas Chemistry Monitor; Aspect A-2000 BIS Monitor should be connected to a Siemens INFINITY Modular Monitor for display.

The Medical Information Bus (MIB) Protocol Converters have received six 510(k) clearances for connectivity with third party devices. Minor software modifications have been implemented in the MIB/MIB II and MIB Duo protocol converters, and a device specific accessory cable is now available that allows an interface connection for Siemens Servoi Ventilator (K010925) to the INFINITY modular moritors (SC 9000/SC7000/SC9000XL/SC8000). This connection enables the display of Servoi Ventilator data on an INFINITY modular monitor and on the VentCentral display (K003246) of the MultiView WorkStation.

This document is a 510(k) summary for the Siemens Medical Information Bus (MIB, MIB II, MIB Duo) Protocol Converter. It outlines the device's purpose, previous clearances, and intended use. The content focuses on regulatory compliance and equivalence to predicate devices, rather than a detailed study demonstrating performance against specific medical acceptance criteria.

Therefore, many of the requested fields cannot be directly extracted or inferred from this document.

Here's an assessment based on the provided text:

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

The document does not specify quantitative acceptance criteria or detailed device performance metrics in the way a clinical study would for diagnostic accuracy or treatment efficacy. The assessment for equivalence is based on "non-clinical performance data" without explicit criteria or results mentioned.

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

Not applicable. This document is a regulatory submission for a protocol converter, not a clinical study involving patient data or a test set in the traditional sense for evaluating diagnostic or therapeutic efficacy. The "non-clinical performance data for equivalence" refers to technical characteristics and functionality.

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. No ground truth establishment by medical experts is described, as this is a device for data integration, not for making clinical diagnoses or treatment decisions itself.

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

Not applicable. No adjudication method is 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 device is a protocol converter, not an AI-assisted diagnostic or therapeutic tool. No MRMC study or AI-related performance improvements are mentioned.

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

Not applicable. This device is not an algorithm for standalone performance. Its function is to facilitate data display for human healthcare professionals.

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

Not applicable. Ground truth, in the context of diagnostic accuracy, is not relevant to this device's function as a protocol converter.

8. The sample size for the training set

Not applicable. There is no mention of a "training set" as this is not a machine learning or AI device.

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

Not applicable. There is no training set mentioned.

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