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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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