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The QEVO System is intended for viewing internal surgical sites during general surgical procedures and for use in visualization of ventricles and structures within the brain during neurological surgical procedures as well as for viewing internal surgical sites during anterior spinal procedures, such as nucleotomy, discectory, and foraminotomy.

The QEVO System comprises of the QEVO ECU (Endoscope Control Unit) and QEVO endoscope. The system is intended for viewing internal surgical sites and for use in visualization during general and certain neurosurgical and spinal procedures. The QEVO System has to be installed and integrated with a host display device (surgical microscope, a monitor, etc). Requirements for physical integration, connectivity, power supply, display resolution, and software integration are established and tested.

The provided document is a 510(k) premarket notification summary for the QEVO System, declaring its substantial equivalence to a predicate device (QEVO System with KINEVO 900). This type of submission focuses on demonstrating that a new device is as safe and effective as a legally marketed device, primarily by showing similar technological characteristics and intended use.

Crucially, this document does NOT contain information about acceptance criteria or a study proving the device meets those criteria in the context of AI/ML performance testing. The "Summary of Studies" section only mentions:

• Sterilization and Shelf Life: The device is reusable and the reprocessing instructions are identical to the predicate device.
• Biocompatibility: Testing was done in accordance with ISO 10993 for the patient-contacting component (insertion tube).
• Performance Testing - Bench: Optical safety was assessed according to IEC 62471:2006. It explicitly states: "The determination of substantial equivalence was not based on an assessment of performance data." This indicates that no clinical performance study (like an MRMC study or standalone algorithm performance) was submitted or required for this 510(k) clearance, as the device is an endoscopic visualization system, not an AI/ML diagnostic tool.

Therefore, I cannot extract the information required to answer your prompt because the provided text pertains to a traditional medical device (an endoscope system) clearance, not an AI/ML-driven device that would involve the rigorous testing methodologies you've asked about (e.g., ground truth, reader studies, test set sizes, etc.).

To summarize why I cannot provide the requested information based on the given text:

• No AI/ML Component: The QEVO System is described as a visualization system (endoscope) that displays images. There's no mention of an embedded AI/ML algorithm for image analysis, diagnosis, or decision support.
• No Performance Data for Clinical Effectiveness: The submission explicitly states that the substantial equivalence determination was not based on performance data. This implies a reliance on technological similarity to the predicate and standard bench testing for safety (electrical, optical) and functionality (image resolution, field of view, etc.).
• Focus on Substantial Equivalence: The entire document is about demonstrating that the new QEVO System is "substantially equivalent" to an existing predicate device, primarily by comparing their specifications and intended use, rather than proving a new diagnostic capability through clinical performance studies.

If you have a document related to an AI/ML medical device, please provide that, and I would be able to address your specific questions about acceptance criteria, study design, and ground truth establishment.

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The QEVO System with KINEVO 900 is intended for viewing internal surgical sites during general surgical procedures and for use in visualization of ventricles and structures within the brain during neurological surgical procedures as well as for viewing internal surgical sites during anterior and posterior spinal procedures, such as nucleotomy, discectomy, and foraminotomy.

The subject device is a system used for viewing internal surgical sites during surgical procedures. The system consists of the following components:
. QEVO System - QEVO Endoscope Control Unit (ECU) + QEVO endoscope
KINEVO 900 surgical microscope .
The KINEVO 900 can be operated by the surgeon and surgical assistant as a standard optical surgical microscope, using tubes and evepieces to view the surgical anatomy. When used with the QEVO System, the KINEVO 900 provides the additional capability to view images from the hand-held QEVO endoscope during neurological and spinal procedures.
The KINEVO 900 is physically connected (via power cable, HDMI/DVI cable, and Ethernet cable) to the QEVO Endoscope Control Unit (ECU). The QEVO endoscope plugs into the QEVO ECU outlet provided on the KINEVO 900.

This document does not contain information about acceptance criteria and a study proving the device meets those criteria in the traditional sense of a clinical trial or performance study with defined endpoints for diagnostic accuracy, sensitivity, or specificity. Instead, the document is a 510(k) summary for a medical device (QEVO System with KINEVO 900), focusing on demonstrating substantial equivalence to a predicate device.

The "studies" mentioned are primarily non-clinical performance tests designed to show the device's compliance with established safety, electrical, and physical standards, rather than evaluating its clinical effectiveness or diagnostic accuracy against a ground truth.

Here's an analysis based on the provided text, addressing the points you requested where information is available:

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

The document does not explicitly present "acceptance criteria" in terms of diagnostic performance metrics (e.g., sensitivity, specificity, accuracy) or clinical outcomes. Instead, the "performance" is reported as compliance with various standards and safety requirements.

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 document for any of the non-clinical tests. The number of devices or components tested, or the origin of any data, is not specified.

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 applicable as the document describes non-clinical engineering and safety tests, not clinical evaluations requiring expert interpretation or ground truth establishment in a medical context.

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

This information is not applicable as the document describes non-clinical engineering and safety tests, not clinical evaluations requiring adjudication.

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 comparative effectiveness study was not done. The document explicitly states: "Clinical Testing: Not applicable. Clinical studies are not necessary to establish the substantial equivalence of this device." This device is an endoscope/microscope visualization system, not an AI-powered diagnostic tool.

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

No, a standalone performance study in the context of an algorithm's diagnostic accuracy was not done. The device is a visualization system and does not appear to incorporate an AI algorithm for diagnostic purposes.

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

For the non-clinical tests described, the "ground truth" is typically defined by the standards themselves (e.g., sterilization efficacy requirements, electrical safety limits, biocompatibility criteria). Chemical, physical, and biological analyses are performed against these predetermined thresholds or methods, rather than against clinical ground truth like pathology or expert consensus.

8. The sample size for the training set

This information is not applicable. The device is a physical medical device (visualization system), not an AI algorithm requiring a training set in the machine learning sense.

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

This information is not applicable for the reasons stated in point 8.

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