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The Neurovision Nerve LocatorMonitor is an electronic device consisting of a surgical nerve stimulator and an evoked EMG monitor with integrated detecting and warning capability. This device is intended for use in surgical procedures where motor nerves are at risk to assist the surgeon in locating these nerves. This device is intended for use only by qualified, trained medical practitioners who perform operative surgery and who fully understand that this device is only an adjuvant to proper surgical technique and good surgical judgment.

The EMGView software is an accessory to the Neurovision Nerve Locator/Monitor. When the dedicated USB output of the EMG unit is connected to a personal computer, the PC installed software provides status monitoring data, troubleshooting data, real-time EMG waveforms, and digital backup of all data obtained during clinical use of the device. The real-time display of EMG waveforms is intended to assist the surgeon with nerve integrity monitoring.

The Neurovision Nerve LocatorMonitor is an electronic device consisting of a surgical nerve stimulator and an evoked EMG monitor with integrated detecting and warning capability.

The EMGView software is an accessory to the Neurovision Nerve Locator/Monitor. When the dedicated USB output of the EMG unit is connected to a personal computer, the PC installed software provides status monitoring data, troubleshooting data, real-time EMG waveforms, and digital backup of all data obtained during clinical use of the device.

I am sorry, but the provided text does not contain the information required to answer your request regarding the acceptance criteria, study details, and performance of the Neurovision Nerve Locator Monitor (Nerveana) and EMGView Software. The document is an FDA 510(k) clearance letter, which confirms substantial equivalence to a predicate device and states the indications for use. It does not include:

• A table of acceptance criteria and reported device performance.
• Sample sizes for test sets, data provenance, or details about training sets.
• Information on ground truth establishment, expert qualifications, or adjudication methods.
• Details about MRMC comparative effectiveness studies or standalone algorithm performance.

Therefore, I cannot generate the requested table and paragraphs based on the provided input.

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The Neurovision Nerve Locator Monitor is an electronic device consisting of a surgical nerve stimulator and an evoked EMG Monitor with integrated detecting or warning capability. The device is intended to assist the surgeon in locating motor nerves that are at risk in surgical procedures.

The Neurovision Nerve Locator Monitor Model NV006 is a single channel evoked EMG device for surgical nerve location and monitoring. The device operates exactly as the predicate model NV004. The stimulation range for the predicate device (Model NV004) as a proactive nerve locator is up to 5.0 mA of pulsed nerve stimulation. An accessory cable adds optional "high level" stimulation (Model NV006) ranging from 3.0 to 12 mA.

Here's a summary of the acceptance criteria and the study information for the Neurovision Nerve Locator/Monitor Model NV006, based on the provided text:

Acceptance Criteria and Device Performance

Study Information:

1. Sample size used for the test set and data provenance:

   • The document states "Preclinical testing verified the design of this device and that all specified requirements were fulfilled." No specific details about the sample size of a test set (e.g., number of patients, cases, or data points) are provided.
   • The provenance of the data (country of origin, retrospective or prospective) is not explicitly mentioned. Given the nature of a 510(k) for a device like a nerve monitor, most "preclinical testing" would likely involve bench testing, animal models, or potentially limited human use studies that are not described in detail here.

2. Number of experts used to establish the ground truth for the test set and their qualifications:

   • This information is not provided in the document.

3. Adjudication method for the test set:

   • This information is not provided in the document.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance:

   • No MRMC comparative effectiveness study is mentioned. The device is a nerve locator/monitor, not an AI-assisted diagnostic imaging device requiring human reader interpretation in the context of typical MRMC studies.

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

   • The "Preclinical testing" mentioned implies standalone testing of the device's functionality. The document highlights various technical specifications and changes from the predicate device that would be subject to such testing (e.g., accuracy of delivered current, impedance measurement, alarm functions, stimulation output). However, no specific details of a formal "standalone" study are provided beyond the general statement.

6. The type of ground truth used:

   • Given that the preclinical testing verified the design and requirements, the ground truth would likely be based on:
     • Engineering specifications and standards: Conformance to ISO 60601-2-40 for stimulation output, accuracy within 5% of delivered current, and other technical requirements.
     • Functional verification: The device's ability to accurately provide impedance readings, generate anticipated audio alerts, and stimulate within specified ranges.
     • Comparison to predicate device performance: Showing that the NV006 performs at least as well as the NV004 for existing functions and that new functions (like high-stim) meet their design intent.

7. The sample size for the training set:

   • The device described is a medical instrument (hardware and embedded software) for nerve location and monitoring, not a machine learning or AI algorithm that typically has a "training set" in the conventional sense. The "logical algorithm" is described as the "Neurovision proactive nerve location algorithm," which is "Same" as the predicate device, implying it is a deterministic, rule-based algorithm rather than a learned model. Therefore, the concept of a "training set" as used for machine learning models does not apply here.

8. How the ground truth for the training set was established:

   • As explained above, the concept of a "training set" for a machine learning model does not apply to this device based on the provided information.

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