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The MR-Guide 2000 system is a frameless stereotactic guiding accessory for Magnetic Resonance (MR) systems. The system is MRI-compatible. It displays the simulated image of a rigid insertion instrument, such as a biopsy needle or an aspiration needle, on a computer monitor screen that also shows the MR image of the target organs and the projected future path of the interventional instrument, compensating for respiratory movements of the patient.

The device is intended to be used in clinical interventions and for anatomical structures where magnetic resonance is currently used for visualizing such structures.

The MR-Guide 2000 provides visual guiding information of the interventional instrument by overlaying graphics depicting its relative position and its predicted future path on the MR image of the internal organs all displayed on the monitor of a personal computer.

This document describes the UltraGuide MR-Guide 2000, a guiding system for interventional instruments used with Magnetic Resonance (MR) imaging.

Here's an analysis of the provided text in relation to acceptance criteria and study information:

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

The provided text does not explicitly state specific numerical acceptance criteria for performance (e.g., accuracy thresholds, precision values, sensitivity, specificity). Instead, it relies on a claim of "equivalence to predicate devices in safety and efficacy."

However, it does mention "Accuracy tests were done in phantoms." While no specific accuracy values are reported in this summary, the success of the 510(k) suggests that the accuracy achieved in phantom testing was deemed acceptable by the FDA for demonstrating substantial equivalence.

2. Sample size used for the test set and the data provenance

• Sample Size for Test Set: Not explicitly stated. The text mentions "Accuracy tests were done in phantoms." It does not specify the number of phantoms or the number of measurements taken within those tests.
• Data Provenance: The "Accuracy tests were done in phantoms" implies a synthetic/benchtop, experimental setting. The country of origin of this data is not specified, but the applicant is UltraGuide Ltd. located in Israel. The nature of the test is non-clinical/bench testing.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. For phantom accuracy tests, the "ground truth" is typically established by the known physical properties and measurements of the phantom itself, often using highly precise measurement tools, not human experts.

4. Adjudication method for the test set

Not applicable. As noted above, the ground truth for phantom tests is not typically established by human experts 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 MRMC comparative effectiveness study was performed or reported. The device is a guiding system, not an AI-assisted diagnostic tool that would typically involve human reader studies.

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

Yes, a form of standalone performance evaluation was done through the "Accuracy tests done in phantoms." This type of testing evaluates the device's inherent capability to track and display information accurately without direct human interaction influencing the measurement itself (though human operators would set up and execute the tests).

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

For the accuracy tests mentioned, the ground truth would be the known physical dimensions and positions within the phantoms, established through precise measurements independent of the device being tested.

8. The sample size for the training set

Not applicable. This device is a "frameless stereotactic guiding accessory" that uses a magnetic tracking system for real-time guidance. It is not an AI/machine learning algorithm that typically undergoes distinct "training" with a dataset in the way a diagnostic image analysis algorithm would. Its function is based on physical principles of magnetic tracking and geometric calculations, not learned patterns from a training dataset.

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

Not applicable, as there is no mention of a training set or AI/machine learning model in the traditional sense outlined in the filing.

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