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The Cube Navigation System and its components are intended as an aid for precisely locating anatomical structures in either open or percutaneous procedures. The Cube Navigation System is indicated for any medical condition in which the use of stereotactic surgery may be appropriate, and where reference to a rigid anatomical structure in the field of ENT surgery, such as the paranasal sinuses, mastoid anatomy, can be identified relative to a CT or MR based model of the anatomy.

Example procedures include, but are not limited to:

ENT Procedures;
Transphenoidal access procedures.
Intranasal procedures.
Sinus procedures, such as Maxillary antrostomies, Sphenoidotomies/Sphenoid explorations, Turbinate resections, and Frontal sinusotomies.
ENT related anterior skull base procedures

The Cube Navigation System is an image guided surgery system, visualizing instrument positions on preoperative scans (e.g., CT, MRI, fluoroscopy) utilizing electromagnetic tracking technology. The position of the instrument with integrated sensor and the patient equipped with localizers are localized within an electromagnetic field, generated by a field generator, called navigation sensor within the Cube navigation system. The principle of navigation is based on electromagnetic spatial measuring of localizer element in a generated electromagnetic field.

The display of navigation information requires an image-to-patient registration procedure. During registration procedure, the navigation system determines the coordinate transformation between the intraoperative position of the patient and the position of the preoperative scan by surface matching performed by the user either tactile using an navigated instrument or non-tactile using the registration device VituEye. Thereafter the spation of the instrument is displayed superimposed to the image data. The navigation information is updated with a rate of 15 to 45 Hz.

The provided text is a 510(k) Summary for the Fiagon Cube Navigation System. It details the device's technical specifications, intended use, and comparison to predicate devices, focusing on demonstrating substantial equivalence rather than a full clinical study with acceptance criteria and detailed performance of an AI-driven vision system.

Therefore, the document does not contain the specific information requested regarding acceptance criteria related to AI performance, sample sizes for test sets (beyond general bench testing), data provenance, number of experts for ground truth, adjudication methods, MRMC study details, standalone algorithm performance, or ground truth establishment for training and test sets in an AI context.

The document primarily focuses on the device's accuracy as a navigation system, but this is a mechanical/electrical performance metric, not an AI diagnostic performance metric.

However, I can extract the relevant acceptance criterion and the reported performance for the device's accuracy:

Acceptance Criteria and Reported Device Performance (as related to accuracy):

Based on the provided text, the following information is NOT available:

• Sample size used for the test set and the data provenance: The document mentions "bench testing" but does not specify the sample size (e.g., number of measurements, number of anatomical models tested) or data provenance (e.g., country of origin, retrospective/prospective).
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: This is not relevant to the described bench accuracy testing of a navigation system, as ground truth for AI image analysis is not being established.
• Adjudication method for the test set: Not applicable for device accuracy testing.
• 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, as this device is a surgical navigation system, not an AI diagnostic/detection system designed to assist human readers.
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The accuracy testing described is effectively a standalone performance test of the navigation system. However, it's not "algorithm only" in the sense of a pure AI diagnostic algorithm.
• The type of ground truth used: For the accuracy testing, the ground truth would be the precisely known physical positions in the bench test setup, measured by calibrated instruments. It's not expert consensus, pathology, or outcomes data.
• The sample size for the training set: Not applicable, as this is not an AI machine learning model being trained.
• How the ground truth for the training set was established: Not applicable.

Summary of available information from the text that partially addresses the prompt:

• Acceptance Criteria for Accuracy: Mean position accuracy < 1.5mm.
• Reported Performance: Implicitly meets the < 1.5mm requirement, confirmed by bench testing.
• Study Type: Non-clinical bench testing, including "Anatomy orientated accuracy bench testing for each instrument probe and localizer without registration" and "Anatomy orientated accuracy bench testing after tactile registration photo based, non-tactile registration."
• Ground Truth (for accuracy testing): Precisely known physical positions in a controlled bench test environment.

The document states that the system's "tracking technology, system components, registration methods, and system functionality however are identical to the primary predicate." This suggests that the performance metrics of the previous devices serve as the benchmark for substantial equivalence. The modifications described are primarily dimensional, aesthetic, or related to updated internal hardware, with no new safety or effectiveness concerns raised.

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