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The Fiagon Navigation System is intended as an aid for locating anatomical structures in either open or percutaneous neurosurgical procedures. The Fiagon 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 cranial surgery can be identified relative to a CT or MR based model of the anatomy.

Example procedures include, but are not limited to:

Cranial Procedures:

• Craniotomies/Craniectomies (e.g., Tumor Resection)
• Skull Base Procedures
• Cranial Biopsies
• General Catheter Shunt Placement
• Pediatric Catheter Shunt Placement

The user should consult the "Bench Accuracy" section of the User Manual to assess whether the accuracy of the system is suitable for their needs.

The Fiagon Navigation System displays the position of the instruments in preoperative scans (e.g., CT, MRI, fluoroscopy) utilizing electromagnetic tracking technology. The instrument with integrated sensor and the patient equipped with localized within an electromagnetic field generated by a field generator. 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 fiducial marker, anatomical landmark or surface matching. Thereafter, the spatial position of the instrument is displayed superimposed to the image data. The navigation is updated with a rate of 15 to 45 Hz.

The components of the navigation system are

1. Navigation unit with Navigation software. It has interfaces for screen, mouse and the components 2 – 4 below.

• 2. Navigation sensor (Headrest with field generator)
• 3. Navigation instrument (ShuntPointer, BiopsyPointer190 and BiopsyPointer 250)

4. Patient reference localizer (Localizer Set Bone Anchor and Localizer Adhesive Pad)

The navigation unit is connected to a medical monitor. The unit runs the navigation software. Preoperative radiological images of the patient (DICOM CT, CBCT, MR) are imported to the system by means of CD-ROM, USB storage media or LAN network and displayed in appropriate way defined by the software.

The navigation unit includes the spatial measuring device electronics as well. This has connections to the field generating device (navigation sensor), the patient localizer and the navigation instrument.

The patient reference localizer and navigation instrument are tracked within the generated field by localizer elements integrated in the devices.

The patient reference localizer is fixed to the patient's anatomy and references it, while the instrument is tracked in relation to the patient localizer and thus to the patient's anatomy.

The provided text describes the Fiagon Navigation System, a stereotaxic instrument used as an aid for locating anatomical structures in neurosurgical procedures. The document focuses on demonstrating the substantial equivalence of this device to existing predicate devices, rather than a standalone AI-driven diagnostic system. As such, many of the requested elements pertaining to AI model validation, such as training set details, multi-reader multi-case studies, and expert consensus for ground truth, are not applicable to this type of medical device submission.

The document primarily details bench testing to assess the accuracy of the navigation system.

Here's an breakdown of the available information based on your request:

Acceptance Criteria and Reported Device Performance

The acceptance criteria for this device appear to be implicitly defined by demonstrating accuracy similar to predicate devices. The key performance metrics are Target Registration Error (TRE) and Angular Registration Error (ARE).

The document states, "The results showed that the average Target Registration Error (TRE) for the device with Localizer Set Bone Anchor was 1.17 mm (99% Cl upper bound: 2.47 mm) and the average Angular Registration Error (ARE) was 1.45° (99% CI upper bound: 2.8°), which were similar to the accuracy of the predicate devices." A similar statement is made for the Adhesive Pads. The predicate devices are listed with "Mean bench accuracy: Position Mean: < 2mm; Angular: Mean: < 2º", which serves as the implicit acceptance threshold.

Details of the Study Proving Device Meets Acceptance Criteria

1. Sample Size Used for the Test Set and Data Provenance:

   • The document describes a bench test using a phantom model. It does not refer to a "test set" in the context of patient data or a dataset of medical images for an AI.
   • The sample size for the bench testing (e.g., number of measurements taken) is not explicitly stated.
   • Data Provenance: The data is generated from a physical phantom model and CT scans of that phantom. It is a prospective test conducted specifically for this submission, not retrospective patient data. There is no country of origin for the "data" as it's a bench test, not clinical data from patients.

2. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:

   • This is not applicable for this type of device and study. The "ground truth" for the bench test was established by a Coordinate Measurement Machine (CMM) with an accuracy of 0.018 mm and the CT scans of the phantom with known reference points. This is a physical, measurable ground truth, not one established by human experts or clinical outcomes for an AI system.

3. Adjudication Method for the Test Set:

   • Not applicable. There was no human interpretation or adjudication involved in establishing the "ground truth" for the performance metrics. The accuracy was measured physically against known points on the phantom.

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

   • No. This type of study is typically performed for diagnostic or AI-assisted devices where human interpretation is a key component. This device is a navigation system used during surgery, and its evaluation focuses on its physical accuracy, not its impact on human reader performance in interpreting medical images.

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

   • Yes, in essence, the bench testing is a standalone performance study. The accuracy of the navigation system itself (the "algorithm" in a broad sense, though it's a physical tracking system) was measured independently of surgeons using it in a live setting. The measurements captured the system's ability to accurately register and track instruments against a known physical model.

6. The Type of Ground Truth Used:

   • Physical/Measured Ground Truth: The ground truth was established by precise measurements of target points on a phantom using a Coordinate Measurement Machine (CMM) and the known geometry derived from CT scans of the phantom. This is objective, physical data, not expert consensus or pathological findings.

7. The Sample Size for the Training Set:

   • Not applicable. This document describes a medical device (navigation system) with electromagnetic tracking, not a machine learning or artificial intelligence algorithm that requires a "training set." The system's functionality is based on established physical principles and engineering, not on learning from a dataset.

8. How the Ground Truth for the Training Set was Established:

   • Not applicable, as there is no "training set" for this device.

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