(120 days)
The Fiagon Navigation System is intended as an aid for precisely locating anatomical structures in either open or percutaneous 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 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 Fiagon Navigation System displays position instruments in 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. 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 information is updated with a rate of 15 to 45 Hz.
The provided document is a 510(k) Premarket Notification from the FDA for a medical device called the Fiagon Navigation System. This type of submission is for demonstrating substantial equivalence to a predicate device, not for establishing novel performance or clinical efficacy through the types of detailed studies typically associated with AI/ML model validation. Therefore, the document does not contain the information required to answer all parts of your request, particularly those related to AI/ML specific evaluations (like training set details, expert consensus for ground truth, MRMC studies, etc.).
However, I can extract the relevant information regarding acceptance criteria and performance data for this device as presented in the submission.
Here's a breakdown based on the provided text:
Acceptance Criteria and Reported Device Performance
The submission focuses on bench testing to demonstrate that the modified device (with WiFi connectivity and iPad remote control with automatic registration) performs as intended and is substantially equivalent to its predicate. The "acceptance criteria" are implied by the types of tests conducted and their successful completion.
1. Table of Acceptance Criteria and the Reported Device Performance:
| Feature/Test | Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|---|
| Positioning Accuracy | Must perform comparably to the predicate device in locating anatomical structures precisely for navigation. | "Positioning accuracy test for target registration" was conducted. The device "functioned as intended and similar to the predicate." |
| Wireless Coexistence | Must not interfere with other wireless devices and must maintain functionality when coexisting with them. | "Wireless coexisting testing" was conducted. The device "functioned as intended and similar to the predicate." |
| Software Functionality | Software must operate without defects and perform all intended functions (e.g., displaying navigation information, updating at specified rates, handling registration). | "Software testing" was conducted. The device "functioned as intended and similar to the predicate." |
| Overall Performance & Equivalence | The modified system must perform as intended and in a similar manner compared to the predicate, and not raise new safety or effectiveness questions. | "In all instances, the device functioned as intended and similar to the predicate, supporting the substantial equivalence to the predicate device." "The modified device does not present any new issues of safety or effectiveness." |
Study Details (as inferable from the document)
2. Sample size used for the test set and the data provenance:
- The document mentions "bench testing" but does not specify sample sizes (e.g., number of tests, number of targets, number of wireless interferences) or data provenance (e.g., country of origin, retrospective/prospective). This level of detail is typically not required for a 510(k) submission focused on substantial equivalence of a modified hardware/software system.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- This information is not provided. The testing appears to be quantitative bench testing of accuracy and functionality, not a study requiring expert interpretation of medical images or clinical outcomes.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
- This is not applicable to the type of bench testing described.
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 study was not done. This device is a navigation system that displays instrument position relative to pre-operative scans, not an AI/ML diagnostic or assistive tool that would involve "human readers" interpreting images.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- The tests described ("Positioning accuracy test for target registration", "Wireless coexisting testing", "Software testing") are essentially standalone performance evaluations of the device's capabilities. However, this is not an AI algorithm in the contemporary sense. The "algorithm" here refers to the underlying calculations for electromagnetic tracking and image-to-patient registration.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- For positioning accuracy, the ground truth would likely be established by a precisely measured physical reference (e.g., a known target position in a phantom) rather than clinical expert consensus or pathology. For wireless coexistence and software testing, the ground truth is whether the system performs according to specifications.
8. The sample size for the training set:
- This is not applicable and not provided. This device is not described as an AI/ML system requiring a distinct "training set" in the context of deep learning.
9. How the ground truth for the training set was established:
- This is not applicable, as there's no mention of a "training set" for an AI/ML model for this device.
§ 882.4560 Stereotaxic instrument.
(a)
Identification. A stereotaxic instrument is a device consisting of a rigid frame with a calibrated guide mechanism for precisely positioning probes or other devices within a patient's brain, spinal cord, or other part of the nervous system.(b)
Classification. Class II (performance standards).