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The Skull Based InstaTrak System is intended for image guided assistance during skull base surgical procedures. It is intended to be used during skull base surgery involving procedures on the base of the brain (junction of the face and neurocranium).

The InstaTrak System is an image guidance system indicated for use during skull base procedures. The Skull Base InstaTrak System is identical in principles of operation to the InstaTrak System cleared under K960330 and the Pediatric InstaTrak System cleared under K981998, which are indicated for use during nasal surgery. Using the Skull Base InstaTrak System, the surgeon can readily identify the immediate location and position of the surgical instrument during skull base procedures. The Skull Base InstaTrak System assists the surgeon in avoiding critical nerves and other anatomical structures. The Skull Base InstaTrak System includes several new components that were not included in K960330 or K981998, such as a head frame, transmitter arm, extended straight sterile pointer, mouth gag, pharyngeal retractor and nasal speculum. The remainder of the components used in the Skull Base System are identical to those described in the original submission. The additional components do not affect the overall operation of the system as the principles of operation are identical to that described in K960330. Like that system, the Skull Base InstaTrak System is an image guided surgery system that employs a computer with a top mounted swiveling monitor, software and an electromagnetic tracking system. The System uses a Sun SPARC STATION™. The System's proprietary software builds a CT model by taking axial CT images and reconstructing the coronal and sagittal views. The electromagnetic tracking system correlates the movement of surgical instruments to the CT model. The tip of the instrument is displayed as a set of cross hairs in the axial, coronal, and sagittal planes on the InstaTrak System monitor. With the InstaTrak System, CT images are used to assist the surgeon in guiding the position of the instrument during skull base surgery.

The Skull Base and InstaTrak Systems allow pre-operative viewing of the patients' CT images, contextual visualization of the pathology, intra-operative localization, screen display outputs for video recording and positional guidance. The system is operated by acquiring an axial CT scan while the patient wears the InstaTrak System headset and associated instruments. The axial images are then transferred via a network connection or cartridge to the InstaTrak System. The Headset position which stays fixed relative to the patients' anatomy, is automatically identified in the CT images by an image processing algorithm. Coronal and sagittal images are reconstructed and along with the Axial images, provide the CT model that will be used as a road map in surgery.

Here's a breakdown of the acceptance criteria and study information based on the provided 510(k) summary:

Acceptance Criteria and Device Performance

The provided document {3} states: "Testing was performed using the new components of the Skull Base InstaTrak System to determine if the new components affected device accuracy. The results showed that the device performed within the specification while using the new components."

While the document explicitly states that the device "performed within the specification," it does not provide the specific numerical acceptance criteria or the reported device performance metrics. It only refers to a general "specification" for accuracy.

Study Details

The provided 510(k) summary offers limited details about the study. Here's what can be inferred:

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

   • Sample size: Not specified.
   • Data provenance: Not specified (e.g., country of origin, retrospective or prospective). The summary only states that "Testing was performed using the new components."

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

   • Not specified. The summary does not mention the involvement of experts for establishing ground truth during the accuracy testing.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

   • Not specified. Given the nature of accuracy testing for an image guidance system, it's likely physical measurements against a known standard were used rather than expert adjudication in the traditional sense, but this is not explicitly stated.

4. 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 comparative effectiveness study was not done. The device is an image guidance system that assists surgeons, not an AI-driven diagnostic tool that impacts human reader performance in interpreting images. The study focused on the device's accuracy with new components.

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

   • Yes, a standalone performance assessment was effectively done regarding the device's accuracy. The testing aimed to determine if the new components affected device accuracy {3}. This implies evaluating the system's ability to accurately track instruments relative to the CT model, which is a standalone function of the device. However, the exact methodology (e.g., phantom studies, cadaver studies) is not detailed.

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

   • Not explicitly stated. For "accuracy" testing of an image guidance system, the ground truth would likely involve physical measurements against precisely known spatial references or benchmarks (e.g., phantom models with known dimensions and fiducial markers) rather than expert consensus on medical findings, pathology, or outcomes data.

7. The sample size for the training set:

   • Not applicable as this is not an AI/Machine Learning device that undergoes training in the conventional sense. The "training" for such a system would involve engineering and calibration, not a data-driven training set.

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

   • Not applicable for the same reason as above.

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