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The StealthStation System, with StealthStation Cranial software, is intended as an aid for locating anatomical structures in either open or percutaneous neurosurgical procedures. Their use is indicated for any medical condition in which the use of stereotactic surgery may be appropriate, and where reference to a rigid anatomical structure, such as the skull, can be identified relative to images of the anatomy.

This can include, but is not limited to, the following cranial procedures (including stereotactic frame-based and stereotactic frame alternatives-based procedures):

• Tumor resections
• General ventricular catheter placement
• Pediatric ventricular catheter placement
• Depth electrode, lead, and probe placement
• Cranial biopsies

The StealthStation™ Cranial Software v2.0 works in conjunction with an Image Guided System (IGS) which consists of clinical software, surgical instruments, a referencing system and platform/computer hardware. Image guidance, also called navigation, tracks the position of instruments in relation to the surgical anatomy and identifies this position on diagnostic or intraoperative images of the patient. During surgery, positions of specialized surgical instruments are continuously updated on these images either by optical tracking or electromagnetic tracking.

Cranial software functionality is described in terms of its feature sets which are categorized as imaging modalities, registration, planning, interfaces with medical devices, and views. Feature sets include functionality that contributes to clinical decision making and are necessary to achieve system performance.

The changes to the currently cleared StealthStation™ S8 Cranial Software are as follows:

• Addition of white matter tractography (WMT) fiber tract creation for the brain referred to as diffusion Magnetic Resonance Imaging (dMRI) tractography. dMRI tractography will process diffusion-weighted MRI data into 3D fiber tract models that represent whitematter tracts. This will be marketed as a software option called Stealth™ Tractography.
• Addition of the Medtronic SenSight™ directional DBS lead to the existing list of view overlays.
• Minor changes to the software were made to address user preferences and to fix minor anomalies.

The provided text describes the performance testing and acceptance criteria for the Medtronic Navigation StealthStation S8 Cranial v2.0 software, particularly focusing on the new white matter tractography (WMT) feature.

Here's a breakdown of the requested information:

1. Table of acceptance criteria and the reported device performance:

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

• Test Set Sample Size: The document does not specify a numerical sample size for the "datasets" used in summative usability validation and clinical expert evaluation. It states "datasets not used for development, composed of normal and abnormal brains in both pediatric and adult populations."
• Data Provenance: Not explicitly stated, but the mention of "datasets not used for development" suggests a separate, possibly curated, test set. There is no information on the country of origin or whether the data was retrospective or prospective.

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

• Number of Experts: The document refers to "clinical experts" (plural) but does not specify the exact number.
• Qualifications of Experts: Not explicitly stated (e.g., "radiologist with 10 years of experience"). It only identifies them as "representative users" and "clinical experts."

4. Adjudication method for the test set:

• The document does not describe a formal adjudication method (e.g., 2+1, 3+1). It states that "Clinical expert evaluations included white matter tract generation and editing," implying direct assessment by these experts.

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 is mentioned. The study focuses on the device's performance and validation through usability and clinical expert evaluation of the tractography feature, not on human reader performance improvement with AI assistance. The device functions as an aid for locating anatomical structures and displays information; it doesn't appear to be an AI that assists human interpretation in a comparative effectiveness sense.

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

• Yes, a standalone performance test was done for the "System Accuracy" related to 3D positional accuracy and trajectory angle accuracy. This was determined "using anatomically representative phantoms and utilizing a subset of system components and features that represent the worst-case combinations of all potential system components." This implies assessment of the system's ability to achieve these accuracy metrics independently of human interaction during the measurement. The "correct creation and rendering of dMRI tracts" also implies an algorithm-only assessment of the output.

7. The type of ground truth used:

• For System Accuracy (Positional and Trajectory): "Anatomically representative phantoms" were used. The ground truth would be the known, precisely measured dimensions and positions within these phantoms.
• For Software Functionality (dMRI tractography): The ground truth appears to be based on whether the software correctly creates and renders the dMRI tracts as per established specifications and expectations, as assessed by performance testing. Clinical experts further evaluated the quality and clinical utility of these rendered tracts in relation to other structures.
• For Usability and Clinical Expert Evaluation: The ground truth is effectively the consensus or expert judgment of the "representative users" and "clinical experts" regarding the safety, effectiveness, and clinical utility of the software and its new tractography feature. This is a form of expert consensus or clinical judgment. No mention of pathology or outcomes data for establishing ground truth is made in this context.

8. The sample size for the training set:

• The document does not provide any information about a training set since this is a regulatory submission for a software device, not an AI model that requires a distinct training phase. The new feature, dMRI tractography, processes diffusion-weighted MRI data into 3D fiber models. While the underlying algorithms would have been developed and "trained" (in a broader development sense), this document does not refer to a dedicated "training set" in the context of the device's clearance.

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

• Not applicable, as a "training set" distinct for an AI model is not described in this regulatory submission. The development and verification of the tractography algorithms would have involved internal processes and known physics/mathematics of dMRI data processing.

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