Search Results

The StealthStation System, with StealthStation Cranial software, is intended to aid in precisely locating anatomical structures in either open or percutaneous neurosurgical procedures. The system is indicated for any medical condition in which reference to a rigid anatomical structure 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):

• Cranial biopsies (including stereotactic)
• Deep brain stimulation (DBS) lead placement
• Depth electrode placement
• Tumor resections
• Craniotomies/Craniectomies
• Skull Base Procedures
• Transsphenoidal Procedures
• Thalamotomies/Pallidotomies
• Pituitary Tumor Removal
• CSF leak repair
• Pediatric Ventricular Catheter Placement
• General Ventricular Catheter Placement

The StealthStation System, with StealthStation Cranial software helps guide surgeons during cranial surgical procedures such as biopsies, tumor resections, and shunt and lead placements. The StealthStation Cranial Software 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. StealthStation 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 furnished document is a 510(k) premarket notification for the StealthStation Cranial Software, version 3.1.5. It details the device's indications for use, technological characteristics, and substantiates its equivalence to a predicate device through performance testing.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

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

The document mentions "System accuracy validation testing" was conducted. However, it does not specify the sample size for this test set (e.g., number of cases, images, or measurements).

Regarding data provenance, the document does not explicitly state the country of origin of the data nor whether the data used for accuracy testing was retrospective or prospective. The study focuses on demonstrating substantial equivalence through testing against predefined accuracy thresholds rather than utilizing patient-specific clinical data.

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

The document does not provide information on the number of experts used to establish ground truth for the system accuracy validation testing, nor their specific qualifications. It mentions "User exploratory testing to explore clinical workflows, including standard and unusual clinically relevant workflows. This testing will include subject matter experts, internal and field support personnel," but this refers to a different type of testing (usability/workflow exploration) rather than objective ground truth establishment for accuracy measurements.

4. Adjudication Method for the Test Set:

The document does not specify an adjudication method (e.g., 2+1, 3+1, none) for establishing ground truth for the system accuracy validation testing. The accuracy measurements appear to be objective, derived from controlled testing environments rather than subjective expert interpretations requiring adjudication.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not conducted as part of this submission. The testing described is focused on the standalone performance of the device's accuracy in a controlled environment, not on how human readers perform with or without AI assistance.

6. Standalone Performance (Algorithm Only without Human-in-the-loop Performance):

Yes, standalone performance testing was done. The "System accuracy validation testing" directly assesses the algorithm's performance in achieving specific positional and angular accuracy. The reported "Positional Error - 0.824 mm" and "Trajectory Error - 0.615 degrees" are metrics of the standalone algorithm's accuracy without direct human intervention in the measurement process itself, although the device is ultimately used by humans in a clinical context.

7. Type of Ground Truth Used:

The ground truth for the system accuracy validation testing appears to be based on objective, controlled measurements within a testing environment, likely involving phantom models or precise physical setups where the true position and orientation are known or can be measured with high precision. This is implied by the nature of "3D positional accuracy" and "trajectory angle accuracy" measurements, which are typically determined against a known, precise reference. It is not expert consensus, pathology, or outcomes data.

8. Sample Size for the Training Set:

The document does not provide any information regarding the sample size for a training set. This is because the StealthStation Cranial Software is a navigation system that uses image processing and registration algorithms, rather than a machine learning model that requires a distinct training dataset in the traditional sense. The software's development likely involves engineering principles and rigorous testing against design specifications, not iterative learning from data.

9. How the Ground Truth for the Training Set Was Established:

As the device does not appear to be an AI/ML model that undergoes a machine learning "training" phase with a labeled dataset in the conventional understanding for medical imaging analysis, the concept of establishing ground truth for a training set is not applicable in this context. The software's functionality is based on established algorithms for image registration and instrument tracking, which are then validated through performance testing against pre-defined accuracy thresholds.

Ask a specific question about this device