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The Cranial IGS System, when used with a compatible navigation platform and compatible instrument accessories, is intended as an image-guided planning and navigation system to enable navigated surgery. It links instruments to a virtual computer image space on patient image data that is being processed by the navigation platform.

The system is indicated for any medical condition in which a reference to a rigid anatomical structure can be identified relative to images (CT, CTA, X-Ray, MR, MRA and ultrasound) of the anatomy, including:

• · Cranial Resection
  • · Resection of tumors and other lesions
  • · Resection of skull-base tumor or other lesions
  • · AVM Resection
• · Cranial biopsies
• Intracranial catheter placement
• · Intranasal structures and Paranasal Sinus Surgery
  • · Functional endoscopic sinus surgery (FESS)
  • · Revision & distorted anatomy surgery all intranasal structures and paranasal sinuses

The Cranial IGS System consists of software and hardware (instruments) components that when used with a compatible navigation or "IGS platform" enables navigated surgery. It links instruments in the real world or "patient scan data or "image space". This allows for the continuous localization of medical instruments and patient anatomy for medical interventions in cranial and ENT procedures.

The provided text describes the Cranial Image Guided Surgery System, which is a medical device. The information details the device's intended use, technological characteristics compared to predicate devices, and a summary of verification and validation activities.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the "Cranial Image Guided Surgery System" are not explicitly stated as distinct acceptance criteria values in the document. Instead, the document presents performance verification results (accuracy), which are implicitly the performance targets for the device. The "mean accuracy" values mentioned are the internal acceptance criteria the device was required to meet.

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

The document mentions "Nonclinical performance testing (Accuracy)" and "The following table summarizes the performance verification results of the system." However, it does not specify the sample size (e.g., number of test cases, number of images, or number of simulated scenarios) used for these accuracy tests.

The data provenance is also not explicitly stated in terms of country of origin or whether the data was retrospective or prospective. Given that it's "nonclinical performance testing," it is likely that the testing involved phantom studies or simulated scenarios rather than real patient data.

3. Number of Experts Used to Establish Ground Truth and Their Qualifications

The document does not specify the number of experts used to establish ground truth for the test set or their qualifications. The accuracy testing seems to be based on physical measurements against established ground truth (e.g., from a phantom or known geometry), rather than expert consensus on image interpretation.

4. Adjudication Method for the Test Set

The document does not mention any adjudication method (e.g., 2+1, 3+1, none) for the test set. Given that the performance testing is focused on mechanical/measurement accuracy (location and trajectory angle errors), an adjudication method requiring human interpretation would not be applicable in the same way as it would be for a diagnostic AI system.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done

The document does not indicate that an MRMC comparative effectiveness study was done. The assessment presented is focused on the device's accuracy in navigation, not on a comparison of human reader performance with and without AI assistance. This device is an image-guided surgery system, which assists surgeons during procedures, rather than an AI diagnostic tool primarily interpreted by human readers.

6. If a Standalone (i.e., Algorithm Only Without Human-in-the-Loop Performance) Was Done

The "Nonclinical performance testing (Accuracy)" described can be considered a form of standalone performance evaluation for the algorithm's core functionality (localization and trajectory determination). The results presented (location error, trajectory angle error) are metrics of the system's inherent accuracy, without explicitly involving real-time human interaction for performance measurement in these specific tests. However, the device is ultimately intended for human-in-the-loop use in surgery.

7. The Type of Ground Truth Used

The ground truth used for the "Nonclinical performance testing (Accuracy)" appears to be physical measurement against a known standard or ideal. For instance, in a phantom study, the "true" location and trajectory would be precisely known or measurable, allowing for the calculation of errors from the device's output. The text does not specify if it was expert consensus, pathology, or outcomes data.

8. The Sample Size for the Training Set

The document does not provide any information regarding the sample size used for the training set. It primarily discusses the device's verification and validation, but not the development or training of any underlying algorithms (if applicable, beyond traditional image processing and navigation).

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

Since no information on a "training set" is provided, there is no mention of how ground truth for a training set was established. The device's functionality appears to be based on established navigation principles and software engineering, rather than a machine learning model that requires a labeled training dataset with associated ground truth for learning.

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