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The Arcus Head Fixation Frame is intended for use as a device to clamp and hold the patient's head in a particular position for procedures requiring Magnetic Resonance Imaging (MRI) of the brain structure and targets.

The Arcus Head Fixation Frame is a re-usable, non-sterile device designed to clamp and hold the patient's head in a particular position for procedures requiring Magnetic Resonance Imaging (MRI) of the brain structure and targets. It consists of a Ring that secures the patient's skull with skull pins, a Base that secures the Ring, and Posts that hold Fixation Screws. The device is secured to the scanner table. It is designed for use with specific Siemens MRI Scanners. The device is MR Conditional and contains brass inserts and titanium pin tips.

This document describes a 510(k) premarket notification for the Arcus Head Fixation Frame. It does not contain information about the acceptance criteria and study designs that are typically associated with artificial intelligence/machine learning (AI/ML) powered medical devices. The device described in this document is a physical medical device (neurosurgical head holder/skull clamp), not an AI/ML algorithm.

Therefore, I cannot provide the requested information about acceptance criteria and study details for an AI/ML device based on this text.

However, I can extract the relevant performance data and comparisons for the physical device as presented in the document:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly present "acceptance criteria" in a tabular format as would be seen for an AI/ML device's performance metrics (e.g., sensitivity, specificity thresholds). Instead, it states that "Testing confirmed the Arcus HFF met the Product Specification Requirements." The performance data is primarily focused on demonstrating equivalence to a predicate device and confirming basic functionalities and safety for an MRI environment.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This information is not applicable and therefore not provided in the document, as it pertains to a physical device evaluation through bench testing, not an AI/ML algorithm trained and tested on data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not applicable as this is a physical device submission without data-driven ground truth. The "ground truth" for this device would be its physical properties and performance under specific test conditions, established through engineering and safety standards.

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

This information is not applicable as it is a physical device evaluation, not a clinical study involving reader performance or expert adjudication.

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

This information is not applicable as the device is a physical head fixation frame, not an AI-powered diagnostic or assistive tool.

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

This information is not applicable as the device is a physical head fixation frame, not a standalone AI algorithm.

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

For this physical device, the "ground truth" is established through:

• Engineering specifications and design: The device is designed to meet certain physical and mechanical criteria.
• Regulatory standards: Adherence to recognized consensus standards like ISO 10993-1, ASTM F2052, and ASTM F2119-07, which define acceptable physical and biological performance.
• Bench testing: Direct experimental verification of physical properties (e.g., securing head, MR safety properties).
• Predicate device comparison: The performance and safety of the device are judged largely against the established performance and safety of a legally marketed predicate device.

8. The sample size for the training set

This information is not applicable as the device is a physical head fixation frame, not an AI/ML algorithm that undergoes training.

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

This information is not applicable for the same reason as above.

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