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The Frameless Radiosurgery Components are a device used for fixation, localization and repositioning of the patient's

• head and neck
• head, neck, and shoulders
  in a linear accelerator environment for stereotactic radiosurgery/radiotherapy procedures,

The Frameless Radiosurgery Components only include mechanical devices, which can be repeatedly attached to the patient to keep his/her head and neck (head, neck and shoulders) in the same position for radiotherapy/radiosurgery. All components are intended to be non-invasive. The Frameless Radiosurgery Components are indicated for any medical condition in which the use of stereotactic radiotherapy may be considered to be safe and effective. They may be used also for repeated diagnostic tomographic scanning, The Frameless Radiosurgery Components can be used with other devices, which have already been cleared by the FDA (e.g. BrainLAB Robotics, BrainLAB Brainscan).

The provided document describes a 510(k) submission for "Frameless Radiosurgery Components." It is a premarket notification to demonstrate substantial equivalence to legally marketed predicate devices, not a study performing clinical validation with acceptance criteria and device performance metrics in the way a de novo or PMA would require.

Therefore, the document does not contain the detailed information requested regarding acceptance criteria, device performance, study design (sample size for test/training sets, data provenance, expert ground truth details, adjudication methods, or MRMC studies), or standalone performance.

The document states:

• "The Frameless Radiosurgery Components have been verified and validated according to BrainLAB's procedures for product design and development. The validation proves the safety and effectiveness of the system."

This indicates that some internal verification and validation activities were performed, but the specifics of these activities, including acceptance criteria and how they directly relate to quantifiable device performance metrics (accuracy, sensitivity, specificity, etc.), are not provided in this 510(k) summary. The FDA's letter confirms substantial equivalence based on the provided information, but it does not detail a clinical study with performance metrics in this summary.

Therefore, I cannot populate the requested table or answer most of the questions based solely on the provided text.

Here's what can be inferred or directly stated from the document, along with what is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size (Test Set): Not provided.
• Data Provenance: Not provided. The verification and validation were done "according to BrainLAB's procedures for product design and development," implying internal testing, but no details on the origin of data (e.g., country, retrospective/prospective) are given for any test data.

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

• Not applicable/Not provided. The device is a mechanical fixation and repositioning system. Its "ground truth" would likely relate to mechanical precision measurements rather than expert interpretations of medical images or conditions.

4. Adjudication method for the test set:

• Not applicable/Not provided.

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. This device is a mechanical component for patient fixation and repositioning during radiosurgery, not an AI-powered diagnostic or interpretive tool. Therefore, an MRMC study related to human reader improvement with AI assistance is not relevant or mentioned.

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

• The concept of "standalone performance" as typically applied to algorithms for diagnostic tasks is not directly applicable here. The device is a physical, mechanical system. Its performance would be measured in terms of mechanical precision and accuracy. The document does not provide specifics on such measurements.

7. The type of ground truth used:

• Not explicitly stated, but for a mechanical device, the ground truth would typically be based on highly accurate physical measurements of precision, accuracy, and repeatability of fixation and repositioning. This would likely involve metrology tools or highly precise imaging techniques to assess mechanical performance against design specifications.

8. The sample size for the training set:

• Not applicable/Not provided. This is a mechanical device, not an algorithm that requires a training set in the machine learning sense.

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

• Not applicable/Not provided. (See point 8).

In summary: The provided 510(k) summary is for a mechanical device and focuses on demonstrating substantial equivalence to predicate devices through design validation, rather than presenting a clinical study with detailed performance metrics and ground truth establishment in the context of an AI/software device.

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