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iPlan RT FiberTracking's indications for use is to prepare and present patient and image data based on CT, MR, Angiographic and other imaging sources including

• image preparation -
• image localization -
• image fusion -
• image segmentation -
• isocenter handling -
• plan review and approval
• fiber tracking -
  where the result is used for stereotactic radiation treatment planning that is intended for use in stereotactic, conformal, computer planned, LINAC based radiation treatment of cranial, head and neck and extracranial lesions.

Fiber Tracking:
iPlan RT FiberTracking's indication for use is to prepare and present patient and image data based on MRI scanned with diffusion-weighted sequences. These diffusion images are used for the calculation and display of fiber bundles in a selected region of interest. The created treatment plans of iPlan TT FiberTracking can be used million in a scienced region of interest. The StainLAB medical devices such as BrainSCAN, iPlan RT Image, and iPlan RT Dose where this medical device is used for radiotherapy treatment planning.

iPlan RT FiberTracking is developed to enhance the functionality of Intuition Image software with the import and il lan in 11 Tiber Husking to do volupes (DTI). Additional to the basic functions of Intuition Image (viewing, drawing, planning of annuolen concernment of this application provides functions for the import and inflage fusion and attrootable racessing of the DTI data and the display of calculated fiber tracks.

This document provides a summary of the iPlan RT FiberTracking device and its substantial equivalence to a predicate device, rather than a detailed study proving its acceptance criteria. Therefore, I cannot fully complete all sections of your request based solely on the provided text.

However, I can extract the information that is present and highlight what is missing.

Here's a breakdown of the available and missing information regarding acceptance criteria and a study proving device performance:

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

The provided text does not contain a table of acceptance criteria or reported device performance metrics for the iPlan RT FiberTracking. It states that the device "has been verified and validated according to BrainLAB's procedures for product design and development," and "The trailer was found to be substantially equivalent with the predicate device iPlan! FiberTracking (K041703) and Intuition Image (K032511)." This indicates that the device met internal validation criteria and regulatory substantial equivalence, but specific performance metrics or their comparison against acceptance criteria are not detailed.

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 provided in the text.

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 provided in the text.

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

This information is not provided in the text.

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 provided in the text. The document describes a "planning system" and "medical charged-particle radiation therapy system" that includes "fiber tracking." While fiber tracking involves advanced image processing, the text does not characterize it as an "AI" system in the modern sense or discuss human-in-the-loop performance studies with AI assistance. The focus is on the device's functionality and substantial equivalence.

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

The text describes the iPlan RT FiberTracking as a system that "prepares and presents patient and image data" and is used for "calculation and display of fiber bundles." This suggests it's an algorithm-driven component. However, the document does not explicitly state that a standalone performance study was conducted or provide its results. The substantial equivalence claim implies that its performance is comparable to predicate devices, which would have undergone their own validation.

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

This information is not provided in the text.

8. The sample size for the training set

This information is not provided in the text. The device's description does not explicitly mention "training set" in the context of machine learning, as this document predates the widespread use of deep learning in medical devices. The development process emphasizes "verification and validation," which implies testing against established data, but not necessarily a distinct "training set" as understood today.

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

This information is not provided in the text.

In summary:

The provided document is a 510(k) premarket notification summary focused on establishing substantial equivalence to existing predicate devices. It states that the device was "verified and validated according to BrainLAB's procedures for product design and development" to ensure its safety and effectiveness. However, it does not include the detailed study methodology, acceptance criteria, specific performance metrics, or patient data acquisition details that would typically be found in a clinical study report. The substantial equivalence argument itself serves as the regulatory "proof" that the device meets acceptance criteria by being similar to already-accepted devices.

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