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The Brainlab Elements Trajectory Planning software is intra- and postoperative image-based planning and review of either open or minimally invasive neurosurgical and neurological procedures.

Its use is indicated for any medical condition in which the use of stereotactic surgery may be appropriate for the placement of instruments/devices and where the position of the instrument/device can be identified relative to images of the anatomy.

This includes, but is not limited to, the following cranial procedures (including frame-based stereotaxy and frame alternative-based stereotaxy):

• · Catheter placement
• · Depth electrode placement (SEEG procedures)
• · Lead placement and detection (DBS procedures)
• · Probe placement
• · Cranial biopsies

Trajectory planning is a software device which is used for the processing and viewing of anatomical images (for example: axial, coronal and sagittal reconstructions, etc.) and corresponding planning contents (for example: co registrations, segmentations, trajectories, etc.). The device is also used for the creation of coordinates and measurements that can be used as input data for surgical intervention (e.g. stereotactic arc settings).

The software is used in three different configurations:

• 1. Trajectory (Element): allows the creation of trajectories
• 2. Stereotaxy (Element); allows the creation of trajectories and supports also frame based procedures
• Lead Localization (Element): allows the creation of trajectories and automatic detection of leads in post-operative images

The software takes (DICOM) data as input and provides (DICOM) data as outputs. This data can be transferred by removable memory devices like USB sticks or via network.

The user interaction can be done with a touchscreen and/or with a mouse, optionally combined with a keyboard on a laptop which fulfill minimum requirements.

The provided document is a 510(k) Pre-market Notification from the FDA regarding Brainlab AG's "Trajectory Planning, Elements Trajectory Planning, Elements Lead Localization" software. It generally certifies substantial equivalence to a predicate device and outlines the software's intended use and performance data. However, it does not contain the detailed information required to fill out the table and answer all the questions about specific acceptance criteria and the study that proves the device meets those criteria.

Specifically, the document states:

• "Software verification testing were conducted and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices,"
• "Software verification was carried out for all the specifications and all tests met the acceptance criteria."
• "Data from the Summative Usability test concluded that there is no indication that the use of the device could lead to critical errors resulting in a hazardous situation for patient or user."
• "Clinical claims were supported via data from literature and post market data collected."

This indicates that internal testing was performed, acceptance criteria were met, and clinical claims were supported, but the specific metrics, criteria values, and detailed study methodologies are not included in this summary document. Such details would typically be found in the actual verification and validation reports submitted to the FDA, not in the publicly available 510(k) summary letter.

Therefore, many sections of your request cannot be fulfilled based solely on the provided text.

Here's an attempt to fill out what can be inferred or explicitly stated, with clear indications of what information is not present:

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

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

• Sample Size for Test Set: Not specified in the provided document.
• Data Provenance: Not specified for the software verification or clinical evaluation directly. It states "data from literature and post market data collected" for clinical claims, which implies a mix, but no specifics about origin or retrospective/prospective nature are given for the actual validation 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)

• Not specified. The document mentions "clinical claims were supported via data from literature and post market data collected" but does not detail how ground truth was established for any specific test set, nor the involvement or qualifications of experts for such ground truth.

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

• Not specified.

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

• Not explicitly mentioned. The document primarily focuses on verifying the software's functionality and safety, not on comparative effectiveness with human readers or AI assistance. The "Lead Localization (Element)" module allows "automatic detection of leads in post-operative images," indicating an AI component, but no MRMC study details or effect sizes are provided.

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

• The software includes modules like "automatic detection of leads in post-operative images." While this implies an algorithm-only component, the document does not explicitly state whether a standalone performance study report (without human intervention) was conducted as per typical regulatory requirements, beyond the general statement of "software verification."

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

• Not specified for software verification or the "automatic lead detection." For general "clinical claims," it states "data from literature and post market data collected," which could implicitly include various types of ground truth (e.g., surgical outcomes, pathology, expert review from literature), but this is not detailed for a specific validation set.

8. The sample size for the training set

• Not specified. This document pertains to regulatory clearance based on substantial equivalence and performance verification; it does not detail the specifics of machine learning model training (if applicable to features like "automatic lead detection").

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

• Not specified. (See point 8).

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