Search Results

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).

Ask a specific question about this device

iPlan's indications for use are the viewing, presentation and documentation of medical imaging, including different modules for image processing, image fusion, atlas assisted visualization and segmentation, intraoperative functional planning where the output can be used e.g. with stereotactic image guided surgery or other devices for further processing and visualization.

Example procedures include but are not limited to:

• Planning and simulation of cranial surgical procedures such as tumor resection, shunt placement, minimal-invasive stereotactic interventions, biopsy, planning and simulation of trajectories for stimulation and electrode recording
• ENT procedures such as sinus surgery, tumor surgery
• Spine procedures such as tumor surgery, pedicle screw planning, vertebroplasty planning
• iPlan View is an application which is intended to be used for reviewing existing treatment plans
• Planning and simulation of cranio-maxillofacial procedures

Typical users of iPlan are medical professionals, including but not limited to surgeons and radiologists.

iPlan is a software based treatment planning application providing functionalities like viewing, processing and documentation of medical data including different modules for image preparation, image fusion, image segmentation where the result is a treatment plan that can be used e.g. for stereotactic and/or image guided surgery.

The provided 510(k) summary for iPlan focuses on demonstrating substantial equivalence to predicate devices through technical characteristics, intended use, and non-clinical performance data. It does not provide detailed acceptance criteria or a specific study proving the device meets those criteria in the format typically associated with AI/ML device performance evaluations (e.g., sensitivity, specificity, AUC).

However, I can extract the information related to non-clinical performance data and application performance testing which served as the basis for the FDA's substantial equivalence determination for this software planning application.

Here's a breakdown of the requested information based on the provided text:

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

The document does not explicitly state quantitative acceptance criteria (e.g., minimum sensitivity, specificity, accuracy) for the device's performance. Instead, it relies on demonstrating equivalence to predicate devices and adherence to internal standards and specifications.

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

The document does not specify a "test set" in the context of a dataset for a performance study.

• For Usability Workshops: The sample size (number of participants) is not mentioned. The workshops were likely conducted on prototypes.
• For Application Performance Testing: The sample size of test cases or data used during bench and integration tests is not specified.
• Data Provenance: Not applicable in the context of a dataset for a performance study.

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

This is not directly applicable as there isn't a traditional "test set" with ground truth established by experts in the context of a performance study for this type of device described.

• For the Expert Group Review in usability: The number of experts is not specified, but they are described as working with Brainlab "to tailor the existing iPlan planning functionalities to the specific needs of CMF surgeons." Their qualifications are implicitly that they are CMF (Cranio-Maxillofacial) surgeons.

4. Adjudication method for the test set

Not applicable, as a dedicated "test set" for performance evaluation with an adjudication process is not described.

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 MRMC comparative effectiveness study is mentioned. The device is a planning application, and the evaluation focuses on its functional correctness, usability, and equivalence to predicates, not on AI-assisted diagnostic improvement for human readers.

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

This refers to the software performing its functions without human interaction. The "Application performance testing" ("bench and integration tests") would implicitly cover the standalone performance of the algorithms and modules within iPlan to ensure they meet internal standards. However, specific metrics are not provided. iPlan is described as a "software based treatment planning application," implying it's a tool for medical professionals, not a fully autonomous diagnostic or therapeutic AI.

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

For the "Application performance testing," the ground truth would be the expected output or behavior of the software modules based on their specifications and internal standards. This is typically established through:

• Functional Specifications: Defining what each module (e.g., Image Fusion, Object Creation) is supposed to do.
• Reference Data/Known Inputs: Using synthetic or previously validated medical data where the "correct" output of a segmentation, fusion, or trajectory calculation is known or can be analytically derived.
• Comparison to Predicate Devices: Ensuring that functionally similar modules perform comparably to predicate devices.

8. The sample size for the training set

This document does not describe the development of an AI/ML model that would typically have a "training set." iPlan is presented as a software application with various processing and visualization functionalities.

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

Not applicable, as a "training set" for an AI/ML model is not mentioned in this context.

Ask a specific question about this device