Search Results

Brainlab Elements Image Fusion is an application for the co-registration of image data within medical procedures by using rigid and deformable registration methods. It is intended to align anatomical structures between data sets. It is not intended for diagnostic purposes.

Brainlab Elements Image Fusion is indicated for planning of cranial and extracranial surgical treatments and preplanning of cranial and extracranial radiotherapy treatments.

Brainlab Elements Image Fusion Angio is a software application that is intended to be used for the co-registration of cerebrovascular image data. It is not intended for diagnostic purposes.

Brainlab Elements Image Fusion Angio is indicated for planning of cranial surgical treatments and preplanning of cranial radiotherapy treatments.

Brainlab Elements Fibertracking is an application for the processing and visualization of cranial white matter tracts based on Diffusion Weighted Imaging (DWI) data for use in treatment planning procedures. It is not intended for diagnostic purposes.

Brainlab Elements Fibertracking is indicated for planning of cranial surgical treatments and preplanning of cranial radiotherapy treatments.

Brainlab Elements Contouring provides an interface with tools and views to outline, refine, combine and manipulate structures in patient image data. It is not intended for diagnostic purposes.

Brainlab Elements Contouring is indicated for planning of cranial and extracranial surgical treatments and preplanning of cranial and extracranial radiotherapy treatments.

Brainlab Elements BOLD MRI Mapping provides tools to analyze blood oxygen level dependent data (BOLD MRI Data) to visualize the activation signal. It is not intended for diagnostic purposes.

Brainlab Elements BOLD MRI Mapping is indicated for planning of cranial surgical treatments.

The Brainlab Elements are applications and background services for processing of medical images including functionalities such as data transfer, image co-registration, image segmentation, contouring and other image processing.

They consist of the following software applications:

1. Image Fusion 5.0
2. Image Fusion Angio 1.0
3. Contouring 5.0
4. BOLD MRI Mapping 1.0
5. Fibertracking 3.0

This device is a successor of the Predicate Device Brainlab Elements 6.0 (K223106).

Brainlab Elements Image Fusion is an application for the co-registration of image data within medical procedures by using rigid and deformable registration methods.

Brainlab Elements Image Fusion Angio is a software application that is intended to be used for the co-registration of cerebrovascular image data. It allows co-registration of 2D digital subtraction angiography images to 3D vascular images in order to combine flow and location information. In particular, 2D DSA (digital subtraction angiography) sequences can be fused to MRA, CTA and 3D DSA sequences.

Brainlab Elements Contouring provides an interface with tools and views to outline, refine, combine and manipulate structures in patient image data. The output is saved as 3D DICOM segmentation object and can be used for further processing and treatment planning.

BOLD MRI Mapping provides methods to analyze task-based (block-design) functional magnet resonance images (fMRI). It provides a user interface with tools and views in order to visualize activation maps and generate 3D objects that can be used for further treatment planning.

Brainlab Elements Fibertracking is an application for the processing and visualization of information based upon Diffusion Weighted Imaging (DWI) data, i.e. to calculate and visualize cranial white matter tracts in selected regions of interest, which can be used for treatment planning procedures.

The provided text is a 510(k) clearance letter and its summary for Brainlab Elements 7.0. It details various components of the software, their indications for use, device descriptions, and comparisons to predicate devices. Crucially, it includes a "Performance Data" section with information on AI/ML performance tests for the Contouring 5.0 module, specifically for "Elements AI Tumor Segmentation."

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, focusing on the AI/ML component discussed:

Acceptance Criteria and Device Performance (Elements AI Tumor Segmentation, Contouring 5.0)

1. Table of Acceptance Criteria and Reported Device Performance

Sub-stratified Performance:

It's important to note that the acceptance criteria are stated for the lower bound of the 95% confidence intervals, while the reported device performance is presented as the mean values. The text explicitly states, "Successful validation has been completed based on images containing up to 30 cranial metastases, each showing a diameter of at least 3 mm, and images with primary cranial tumors that are at least 10 mm in diameter (for meningioma, cranial/paraspinal nerve tumors, gliomas, glioneuronal and neuronal tumors)." This implies that the lower bounds of the 95% confidence intervals for the reported mean values also met or exceeded the criteria.

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size: 412 patients (595 scans, 1878 annotations).
• Data Provenance: Retrospective image data sets from multiple clinical sites in the US and Europe. The data contained a homogenous distribution by gender and a diversity of ethnicity groups (White/Black/Latino/Asian). Most data were from patients who underwent stereotactic radiosurgery with diverse MR protocols (mainly 1.5T/3T MRI scans acquired in axial scan orientation). One-quarter (1/4) of the test pool corresponded to data from three independent sites in the USA.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

• Number of Experts: Not explicitly stated as a specific number. The text refers to an "external/independent annotator team."
• Qualifications of Experts: The annotator team included US radiologists and non US radiologists. No further details on their experience (e.g., years of experience) are provided.

4. Adjudication Method for the Test Set

• The text states that the ground truth segmentations, "the so-called annotations," were established by an external/independent annotator team following a "well-defined data curation process." However, the specific adjudication method (e.g., 2+1, 3+1 consensus, or independent review) among these annotators is not detailed in the provided 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

• No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study evaluating human readers' improvement with AI assistance vs. without AI assistance was not discussed or presented in the provided text. The performance data is for the AI algorithm in a standalone manner.

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

• Yes, a standalone performance evaluation of the "Elements AI Tumor Segmentation" algorithm was performed. The study describes the algorithm's quantitative validation by comparing its automatically-created segmentations directly to "ground-truth annotations." This indicates an algorithm-only performance assessment.

7. The Type of Ground Truth Used

• The ground truth used was expert consensus (or at least expert-generated) segmentations/annotations. The text explicitly states, "The validation was conducted quantitatively by comparing the (manual) ground-truth segmentations, the so-called annotations with the respective automatically-created segmentations. The annotations involved external/independent annotator team including US radiologists and non US radiologists."

8. The Sample Size for the Training Set

• The sample size for the training set is not specified in the provided text. The numbers given (412 patients, 595 scans, 1878 annotations) pertain to the test set used for validation.

9. How the Ground Truth for the Training Set was Established

• The method for establishing ground truth for the training set is not detailed in the provided text. The description of ground truth establishment (expert annotations) is specifically mentioned for the test set used for validation. However, it's highly probable that a similar expert annotation process was used for the training data given the nature of the validation.

Ask a specific question about this device