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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.

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Brainlab Elements Contouring provides an interface with tools and views to outline, refine, combine and manipulate structures in patient image data. The generated 3D structures are not intended to create physical replicas used for diagnostic purposes. The device itself does not have clinical indications.

Brainlab Elements Fibertracking is an application for the processing and visualization of cranial white matter tracts based on Diffusion Tensor Imaging (DTI) data for use in treatment planning procedures. The device itself does not have clinical indications.

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. The device itself does not have clinical indications.

Brainlab Elements Image Fusion Angio is a software application that is intended to be used for the co-registration of cerebrovascular image data. The device itself does not have clinical indications.

Brainlab Elements is a medical device for processing of medical images, that is used to support treatment planning of surgical or radio-therapeutical procedures.

The Brainlab Elements applications transfer DICOM data to and from picture archiving and communication systems (PACS) and other storage media devices. They include modules for 2D & 3D image viewing, image processing, image co-registration, image segmentation and 3D visualization of medical image data for treatment planning procedures.

Brainlab Elements main software functionalities include:

• Visualization of medical image data in DICOM format -
• Co-registration of different imaging modalities by using both rigid and deformable registration methods
• Processing of co-registered data to highlight differences between distinct scanning sequences or to assess the response to a treatment
• Contouring and delineation of objects and anatomical structures -
• -Automatic segmentation of anatomical structures
• Manipulation of objects and seqmented structures (e.g. splitting, mirroring, etc.) -
• -Measuring tools
• Co-reqistration of cerebrovascular image data -
• Visualization of Diffusion Tensor Imaging (DTI) based data and processing of such data to visualize e.g. cranial white matter tracts

The provided text describes a 510(k) premarket notification for "Brainlab Elements," a medical image processing system. The document outlines the device's intended use, its technological characteristics, and comparison to predicate devices, along with performance data.

Here's an analysis of the acceptance criteria and study that proves the device meets them, based on the provided text:

Acceptance Criteria and Reported Device Performance

The document states that "In all cases, acceptance criteria for the validation tests were derived from scientific literature." However, the specific quantitative acceptance criteria are not explicitly detailed in the provided text. Instead, it broadly mentions the parameters that were evaluated for accuracy.

Study Details:

1. Sample Size Used for the Test Set and Data Provenance:

   • The document states that "Validation tests using retrospective patient data and phantom data were performed" for the Fibertracking algorithm and for comparing results to the predicate device.
   • Sample Size: The exact sample size (number of patients or phantom data sets) used for the test set is not specified in the provided text.
   • Data Provenance: The text indicates "retrospective patient data" and "phantom data." The country of origin of this data is not mentioned. The data's nature is retrospective.

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

   • The document does not provide any information regarding the number of experts or their qualifications used to establish ground truth for the test set.

3. Adjudication Method for the Test Set:

   • The document does not provide any information on the adjudication method (e.g., 2+1, 3+1, none) used for the test set. Given the lack of mention of multiple experts, it's possible such a method was not explicitly described or applied in a consensus manner for ground truth creation.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • The document does not mention an MRMC comparative effectiveness study involving human readers with and without AI assistance. The focus of the validation described is on the accuracy of the software's outputs (co-registration, segmentation, fiber tracts) and its substantial equivalence to predicate devices, rather than a human-in-the-loop performance improvement study.

5. Standalone (Algorithm-Only) Performance:

   • Yes, the performance tests described (accuracy of co-registrations, segmented objects, and fiber tracts) appear to be evaluating the standalone performance of the algorithms. The results are compared against "scientific literature" derived acceptance criteria, implying an assessment of the algorithm's output directly.

6. Type of Ground Truth Used:

   • The document states that for accuracy validation, "acceptance criteria for the validation tests were derived from scientific literature." While this suggests a scientific basis for evaluation, the specific type of ground truth (e.g., expert consensus, pathology, outcomes data, or a gold standard from imaging physics/phantoms) is not explicitly stated. For "retrospective patient data," it's often expert-derived or an existing clinical standard, but this is not confirmed. For "phantom data," the ground truth would be known by design.

7. Sample Size for the Training Set:

   • The document does not provide any information regarding the sample size of the training set. It mentions "Contouring: Automatic segmentation of anatomical structures" and "Automatic DTI data processing", which typically implies machine learning models requiring training data, but the details are omitted.

8. How the Ground Truth for the Training Set Was Established:

   • The document does not provide any information on how the ground truth for the training set (if any machine learning was used implicitly for "automatic segmentation" or "automatic DTI processing") was established.

In summary, while the document confirms that validation tests were performed to demonstrate substantial equivalence and adherence to "state of the art requirements" based on "scientific literature," it largely lacks the detailed quantitative acceptance criteria and the specifics of the study methodology (e.g., precise sample sizes, expert details, and ground truth establishment methods) that would typically be expected for a comprehensive understanding of the device's performance validation.

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The application can be used in clinical workflows that benefit from the co-registration of vascular image data as a planning or preplanning step.

Image Fusion Angio is intended to co-register digital subtraction angiographies with volumetric medical image data.

The provided text describes the acceptance criteria and a study proving the device meets these criteria for the Brainlab Elements Image Fusion Angio device.

Here's an analysis based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly stated as quantitative thresholds in a table format within this document. Instead, the document focuses on demonstrating substantial equivalence to predicate and reference devices, particularly in terms of fusion accuracy.
The performance is reported through nonclinical performance testing (accuracy tests).

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

• Phantom Bench Test & MRA Bench Test: "The test was repeated 3 times" for each. The data provenance is controlled lab/bench test scenario.
• Retrospective Study: "We used 35 datasets from 16 different clinical sites (11 different scanner types)." The data provenance is retrospective clinical data from multiple sites. The country of origin is not explicitly stated but implies a multi-center approach.

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

• Retrospective Study: "The gold standard fusions were defined by medical experts." The number of experts is not specified (e.g., "by medical experts" could mean one, two, or more).
• Qualifications of Experts: The document states "medical experts" but does not provide specific qualifications (e.g., "radiologist with 10 years of experience" or "neurosurgeon").

4. Adjudication Method for the Test Set

• Retrospective Study: "The gold standard fusions were defined by medical experts." and "All fusions were further reviewed by medical experts." This suggests expert consensus or review, but the specific adjudication method (e.g., 2+1, 3+1) is not detailed.

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, an MRMC comparative effectiveness study involving human readers improving with AI vs. without AI assistance was not done or reported in this document. The study focuses purely on the accuracy of the algorithm's 2D/3D co-registration.

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

• Yes, the accuracy tests (Phantom Bench Test, MRA Bench Test, Retrospective Study) evaluate the Brainlab Elements Image Fusion Angio and iPlan RT Image algorithms in a standalone manner, measuring their targeting accuracy against a defined gold standard. While medical experts defined and reviewed the ground truth/gold standard, the performance being measured is that of the algorithm itself, not an interaction with a human.

7. The Type of Ground Truth Used

• Phantom Bench Test & MRA Bench Test: "gold standard fusion" (likely established by precise manual registration or by the design of the phantom itself). This represents a highly controlled, measurable ground truth.
• Retrospective Study: "The gold standard fusions were defined by medical experts." This indicates expert consensus as the ground truth for clinical data.

8. The Sample Size for the Training Set

• The document does not provide information regarding the sample size used for the training set for the Image Fusion Angio algorithm. This section only covers performance testing (validation).

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

• Since the training set information is not provided, how its ground truth was established is also not detailed in this document.

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