(134 days)
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.
| Acceptance Criteria Category | Reported Device Performance (as stated in the document) |
|---|---|
| Accuracy of Co-registrations | Tested for devices Elements Image Fusion and Elements Image Fusion Angio. Result: "Validation tests were performed to demonstrate that the products fulfill critical state of the art requirements." (Specific quantitative accuracy values or thresholds are not provided.) |
| Accuracy of Automatically Segmented Objects | Tested for device Elements Contouring. Result: "Validation tests were performed to demonstrate that the products fulfill critical state of the art requirements." (Specific quantitative accuracy values or thresholds are not provided, e.g., Dice Similarity Coefficient, Hausdorff Distance, etc.) |
| Accuracy of Fiber Tracts | Tested for device Elements Fibertracking. Result: "Validation tests were performed to demonstrate that the products fulfill critical state of the art requirements." (Specific quantitative accuracy values or thresholds are not provided.) |
| General Product Specifications | "Product specifications and the implementation of risk control measures have been tested in verification tests for the device according to IEC 62304 and ISO 14971." (No specific performance metrics are listed beyond conformity to standards.) |
| Usability Requirements | "Usability tests were performed to demonstrate the devices meet usability requirements as defined in IEC 62366." (No specific usability metrics or thresholds, such as task completion rates or error rates, are provided. It generally states that the new/modified user interface and modified interactions were subject to formative and summative usability tests.) |
| Safety and Effectiveness | "Verification and validation activities ensured that the design specifications are met and that Brainlab Elements does not introduce new issues concerning safety and effectiveness. Hence, Brainlab Elements is substantially equivalent to the predicate device(s)." (This is a summary conclusion rather than a specific performance metric.) |
Study Details:
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).