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GBrain MRI is a post processing medical device software intended for analyzing and quantitatively reporting signal hyperintensities in the brain on FLAIR MR images in the context of diagnostic radiology.
GBrain MRI is intended to provide automatic segmentation, quantification, and reporting of derived image metrics. It is not intended for detection or specific diagnosis of any disease nor for the detection of signal hyperintensities.
GBrain MRI should not be used in-lieu of a full evaluation of the patient's MRI scans. The physician retains the ultimate responsibility for making the final patient management and treatment decisions.
GBrain MRI is a non-invasive MR imaging post-processing medical device software that aids in the volumetric quantification of hyperintensities in T2-weighted Fluid Attenuated Inversion Recovery (T2w FLAIR) brain MR images. It is intended to aid the trained radiologist in quantitative measurements.
The input to the software is the T2w FLAIR brain MR images.
The outputs are volume measurements in Secondary Capture DICOM format, a DICOM Encapsulated pdf file, as well as a DICOM SR. More specifically, the total volume of hyperintensities in the input T2w FLAIR are shown in a new secondary capture image series, called GBrain T2 FLAIR with a segmentation overlay on the hyperintensities that were used to measure the total volumes. These volume measurements are summarized in the DICOM encapsulated pdf and DICOM SR files.
The outputs are provided in standard DICOM format that can be displayed on most third-party DICOM workstations and Picture Archive and Communications Systems (PACS).
The software is suitable for use in routine patient care as a support tool for radiologists in assessment of structural adult brain MRIs, by providing them with complementary quantitative information.
The GBrain MRI processing architecture includes a proprietary automated internal pipeline that performs skull stripping, signal normalization, segmentations, volume calculations, and report generation.
From a workflow perspective, GBrain MRI is packaged as a computing appliance that is capable of supporting DICOM file transfer for input, and output of results. The software is designed without the need for a user interface after installation. Any processing errors are reported either in the output series report, or in the system log files.
GBrain MRI software is intended to be used by trained personnel only and is to be installed by trained technical personnel.
Quantitative reports and derived image data sets are intended to be used as complementary information in the review of a case.
The GBrain MRI software does not have any accessories or patient contacting components.
The GBrain MRI device is intended to be used for the adult population only.
The provided text is an FDA 510(k) clearance letter and summary for the GBrain MRI device. While it states that performance testing was conducted and provides some high-level information about the testing, it does not include the specific acceptance criteria or the reported device performance in a granular format. It merely states that the device "passed the planned acceptance criteria."
Therefore, I cannot fully complete the requested table or answer all of your questions directly from the provided text.
Here's what I can extract and infer based on the information given:
1. A table of acceptance criteria and the reported device performance
The document states: "Acceptance criteria were set such that the GBrain MRI model performance meets clinically acceptable levels." and "The results of the segmentation performance testing demonstrated that the GBrain MRI system segments hyperintensities with an accuracy that passed the planned acceptance criteria."
However, the specific numerical values for the acceptance criteria and the quantitative reported performance of the device are not provided in this document. They describe the type of metrics used for comparison, but not the actual thresholds or results.
| Metric Type | Acceptance Criteria (Not Explicitly Stated in Document) | Reported Device Performance (Not Explicitly Stated in Document) |
|---|---|---|
| Volume Measurement Accuracy (OLS Regression) | Clinically acceptable level | Passed acceptance criteria |
| Segmentation Overlap Agreement (Dice Similarity Coefficient) | Clinically acceptable level | Passed acceptance criteria |
2. Sample size used for the test set and the data provenance
- Sample Size: Not explicitly stated.
- Data Provenance: Not explicitly stated (e.g., country of origin, retrospective or prospective).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Number of Experts: Not explicitly stated. The text mentions "expert-labeled segmentations."
- Qualifications of Experts: Not explicitly stated. The text refers to them as "expert-labeled segmentations of hyperintensities."
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- The ground truth was established using a "Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm generated consensus." STAPLE is a method for combining multiple expert segmentations into a single probabilistic estimate of the true segmentation, effectively an automated soft adjudication method. It is not a fixed 2+1 or 3+1 method, but rather a statistical model that estimates the true segmentation and the performance parameters of each rater, allowing for a consensus that accounts for individual rater biases and accuracies.
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
- MRMC Study: No, the document describes a standalone performance evaluation of the algorithm against expert-derived ground truth, not a comparative effectiveness study involving human readers with and without AI assistance.
- Effect Size: Not applicable, as no MRMC study comparing human readers with/without AI assistance was described.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
- Yes, a standalone algorithm-only performance study was done. The document states: "GBrain MRI segmentation performance was evaluated by comparing the software-derived segmentations to a... consensus of expert-labeled segmentations..." This indicates an evaluation of the algorithm's output directly against ground truth, independent of human interaction during the test.
7. The type of ground truth used
- Expert Consensus: The ground truth for the test set was "a Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm generated consensus of expert-labeled segmentations of hyperintensities."
8. The sample size for the training set
- Not explicitly stated in the provided document.
9. How the ground truth for the training set was established
- Not explicitly stated in the provided document. While it mentions deep learning is used for segmentation, it doesn't describe the ground truth generation process for the training data specifically.
§ 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).