(30 days)
icobrain is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures from a set of MR images. This software is intended to automate the current manual process of identifying, labeling and quantifying the volume of segmentable brain structures identified on MR images. icobrain consists of two distinct image processing pipelines: icobrain cross and icobrain long.
icobrain cross is intended to provide volumes from images acquired at a single timepoint icobrain long is intended to provide changes in volumes between two images that were acquired on the samer, with the same image acquisition protocol and with same contrast at two different timepoints The results of icobrain cross cannot be compared with the results of icobrain long.
icobrain is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures from a set of MR images. This software is intended to automate the current manual process of identifying, labeling and quantifying the volume of segmentable brain structures identified on MR images.
icobrain consists of two distinct image processing pipelines: icobrain cross and icobrain long.
- icobrain cross is intended to provide volumes from images acquired at a single timepoint
- · icobrain long is intended to provide changes in volumes between two images that were acquired on the same scanner, with the same image acquisition protocol and with same contrast at two different timepoints
The results of icobrain cross cannot be compared with the results of icobrain long.
As input, icobrain uses TI-weighted and a fluid-attenuated inversion recovery (FLAIR) DICOM MR images from a single or from multiple time points. In case of multiple time points, i.e. multiple MRI scans from the same subject, for each time point one FLAIR and one TI image are used as input. During the pre-processing, the scan type (TI, FLAIR) is detected for every input image before it is converted from DICOM format to NIFTI format. The image processing then performs the actual segmentation and calculates the volumes of the brain structures. In case MRI scans from the same subject on multiple time points are available, the changes in volume of the brain structures are calculated as well. Finally, the computed volumes and volume changes (in case of multiple time points) are summarized into an electronic report and (some) segmentations are overlaid on the input images.
The provided text describes the performance testing of icobrain, focusing on its accuracy and reproducibility for volumetric quantification of brain structures from MR images.
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 mentions that a literature review was performed to set relevant acceptance criteria for each type of experiment. However, the specific numerical acceptance criteria (e.g., minimum Pearson correlation or ICC values) are not explicitly stated in the provided text.
| Performance Metric | Acceptance Criteria (as per literature review) | Reported Device Performance |
|---|---|---|
| Pearson Correlation Coefficient (Accuracy) | Not explicitly stated | 0.91 (averaged over all experiments) |
| Intraclass Correlation Coefficient (Reproducibility) | Not explicitly stated | 0.89 (averaged over all experiments) |
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 (Test Set): "The experiments encompassed 463 subject datasets in total."
- Data Provenance: The document states, "The subjects upon whom the device was tested include healthy subjects, Alzheimer's disease patients, traumatic brain injury patients, depression patients." The country of origin and whether the data was retrospective or prospective are not specified.
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)
- The document states, "In the accuracy experiments, the volumes / volume changes are compared to simulated and/or manually labeled ground truth volumes / volume changes".
- The number of experts and their qualifications for establishing the ground truth are not specified.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- The document mentions "manually labeled ground truth" but does not specify any adjudication method used for cases where multiple experts might have been involved in labeling.
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
- The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study or any assessment of human reader improvement with AI assistance. The study focuses on the standalone performance of the device against a ground truth.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
- Yes, a standalone performance study was done. The description indicates the device's measured volumes/volume changes were compared to ground truth, which implies an algorithm-only evaluation. The statement "This software is intended to automate the current manual process" further supports this.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
- The ground truth used was "simulated and/or manually labeled ground truth volumes / volume changes." This implies expert manual segmentation and/or simulated data.
8. The sample size for the training set
- The document does not specify the sample size used for the training set. It only discusses the "463 subject datasets" used for testing.
9. How the ground truth for the training set was established
- The document does not specify how the ground truth for the training set was established, as the details focus on the "experiments" (performance testing).
§ 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).