(128 days)
icobrain is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures from a set of MR or NCCT 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 or NCCT images. icobrain consists of two distinct image processing pipelines: icobrain cross and icobrain long.
icobrain cross is intended to provide volumes from MR or NCCT images acquired at a single time point. icobrain long is intended to provide changes in volumes between two MR 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.
icobrain is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures from a set of MR or NCCT 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 or NCCT images.
icobrain consists of two distinct image processing pipelines: icobrain cross and icobrain long.
- icobrain cross is intended to provide volumes from MR or NCCT images acquired at a single time point.
- icobrain long is intended to provide changes in volumes between two MR 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.
The input images can be MR images (current icobrain software - KI6I 148 and KI80326) or CT images (current icobrain software - K181939). During the pre-processing, each scan is converted from DICOM format to NIFTI format. The image processing then performs the actual segmentation and calculates the measurements of the brain structures and abnormalities. Finally, the computed measurements are summarized into an electronic report and (some) segmentations are overlaid on the input images.
Here's a breakdown of the acceptance criteria and study details for the icobrain device, based on the provided text:
1. Acceptance Criteria and Reported Device Performance
The document states that a literature review was performed to set relevant acceptance criteria for each type of experiment. The acceptance criteria were based on the 90th percentile of the absolute differences in comparison to the validation threshold, and all experiments passed the acceptance criteria.
While specific numerical acceptance thresholds for individual metrics (e.g., specific volume differences) are not explicitly stated in the provided text, the overall performance is summarized by correlation coefficients:
Table: Reported Device Performance
| Metric Type | Performance (MR Experiments) | Performance (CT Experiments) |
|---|---|---|
| Pearson Correlation Coefficient | 0.91 (averaged) | 0.94 (averaged) |
| Intraclass Correlation Coefficient | 0.90 (averaged) | 0.93 (averaged) |
Note: The text explicitly states that all experiments passed the acceptance criteria, implying that the device's performance metrics were within the predefined thresholds.
2. Sample Sizes and Data Provenance
- Sample Size for Test Set:
- 463 MR subject datasets
- 618 CT subject datasets
- Data Provenance: The document does not explicitly state the country of origin for the data. It mentions subjects included:
- Healthy subjects
- Alzheimer's disease patients
- Multiple sclerosis patients
- Traumatic brain injury patients
- Depression patients
- Retrospective or Prospective: The document does not explicitly state whether the data was retrospective or prospective.
3. Number of Experts and Qualifications for Ground Truth (Test Set)
The document states that in accuracy experiments, the volumes/volume changes were compared to manually labeled ground truth volumes/volume changes. However, it does not specify the number of experts used or their qualifications (e.g., radiologist with X years of experience) for establishing this manual ground truth.
4. Adjudication Method (Test Set)
The document does not specify an adjudication method (e.g., 2+1, 3+1, none) for the test set ground truth. It only mentions "manually labeled ground truth."
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
The provided text does not mention a multi-reader multi-case (MRMC) comparative effectiveness study. Therefore, no effect size of human readers improving with AI vs. without AI assistance is reported. The study focuses on comparing the algorithm's performance against ground truth and test-retest reproducibility.
6. Standalone Performance Study
Yes, a standalone performance study was done. The accuracy experiments described compare the device's measured volumes and volume changes to simulated and/or manually labeled ground truth. The reproducibility experiments compare the device's output on test-retest imaging data sets. This directly assesses the algorithm's performance without human-in-the-loop.
7. Type of Ground Truth Used
The ground truth used for the accuracy experiments was:
- Simulated ground truth
- Manually labeled ground truth
For reproducibility experiments, test-retest consistency was used as a measure, rather than an independent ground truth.
8. Sample Size for the Training Set
The document does not provide the sample size used for the training set. It only describes the test set.
9. How Ground Truth for the Training Set Was Established
The document does not describe how the ground truth for the training set was established. The information provided focuses solely on the 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).