(309 days)
The NeuroReader Medical Image Processing Software 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.
Neuroreader is a medical image processing software intended for automatic labeling, visualization and volumetric quantification of identifiable brain structures from magnetic resonance images. The segmentation system relies on a number of atlases which each consist of a T1-weighted MR image, a binary mask covering the brain and a label map dividing the MR image into different anatomical segments. Neuroreader provides an estimation of the normal volume for a person with similar demographic data. This is done based on a statistical model and a database of healthy material. Neuroreader is intended to automate the current manual process of identifying, labeling and quantifying the volume of brain structures identified on MR images. Neuroreader is aimed to be a support tool for clinicians in assessing structural MRIs. Neuroreader describes the analysis results in a self-explicative volumetric report within an analysis-time of 10 minutes.
Here's a breakdown of the acceptance criteria and study information for the NeuroReader Medical Image Processing Software, based on the provided document:
Acceptance Criteria and Device Performance
| Acceptance Criteria / Metric | Reported Device Performance |
|---|---|
| Segmentation Quality (Hippocampus) | Dice similarity index of 0.87 (both right and left hippocampus) |
| Max Segmentation Quality (Hippocampus) | Dice similarity index of 0.91 |
| Analysis Time | 10 minutes |
| Comparison to Normative Database (Correction for) | Sex, head size, and age |
Study Details
1. Sample Size Used for the Test Set and Data Provenance:
* Sample Size: 100 images
* Data Provenance: The dataset used was the AEAD-ADNI Hippocampal segmentation protocol dataset. While the document doesn't explicitly state the country of origin, ADNI (Alzheimer's Disease Neuroimaging Initiative) is primarily a North American study, typically involving institutions across the US and Canada. The study was retrospective, as it utilized an existing dataset.
2. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:
* The document states that the ground truth was "100 images of the manually segmented AEAD-ADNI Hippocampal segmentation protocol dataset." It does not specify the number of experts or their qualifications, but implies that the dataset itself provides the manually segmented ground truth.
3. Adjudication Method for the Test Set:
* Not explicitly stated. The ground truth is described as "manually segmented," suggesting a consensus or single expert approach for the original manual segmentation, but no specific adjudication process for validating those manual segmentations in the context of this study is mentioned.
4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done:
* No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned or described. The study focuses on evaluating the software's performance against a pre-existing "manually segmented" ground truth, not on comparing human readers with and without AI assistance.
5. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) was Done:
* Yes, a standalone study was done. The evaluation involved comparing the NeuroReader's automatic segmentations directly against the manually segmented images in the test set.
6. The Type of Ground Truth Used:
* Expert Consensus / Manual Segmentation: The ground truth was established by "manually segmented" images from the AEAD-ADNI Hippocampal segmentation protocol dataset. This indicates expert manual delineation of the structures, likely through a consensus or highly standardized protocol.
7. Sample Size for the Training Set:
* The document mentions that the segmentation system relies on "a number of atlases" and "a database of healthy material" to estimate normal volumes. However, it does not specify the sample size for the training set used to develop the NeuroReader's segmentation algorithm itself.
8. How the Ground Truth for the Training Set Was Established:
* The document states that the segmentation system relies on "a number of atlases which each consist of a T1-weighted MR image, a binary mask covering the brain and a label map dividing the MR image into different anatomical segments." It also mentions that "All atlases must agree on which label values belong to which segments" and that "For this purpose the standards implemented in the Freesurfer project are used." This suggests that the training set's ground truth was established using pre-existing anatomical atlases and standardization protocols, likely developed through expert anatomical knowledge and manual segmentation, aligned with methodologies like those in the Freesurfer project.
§ 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).