(85 days)
Deep Gray is intended to measure the volume of any brain structure and tissue from a set of MR images. It provides visualization tools, basic and advanced regions of interest drawing features and volumetric quantification. Deep Gray is to be used by trained physicians.
Visualization/Processing/Analysis of brain images from MR scanners.
Deep Gray is a software device that provides the following features:
- Import of MR brain images (DICOM 3.0 format).
- Multi-frame and multi-orientation image display.
- Basic regions of interest drawing tools: free hand drawing, filled polygon . drawing. Labels can be associated with drawn objects.
- Advanced drawing tool: semi-automatic labeling of normal brain structures and . tissues.
- . Generation of a report listing the volumes of labeled structures and tissues.
The operator can choose to manually draw and label brain structures and tissues, or they can chose to perform a semi-automatic labeling, followed by visual inspection and manual adjustment. The Deep Gray system does not have any adverse affects on health. This tool measures and displays the volume of regions of interest. The operator can choose to accept, modify, or reject the volume and/or label suggested by the program.
The document provided a summary of the performance testing and clinical evaluation of the Deep Gray system, focusing on its semi-automatic labeling feature for brain structures. However, it does not include detailed acceptance criteria, specific reported performance metrics against those criteria, or comprehensive information about the study design that would be required to fully answer all aspects of your request.
Here's a breakdown of what can be extracted and what is missing based on the provided text:
Based on the provided text, the Deep Gray system underwent a clinical evaluation to compare its semi-automatic labeling feature with manual labeling of brain structures.
Here's an analysis of the requested information:
1. A table of acceptance criteria and the reported device performance
- Acceptance Criteria: Not explicitly stated in the provided text.
- Reported Device Performance: The document only states that "Laboratory performance comparisons between the semi-automatic labeling feature and manual labeling of brain structures has been successfully completed." No specific quantitative metrics (e.g., accuracy, precision, dice coefficient, volume difference, time difference) are provided.
Therefore, a table cannot be constructed with the available information.
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: Not specified in the provided text.
- Data Provenance: Not specified (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 (e.g., radiologist with 10 years of experience)
- Number of Experts: Not specified.
- Qualifications of Experts: Not specified. The document only mentions "manual labeling," implying that human experts performed this, but their number and qualifications are not detailed.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
- Adjudication Method: Not specified.
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: The document describes "performance comparisons between the semi-automatic labeling feature and manual labeling." While it compares an AI-assisted method (semi-automatic) to a manual method, it does not explicitly state that it was an MRMC study designed to measure the improvement of human readers with AI assistance. It seems to compare the output of the semi-automatic process to the output of manual labeling.
- Effect Size: Not provided.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
- Standalone Performance: The description of the device states, "The operator can choose to manually draw and label brain structures and tissues, or they can chose to perform a semi-automatic labeling, followed by visual inspection and manual adjustment." This implies that the semi-automatic labeling is designed to be used with human oversight and potential adjustment. Therefore, the "clinical evaluation" appears to compare this semi-automatic, human-reviewed process against manual labeling, rather than a purely standalone algorithm without any human intervention. The term "algorithm only" is not explicitly addressed, but the operational description suggests human-in-the-loop.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
- Type of Ground Truth: The ground truth for the comparison was established by "manual labeling of brain structures." This suggests expert-derived ground truth, where human experts manually delineated the structures. There's no mention of pathology or outcomes data.
8. The sample size for the training set
- Training Set Sample Size: Not specified in the provided text. The document mentions software development, testing, and validation, but not the specific details of a training set for a machine learning model.
9. How the ground truth for the training set was established
- Training Set Ground Truth Establishment: Not specified.
Summary of Missing Information:
The provided 510(k) summary is very high-level regarding the performance testing and clinical evaluation. Critical details such as specific acceptance criteria, quantitative performance metrics, sample sizes, expert qualifications, and detailed study methodologies are not included. This type of information is typically found in the full submission documents or an accompanying clinical report, not an abbreviated 510(k) summary.
§ 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).