(84 days)
SubtleMR is an image processing software that can be used for image enhancement in MRI images. It can be used to reduce image noise for head, spine, neck and knee MRI, or increase image sharpness for non-contrast enhanced head MRI.
SubtleMR is Software as a Medical Device (SaMD) consisting of a software algorithm that enhances images taken by MRI scanners. As it only processes images for the end user, the device has no user interface. It is intended to be used by radiologists in an imaging center, clinic, or hospital. The software can be used with MR images acquired as part of MRI exams on 1.2 Tesla, 1.5 Tesla or 3 Tesla scanners. The device's inputs are standard of care MRI images. The outputs are images with enhanced image quality.
Here's a breakdown of the acceptance criteria and study details for SubtleMR, based on the provided FDA 510(k) summary:
1. Acceptance Criteria and Reported Device Performance
The acceptance criteria are divided into two main performance tests: noise reduction and sharpness increase.
| Performance Metric | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Noise Reduction Test | ||
| Signal-to-Noise Ratio (SNR) Improvement | SNR of a selected Region of Interest (ROI) in each test dataset is on average improved by ≥ 5% after SubtleMR enhancement compared to the original images. | The study passed this criterion. (Specific average improvement percentage is not detailed in the provided text, just that it passed). |
| Visibility of Small Structures | The visibility of small structures in the test datasets before and after SubtleMR is on average ≤ 0.5 Likert scale points (implying minimal or no degradation, or slight improvement in perception). | The study passed this criterion. (Specific average Likert scale change is not detailed in the provided text, just that it passed). |
| Sharpness Increase Test | ||
| Anatomical Structure Thickness & Boundary Sharpness Improvement | The thickness of anatomic structure and the sharpness of structure boundaries are improved after SubtleMR enhancement in at least 90% of the test datasets. | The study passed this criterion. (Specific percentage of datasets improved is not detailed, just that it passed and met the "at least 90%" threshold). |
2. Sample Size Used for the Test Set and Data Provenance
The exact sample size for the test set is not explicitly stated in the provided document. It refers to "each test dataset" for the noise reduction test and "at least 90% of the test datasets" for the sharpness increase test, indicating multiple datasets were used.
The data provenance is stated as retrospective clinical data. The country of origin is not specified.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Experts
The document does not specify the number of experts used or their qualifications for establishing the ground truth for the test set.
4. Adjudication Method for the Test Set
The document does not specify an adjudication method (e.g., 2+1, 3+1) for the test set. The evaluation seems to have been based on quantitative metrics (SNR) and a Likert scale assessment, but the process of aggregation or reconciliation if multiple readers were involved is not described.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study to assess how much human readers improve with AI vs. without AI assistance. The performance tests described focus on quantitative image quality metrics (SNR, sharpness) and a perceptual assessment of small structures, not a reader study of diagnostic accuracy or efficiency.
6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done
Yes, the described performance tests appear to be standalone (algorithm only) evaluations. The metrics (SNR, Likert scale for structure visibility, and sharpness/thickness improvement percentages) directly assess the output of the algorithm on the images, rather than measuring reader performance with and without the algorithm. The device itself is described as having "no user interface," further suggesting a standalone processing function.
7. The Type of Ground Truth Used
The ground truth for the noise reduction test appears to be derived from a quantitative measurement (SNR) and a perceptual assessment (Likert scale for small structures). For the sharpness increase test, it was based on assessing the improvement in thickness of anatomic structures and sharpness of structure boundaries. These are essentially expert-defined metrics or assessments applied to the processed images, rather than external pathology or outcomes data.
8. The Sample Size for the Training Set
The document does not provide the sample size for the training set. It mentions that the algorithm uses a "convolutional network-based algorithm" whose "parameters... were obtained through an image-guided optimization process," implying a training phase, but the details of the training data are not included in this summary.
9. How the Ground Truth for the Training Set Was Established
The document does not explain how the ground truth for the training set was established. It only states that the "parameters of the filters were obtained through an image-guided optimization process," which is vague regarding the ground truth data used for this optimization. For image enhancement tasks, ground truth often involves pairs of original and "ideal" or "target" enhanced images, or noise-free versions of images, but this is not detailed here.
§ 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).