(122 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, nelvis, prostate, breast and musculosketal MRI, or increase image sharpness for 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 inouts are standard of care MRI images. The outputs are images with enhanced image quality.
Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
| Performance Test | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Noise Reduction | (i) Signal-to-noise ratio (SNR) of a selected region of interest (ROI) in each test dataset is on average improved by greater than or equal to 5% after SubtleMR enhancement compared to the original images. | |
| (ii) The visibility of small structures in the test datasets before and after SubtleMR is on average less than or equal to 0.5 Likert scale points (implying minimal visual difference in small structures). | This test passed. | |
| Sharpness Enhancement | The thickness of anatomic structure and the sharpness of structure boundaries are improved after SubtleMR enhancement in at least 90% of the test datasets. | This test passed. |
2. Sample Size Used for the Test Set and Data Provenance
The document states that the study "utilized retrospective clinical data." However, it does not explicitly state the sample size for the test set (number of images or patients) or the country of origin of the data.
3. Number of Experts Used and Qualifications of Experts
The document does not explicitly state the number of experts used or their specific qualifications (e.g., "radiologist with 10 years of experience"). It mentions "visibility of small structures" and "thickness of anatomic structure and the sharpness of structure boundaries" were evaluated, implying expert review, but the details are missing.
4. Adjudication Method for the Test Set
The document does not describe any specific adjudication method (e.g., 2+1, 3+1) for establishing the ground truth or evaluating the image quality metrics. It simply states that the tests "passed."
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
The document does not describe a multi-reader multi-case (MRMC) comparative effectiveness study involving human readers with and without AI assistance. The performance tests described focus on objective metrics (SNR) and subjective evaluation of image quality changes by the device, not on reader performance improvement.
6. Standalone (Algorithm Only) Performance
Yes, the performance data presented appears to be a standalone (algorithm only) performance evaluation. The metrics (SNR improvement, visibility of small structures, sharpness of structure boundaries) are directly related to the algorithm's output on images rather than evaluating human reader performance with or without the algorithm.
7. Type of Ground Truth Used
The ground truth used appears to be a combination:
- Objective Measurement: For noise reduction, the "signal-to-noise ratio (SNR) of a selected region of interest (ROI)" was objectively measured.
- Expert Consensus/Subjective Evaluation: For "visibility of small structures" and "thickness of anatomic structure and the sharpness of structure boundaries," a subjective evaluation was conducted using a Likert scale for noise reduction, and a percentage of datasets showing improvement for sharpness enhancement. While not explicitly stated as "expert consensus," these evaluations would typically require trained medical professionals (e.g., radiologists) to perform.
8. Sample Size for the Training Set
The document does not provide the sample size for the training set. It mentions the algorithm uses a "convolutional network-based algorithm" and that "parameters of the filters were obtained through an image-guided optimization process," implying a training phase, but the size is not specified.
9. How the Ground Truth for the Training Set Was Established
The document does not explicitly state how the ground truth for the training set was established. It mentions "image-guided optimization process" to obtain the parameters of the filters, which implies that the training data had some form of "ground truth" to guide the optimization, but the nature of this ground truth (e.g., perfectly noise-free images, perfectly sharp images) and how it was derived is not detailed.
§ 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).