(202 days)
AiMIFY is an image processing software that can be used for image enhancement in MRI images. It can be used to increase contrast-to-noise ratio (CNR), contrast enhancement (CEP), and lesion-to-brain ratio (LBR) of enhancing tissue in brain MRI images acquired with a gadolinium-based contrast agent. It is intended to enhance MRI images acquired using standard approved dosage per the contrast agent's instructions for use.
The AiMIFY device is a software as a medical device consisting of a machine learning software algorithm that enhances images taken by MRI scanners. AiMIFY consists of a software algorithm that improves contrast-to-noise ratio (CNR), contrast enhancement (CEP), and lesion-to-brain ratio (LBR) of Gadolinium-Based Contrast Agent (GBCA) enhanced T1-weighted images while maintaining diagnostic performance, using deep learning technology. It is a post-processing software that does not directly interact with the MR scanner and does not have a graphical user interface. It is intended to be used by radiologists in an imaging center, clinic, or hospital. The AiMIFY software uses T1 pre and post-contrast MR images acquired as part of standard of care contrast-enhanced MRI exams as the software input. The outputs are the corresponding images with enhanced contrast presence. AiMIFY enhances DICOM images.
AiMIFY image processing software uses a convolutional network based algorithm to enhance the AiMIFY-contrast images from pre-contrast and standard-dose post-contrast images. The image processing can be performed on MRI images with predefined or specific acquisition protocol settings as follows: gradient echo (pre- and post-contrast), 3D BRAVO (pre- and post-contrast), 3D MPRAGE (preand post-contrast), 2D T1 spin echo (pre- and post-contrast), T1 FLAIR/ inversion recovery spin echo (pre- and post-contrast).
The AiMIFY image is created by AiMIFY and sent back to the picture archiving and communication system (PACS) or other DICOM node by the compatible MDDS for clinical review.
Because the software runs in the background, it has no user interface. It is intended to be used by radiologists in an imaging center, clinic, or hospital.
Note, depending on the functionality of the compatible MDDS, AiMIFY can be used within the facility's network or remotely. The AiMFY device itself is not networked and therefore does not increase the cybersecurity risk of its users. Users are provided cybersecurity recommendations in labeling.
Here's an analysis of the acceptance criteria and the study proving the device meets those criteria, based on the provided text.
Device: AiMIFY (1.x)
Indications for Use: Image processing software for enhancement of MRI images (increase CNR, CEP, LBR of enhancing tissue in brain MRI images acquired with gadolinium-based contrast agent).
1. Acceptance Criteria and Reported Device Performance
Table of Acceptance Criteria and Reported Device Performance:
| Metric | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Quantitative Assessment | ||
| CNR (Contrast-to-Noise Ratio) Improvement | On average, improved by >= 50% after AiMIFY enhancement compared to traditionally acquired contrast images. | Achieved: 559.94% across all 95 cases; 831.70% for 57 lesion-only cases. Significantly higher than standard post-contrast images (Wilcoxon signed-rank test, p = 50% after AiMIFY enhancement compared to traditionally acquired contrast images. (Inferred from primary endpoint definition encompassing CNR, LBR, CEP) |
| CCC for Parenchyma Tissue (7 feature classes) | >= 0.8 | Achieved: Ranged from 0.82 to 0.92 for parenchyma tissue. |
| SubtleMR Denoising Module Performance | ||
| Visibility of Small Structures | Average scores between original and SubtleMR enhanced images = 5% (Acceptance criteria established in SubtleMR validation K223623) | Achieved: Average SNR improvement was 14%. |
2. Sample Size Used for the Test Set and Data Provenance
- Test Set Sample Size: 95 T1 brain cases.
- Of these, 57 cases had identified lesions and were used for lesion-specific analyses (e.g., LBR, lesion-specific CNR).
- Data Provenance: Retrospective, acquired from clinical sites or hospitals.
- Country of Origin: USA (California, New York, Nationwide), Beijing, China.
- Acquisition details: Variety of T1 input protocols (BRAVO, MPRAGE+, FLAIR, FSE), orientations (axial, sagittal, coronal), acquisition types (2D, 3D), field strengths (0.3T, 1.5T, 3.0T), and MR scanner vendors (GE, Philips, Siemens, Hitachi).
- Patient Demographics: Age (7 to 86, relatively even distribution), Sex (relatively even distribution of females and males), Pathologies (Cerebritis, Glioma, Meningioma, Metastases, Multiple Sclerosis, Neuritis, Inflammation, Other tumor related, other abnormalities).
3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications
- Quantitative Assessment (ROI drawing): One board-certified radiologist.
- Qualitative Assessment (Reader Study): Three board-certified neuro-radiologists.
- Specific years of experience are not mentioned, but "board-certified" implies a certain level of qualification and experience within their specialty.
4. Adjudication Method for the Test Set
- Quantitative Assessment: ROIs were drawn by a single board-certified radiologist. No explicit mention of adjudication or multiple expert consensus for the initial ROI placement. The statistical analysis (Wilcoxon signed-rank test) focuses on the comparison of metrics derived from these ROIs.
- Qualitative Assessment (Reader Study): The readers individually rated images on Likert scales. The results are presented as aggregated statistics (e.g., "significantly better/not worse by p
§ 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).