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510(k) Data Aggregation
(107 days)
The iQMR is intended for networking, communication, processing and enhancement of MRI images in DICOM format. The device processing is not effective for lesion, mass, or abnormalities of sizes less than 1.5mm. This device is indicated for use by qualified trained medical professionals.
iQMR is a software package that is aimed to process MRI images. The iQMR processing enhances MRI images by reduction of the image noise. The software runs on a PC server, which is connected to the Local Area Network (LAN). The device receives MRI images from different work stations over the network in DICOM format, processes the images and transmits them in DICOM format, to selected work stations.
The provided text describes the iQMR device and its FDA 510(k) submission. However, it does not contain the detailed information required to fully answer the request regarding acceptance criteria, a specific study proving it meets those criteria, expert details, or sample sizes for training and test sets in a clinical context.
The document states:
- "Clinical tests were not conducted." [Page 4]
- "However, MRI clinical images were processed in order to ensure that the iQMR conforms to defined user needs and intended uses under actual conditions." [Page 4]
- It also references "predefined software test plan" and "MRI standard phantoms" for non-clinical verification. [Page 3]
Therefore, the response below will highlight the limitations of the provided text in addressing the specific questions, while extracting what information is available.
Here's a breakdown of the requested information based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state quantitative acceptance criteria or detailed performance metrics from a formal clinical study. It mentions the device's intended use and the results of non-clinical tests.
| Acceptance Criteria Category | Specific Criteria (as implied or stated) | Reported Device Performance (from non-clinical tests) |
|---|---|---|
| Intended Use Conformity | Device conforms to defined user needs and intended uses under actual conditions. | Clinical MRI images were processed to ensure conformity under actual conditions. |
| Software Functionality | Software meets its specified performance. | Verified by testing the software following predefined software test plan. |
| System Performance | System meets its specified performance (noise reduction, image enhancement). | Verified by testing using MRI standard phantoms. |
| Image Resolution Limit | Processing not effective for lesions, mass, or abnormalities of sizes less than 1.5mm. | (This is a limitation stated, not a performance metric achieved by the device in terms of detecting such objects). The document doesn't provide data to "prove" this performance, it's a declared limitation of the device's capability. |
| DICOM Compatibility | Receives and transmits MRI images in DICOM format. | Software handles DICOM input and output. |
| Safety and Effectiveness | Equivalent to predicate device in terms of safety and effectiveness. | Non-clinical tests demonstrate that differences in technological characteristics do not raise new questions of safety or effectiveness. |
| Risk Management | Risks managed and controlled following ISO 14971 standard. | Hazards identified, risk levels evaluated, mitigation measures taken. Benefits outweigh residual risks. |
2. Sample Size Used for the Test Set and Data Provenance
The document states: "Clinical tests were not conducted." [page 4].
It does mention that "MRI clinical images were processed" [page 4] but does not specify the sample size, number of images, or their provenance (e.g., country of origin, retrospective/prospective nature).
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
Since formal clinical tests were not conducted, and the document only mentions "processing MRI clinical images," there is no information provided about experts establishing ground truth for a test set.
4. Adjudication Method for the Test Set
As formal clinical tests with human interpretation and ground truth establishment were not conducted, no adjudication method is described.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No MRMC comparative effectiveness study was mentioned or conducted. The submission clarifies that "Clinical tests were not conducted." [page 4]. Therefore, no effect size of human readers improving with AI vs. without AI assistance is provided.
6. Standalone Performance
The device is described as "a software package that is aimed to process MRI images" [page 3] for "enhancement of MRI images by reduction of the image noise." [page 3]. While it performs its function without a human "in the loop" during image processing, there's no standalone clinical performance study results provided in the sense of diagnostic accuracy (e.g., sensitivity, specificity for disease detection) because its purpose is image enhancement and noise reduction, not primary diagnosis. The device's declared limitation ("not effective for lesion, mass, or abnormalities of sizes less than 1.5mm") further indicates it's an enhancement tool, not a diagnostic one.
7. Type of Ground Truth Used
For the "processing of MRI clinical images" to ensure conformity, the document does not specify the type of ground truth. Given the device's function (image enhancement/noise reduction) and the lack of clinical studies, it's highly likely that ground truth, if informally assessed, would relate to image quality metrics, noise levels, or visual assessments by qualified personnel familiar with MRI, rather than pathology or outcome data for diagnostic accuracy. For the non-clinical tests, ground truth would be based on "MRI standard phantoms."
8. Sample Size for the Training Set
The document does not specify a sample size for any training set. It details verification and validation using predefined software test plans, MRI standard phantoms, and processing clinical images, but does not distinguish these as "training" or "test" sets in the context of machine learning model development. The algorithm is described as "Medic Vision's Algorithms" without further detail on its development process involving training data.
9. How the Ground Truth for the Training Set Was Established
Since no training set is mentioned, no information is provided on how its ground truth was established.
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