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510(k) Data Aggregation
(77 days)
Anatomy: head, limbs, spine, torso; Nuclei: H-1; Diagnostic uses: Imaging
Magnetic Resonance Diagnostic Device.
Here's an analysis of the provided text regarding the IMIG-MRI device, focusing on acceptance criteria and study details.
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria Category | Specific Metric | Acceptance Criteria (Head Coil) | Reported Device Performance (Head Coil) | Acceptance Criteria (Body Coil) | Reported Device Performance (Body Coil) |
|---|---|---|---|---|---|
| Signal-to-Noise Ratio (S/N) | S/N | > 32 | > 32 (implied, no specific value given) | > 31 | > 31 (implied, no specific value given) |
| Uniformity | Uniformity | < 15 % | < 15 % (implied) | < 40 % | < 40 % (implied) |
| Geometric Distortion | Geometric Distortion | < 2.5 % | < 2.5 % (implied) | < 3.5 % | < 3.5 % (implied) |
| Slice Thickness | Within 10% of nominally designated value | Within 10% (implied) | Within 10% (implied) | Within 10% (implied) | Within 10% (implied) |
| Slice Position (gap) | Within 10% of nominally designated value | Within 10% (implied) | Within 10% (implied) | Within 10% (implied) | Within 10% (implied) |
| Spatial Resolution | Nominally equivalent to pixel size | Nominally equivalent | Nominally equivalent (implied) | Nominally equivalent | Nominally equivalent (implied) |
| Acoustic Noise Levels | Maximum peak | N/A | 114 dB peak | N/A | N/A |
| Acoustic Noise Levels | A-weighted RMS | N/A | 95 dB A-weighted RMS | N/A | N/A |
| Safety Parameters | Maximum static magnetic field | N/A | 0.15 Tesla | N/A | N/A |
| Maximum rate of magnetic field change | N/A | 18.4 Tesla/sec | N/A | N/A | |
| Maximum RF power deposition | N/A | 0.05 W/kg | N/A | N/A |
Note: The provided text only states the performance criteria as "Specifications" and then lists the values. It implies the device meets these specifications, as it's presented in a 510(k) summary for device clearance. The table above assumes the reported performance meets the stated criteria for the categories where a numerical criterion is given. For safety parameters and acoustic noise, specific performance values are given, but no explicit "acceptance criteria" for those specific values are provided within this document excerpt.
2. Sample Size Used for the Test Set and the Data Provenance
The provided text does not specify the sample size used for the performance test set. It also does not provide any information regarding the data provenance (e.g., country of origin, retrospective or prospective nature of the data). The performance summary focuses on technical specifications of the device itself rather than clinical image-based performance.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts
This information is not provided in the document. The "performance test data summary" focuses on technical specifications inherent to the MRI system (S/N, uniformity, distortion, slice accuracy, spatial resolution) rather than diagnostic accuracy based on expert interpretation of images. Therefore, the concept of "ground truth" derived from expert consensus on images is not applicable to the reported performance evaluation in this specific context.
4. Adjudication Method (for the test set)
Since the "performance test data summary" focuses on technical image quality metrics and not on diagnostic accuracy requiring expert interpretation, no adjudication method is mentioned or implied.
5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done
No, the document does not mention a Multi Reader Multi Case (MRMC) comparative effectiveness study. The performance summary is purely about the technical specifications of the MRI device itself, not its impact on human reader performance. Therefore, there is no information about the effect size of how much human readers improve with AI vs. without AI assistance. (It's worth noting that this document is from 1996, long before AI assistance in radiology was commonplace.)
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
No, the document does not mention a standalone algorithm performance study. This is an MRI device itself, not an algorithm designed for image analysis, so a standalone algorithm performance study as typically understood in AI/ML device submissions is not applicable here.
7. The Type of Ground Truth Used
For the technical performance metrics listed (S/N, uniformity, geometric distortion, slice thickness, slice position, spatial resolution), the "ground truth" would be established by physical measurements and phantom studies using established standards and quality assurance protocols for MRI systems. This typically involves using phantoms with known properties and measuring the system's output against those known values. It is not based on expert consensus, pathology, or outcomes data in the context of diagnostic performance.
8. The Sample Size for the Training Set
This information is not applicable and not provided. MRI systems like the IMIG-MRI do not use "training sets" in the context of the machine learning algorithms often discussed when asking this question. The device's performance is based on its hardware design, software programming, and physical principles, not on being "trained" on a dataset of images to learn patterns.
9. How the Ground Truth for the Training Set Was Established
This information is not applicable and not provided for the same reasons as #8. There is no training set for this type of medical device functionality.
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