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510(k) Data Aggregation
(196 days)
The device is designed to perform radiographic x-ray examinations on all pediatric and adult patients, in all patient treatment areas.
The DRX-Revolution Mobile X-ray System is a mobile diagnostic x-ray system that utilizes digital technology for bedside or portable exams. Key components of the system are the x-ray generator, a tube head assembly (includes the x-ray tube and collimator) that allows for multiple axes of movement, a maneuverable drive system, touchscreen user interface(s) for user input. The system is designed with installable software for acquiring and processing medical diagnostic images outside of a standard stationary X-ray room. It is a mobile diagnostic system intended to generate and control X-rays for examination of various anatomical regions.
The provided text describes a 510(k) premarket notification for the DRX-Revolution Mobile X-ray System, which includes changes such as the addition of Smart Noise Cancellation (SNC) functionality and compatibility with a new detector (Lux 35). The study focuses on demonstrating the substantial equivalence of the modified device to a previously cleared predicate device (DRX-Revolution Mobile X-ray System, K191025).
Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided information:
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria (for SNC) | Reported Device Performance |
|---|---|
| At least 99% of all image pixels were within ± 1 pixel value | Achieved. The results demonstrated that at least 99% of all image pixels were within ± 1 pixel value. |
| Absolute maximum difference across all test images should be ≤ 10-pixel values | Achieved. The absolute maximum difference seen across all test images was 3-pixel values, meeting the acceptance criterion of a maximum allowable difference of 10-pixel values. |
| Noise ratio values computed for every pixel of the test images should be < 1.0 | Achieved. All noise ratio values computed for every pixel of the test images were less than 1.0, indicating that the difference in SNC noise fields between the Evolution and Revolution systems was less than the expected system noise. |
| No perceptible differences visually when compared at 200% magnification using flicker comparison | Achieved. Processed images on both systems were visually compared on a diagnostic monitor using flicker comparison, and no perceptible differences were observed when compared at 200% magnification. |
2. Sample size used for the test set and the data provenance
- Test Set Sample Size: Not explicitly stated as a number of images or patients. The study refers to "all the test images" for pixel difference analysis and "every pixel of the test images" for noise ratio calculations, implying a comprehensive evaluation of the images used.
- Data Provenance: Not explicitly stated (e.g., country of origin, retrospective/prospective). It mentions comparing images from the "in-room system (K202441)" which is the DRX-Evolution Plus system, suggesting a controlled comparison under laboratory or simulated clinical conditions rather than real-world patient data.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- This type of information is generally relevant for studies involving human interpretation and clinical endpoints. For the technical performance evaluation of Smart Noise Cancellation (SNC) described, the "ground truth" was established through quantitative technical metrics (pixel value differences, noise ratios) and visual comparison, rather than human expert consensus on clinical diagnoses. Therefore, no human experts were explicitly used to establish ground truth in the traditional sense for this specific performance evaluation.
4. Adjudication method for the test set
- Given that the performance evaluation was based on quantitative pixel-level analysis and visual comparison by presumably trained personnel rather than clinical interpretation, an adjudication method (like 2+1 or 3+1) was not applicable or described.
5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance
- No MRMC comparative effectiveness study was done or described. The study focused on the technical equivalency of the SNC feature between two systems (mobile vs. in-room) and the integration of new hardware (detector), not on the impact of AI assistance on human reader performance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Yes, a standalone performance evaluation of the SNC algorithm and its image output was effectively done. The assessment involved a "pixel-by-pixel analysis" and "noise ratio metric" to compare the output of the SNC processing on the mobile system against the in-room system. This evaluated the algorithm's performance independently of human interpretation.
7. The type of ground truth used
- The ground truth for the SNC performance evaluation was established through quantitative technical metrics and visual comparison against a known reference (the in-room system's SNC output). The reference was the cleared in-room system (DRX-Evolution Plus, K202441) with SNC, which was considered the "expected" or "gold standard" performance for SNC.
8. The sample size for the training set
- The document does not provide information on the sample size for the training set. This submission is for a modification to an existing device, specifically integrating an already cleared SNC technology (from K202441) onto a mobile platform and adding a new detector. The focus is on demonstrating the equivalence of the implementations and not on the development or training of the SNC algorithm itself.
9. How the ground truth for the training set was established
- Since information on the training set for the SNC algorithm is not provided, how its ground truth was established is not detailed in this document. It's implied that the SNC algorithm itself was developed and validated in the predicate device (DRX-Evolution Plus, K202441) submission, and the current submission leverages that existing, cleared technology.
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