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510(k) Data Aggregation
(14 days)
Micro-X Limited
The device is designed to perform radiographic x-ray examinations on pediatric and adult patient treatment areas.
The Rover product concept was developed under a contract from the Australian Department of Defense to fulfil a need for a full performance digital medical x-ray imager, light enough to be used in deployed medical facilities. Key Design Features: Full trauma imaging capability 40-110kV, 0.2-20mAs; Ultra-light weight at 105 kg; Ground Clearance allows for 75mm step up; Operation on uneven ground; Spare battery tray swap out in under a minute; The unit uses FDA cleared digital image capture panels and software made by FujiFilm OR Varex.
The provided document is a 510(k) summary for a mobile x-ray system (ROVER) and does not describe acceptance criteria for an AI/ML device or detailed studies proving such a device meets those criteria. The document focuses on establishing substantial equivalence for a hardware medical device to previously cleared devices.
Therefore, many of the requested items (e.g., sample size for test set, data provenance, number of experts, adjudication method, MRMC comparative effectiveness, ground truth type, training set size and ground truth establishment methods) are not applicable or cannot be extracted from this document as it pertains to an X-ray system, not an AI/ML diagnostic aid.
Here's the information that can be extracted or inferred:
1. A table of acceptance criteria and the reported device performance
The document does not specify quantitative acceptance criteria in terms of diagnostic performance metrics for an AI/ML device. Instead, it relies on regulatory standards and the equivalence to predicate devices. The "reported device performance" is essentially that it operates properly and produces diagnostic quality images.
| Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|
| Compliance with US Performance Standard for Diagnostic X-Ray Systems (21 CFR 1020.30) | "YES 21 CFR 1020.30" |
| Compliance with IEC 60601-1 (General requirements for basic safety and essential performance) | Tested and found to be compliant. |
| Compliance with IEC 60601-1-2 (EMC) | Tested and found to be compliant. |
| Compliance with IEC 60601-1-3 (Radiation protection in diagnostic X-ray equipment) | Tested and found to be compliant. |
| Compliance with IEC 60601-1-6 (Usability) | Tested and found to be compliant. |
| Compliance with IEC 60601-2-28 (X-ray tube assemblies) | Tested and found to be compliant. |
| Compliance with IEC 60601-2-54 (X-ray equipment for radiography and radioscopy) | Tested and found to be compliant. |
| Proper system operation and diagnostic quality images | "worked properly and produced diagnostic quality images" |
| Software Validation (per FDA Guidance May 11, 2005) | "Software was validated" |
| Cybersecurity management (per FDA Guidance October 2, 2014) | "observed the recommendations" |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
Not applicable. The document states "Clinical testing was not required to establish substantial equivalence because all digital x-ray receptor panels have had previous FDA clearance." The testing described is bench testing and verification of system operation, not a clinical study with a test set of patient data.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
Not applicable, as no clinical test set requiring expert ground truth was used.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable.
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
Not applicable. This device is an X-ray system, not an AI diagnostic aid requiring MRMC studies to assess reader improvement.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. This is an X-ray system, not an AI algorithm.
7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)
Not applicable, as no clinical test set requiring ground truth was used. The focus was on engineering verification and compliance with standards.
8. The sample size for the training set
Not applicable. This is not an AI/ML device, so there is no training set mentioned.
9. How the ground truth for the training set was established
Not applicable.
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