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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.

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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The device is designed to perform radiographic x-ray examinations on pediatric and adult patients 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.

The provided document is a 510(k) Premarket Notification for the "Rover" mobile x-ray system. It details the device's technical specifications and compares it to a legally marketed predicate device (DRX-Revolution Nano Mobile X-ray System). The acceptance criteria and testing described are focused on demonstrating substantial equivalence to an existing device, rather than proving performance against specific acceptance criteria for an AI/ML-based device.

Therefore, the document does not contain the information requested regarding acceptance criteria related to AI/ML device performance, ground truth establishment, expert adjudication, MRMC studies, or standalone algorithm performance.

Here's why the document doesn't provide the requested information and what it does provide:

• Device Type: The Rover is a mobile x-ray system, a physical medical device for capturing x-ray images. It uses FDA-cleared digital image capture panels and software (specifically, Fujifilm and Fuji FDX Console Advance DR-ID 300CL Software) which are themselves "previously cleared." This submission is about the system integrating these components, not about a novel AI/ML algorithm for image analysis or diagnosis.
• Basis for Clearance: The basis for clearance is "substantial equivalence" to a predicate device, focusing on functional, technical, and safety equivalence of the hardware and integrated pre-cleared software.
• Testing: The testing detailed is primarily non-clinical bench testing to confirm proper system operation and safety standards compliance (e.g., IEC standards, radiation performance, cybersecurity, wireless technology).
• Clinical Testing: The document explicitly states: "Clinical testing was not required to establish substantial equivalence because all digital x-ray receptor panels have had previous FDA clearance." This means no new clinical data (and thus no associated ground truth, expert reads, or AI performance metrics) was generated for this specific 510(k) submission.

Summary of what is present in the document that somewhat relates to the request, but not in the context of AI/ML acceptance criteria:

1. A table of acceptance criteria and the reported device performance:

   • Acceptance Criteria (Implicit): Substantial equivalence to the predicate device in terms of indications for use, configuration, generator specifications, panel interfaces, and meeting US performance standards (21 CFR 1020.30 and 21 CFR 1020.31). Also, compliance with various IEC standards (60601-1, 60601-1-2, 60601-1-3, 60601-1-6, 60601-2-28, 60601-2-54).
   • Reported Device Performance: The "Substantial Equivalence Chart" (page 5) compares the Rover to the predicate, showing "SAME" or "Equivalent" for most characteristics. "Bench testing indicate that the new devices are as safe and effective as the predicate devices. Proper system operation is fully verified upon installation. We verified that the modified combination of components worked properly and produced diagnostic quality images as good as our predicate generator/panel combination." (page 6)

2. Sample sized used for the test set and the data provenance: Not applicable in the context of AI/ML performance testing. Testing was system-level functional and safety verification.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for image interpretation was not established as part of this submission. The software components were previously cleared.

4. Adjudication method for the test set: Not applicable.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done: No, an MRMC study was not done.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: No, as this is a mobile x-ray system, not a standalone AI algorithm. The imaging software used is a pre-cleared component.

7. The type of ground truth used: Not applicable for AI/ML performance. The "ground truth" for the device's acceptable performance was its compliance with safety standards and its ability to produce diagnostic quality images comparable to the predicate, as verified through bench testing.

8. The sample size for the training set: Not applicable. This device is not an AI/ML algorithm that requires a training set.

9. How the ground truth for the training set was established: Not applicable.

In conclusion, the document describes the clearance of a medical device (a mobile X-ray system) based on substantial equivalence to a predicate, not the performance claims of a novel AI/ML-based diagnostic or analytical tool. Therefore, it does not provide the specific information requested about acceptance criteria and studies for AI/ML device performance.

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Ask a specific question about this device

Ask a specific question about this device