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The XGEO GU60A Digital X-ray Imaging System is intended for use in generating radiographic images of human anatomy by a qualified/trained doctor or technician. This device is not intended for mammographic applications.

The XGEO GU60A digital X-ray imaging system is to be used to take and store image for diagnosis of patients. It consists of the High voltage generator (HVG), U-arm positioner, Detector, X-ray tube, Collimator and etc.

The provided text does not contain detailed information about the acceptance criteria or a study that proves the device meets specific performance criteria beyond general safety and effectiveness.

Here's an analysis of what is and isn't present in the document based on your request:

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

• The document states that "Non-clinical testing and Clinical Testing were conducted in accordance with Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices" and that "All test results are satisfied."
• It also mentions differences from the predicate device in "pixel pitch, high contrast limiting resolution, DQE, MTF and communication," noting that the proposed device has higher DQE and MTF values. These are performance metrics, but no specific acceptance criteria or quantitative performance results are provided in a table.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

• This information is not provided in the document.

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)

• This information is not provided in the document.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set

• This information is not provided in the document.

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

• The text describes a digital X-ray imaging system, not an AI-assisted diagnostic tool. Therefore, an MRMC study comparing human readers with and without AI assistance is not applicable and not mentioned.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done

• As the device is an X-ray imaging system and not an algorithm for diagnosis, standalone algorithm performance is not applicable and not mentioned. The device produces images for a qualified/trained doctor or technician to interpret.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• This information is not provided in the document.

8. The sample size for the training set

• This information is not provided in the document. The device is a hardware imaging system, not a machine learning algorithm that requires a training set in the typical sense.

9. How the ground truth for the training set was established

• This information is not provided as it's not an AI/ML device in the context of requiring ground truth for a training set.

Summary of available information related to acceptance criteria and device performance:

The document primarily focuses on demonstrating substantial equivalence to a predicate device (XGEO GU60A, K123106) by highlighting that observed differences in technical specifications (pixel pitch, high contrast limiting resolution, DQE, MTF, communication) do not negatively impact safety or effectiveness.

• Acceptance Criteria (Implicit/General):

  • Compliance with various electrical, mechanical, environmental safety, and performance standards (ES 60601-1:2012, IEC 60601-1-3:2008, IEC 60601-2-28:2010, IEC 60601-2-54:2009, 21CFR1020.30, 21CFR1020.31, IEC 60601-1-2:2007).
  • Compliance with "Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices" for non-clinical and clinical testing.
  • No new potential safety risks compared to the predicate device.
  • Performance "as well as" the predicate device despite differences in certain technical specifications.
  • The proposed device has "higher DQE value" and "higher MTF value" compared to the predicate, implying improvement in image quality.

• Reported Device Performance:

  • "All test results are satisfied" (in reference to the listed standards and guidance documents).
  • The device has a different (likely smaller, leading to higher resolution) pixel pitch.
  • Higher high contrast limiting resolution (due to pixel pitch).
  • Higher DQE value (affects image quality).
  • Higher MTF value (affects image quality).
  • Supports both wired and wireless communication (predicate only wired).

No specific quantitative acceptance criteria or detailed results of performance studies are presented in this 510(k) summary beyond the general statement that all tests were satisfied and the device performs at least as well as the predicate, with some technical improvements.

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The XGEO GU60 Digital X-ray Imaging System is intended for use in generating radiographic images of human anatomy by a qualified/trained doctor or technician. This device is not intended for mammographic applications.

The XGEO GU60 digital X-ray imaging system is to be used to take and store image for diagnosis of patients. It consists of the High voltage generator (HVG), U-arm positioner, Detector, X-ray tube, Collimator and etc.

The provided text does not contain detailed information about specific acceptance criteria for device performance, nor does it describe a study that explicitly proves the device meets such criteria through quantitative performance metrics. This document is a 510(k) Premarket Notification summary, which focuses on demonstrating substantial equivalence to predicate devices rather than proving specific performance beyond general safety and effectiveness standards.

However, based on the information provided, here's what can be gathered regarding safety and performance testing:

Key Takeaways from the Document:

• Focus on Substantial Equivalence: The primary goal of this 510(k) submission is to assert that the XGEO GU60 is "substantially equivalent" to predicate devices (Revolution XR/d and Diamond-5A). This means demonstrating that it is as safe and effective as commercially available devices.
• Compliance with Standards: The document states that electrical, mechanical, environmental safety, and performance testing was conducted according to various IEC and CFR standards (e.g., IEC 60601-1, IEC 60601-1-3, IEC 60601-2-54, 21CFR1020.30, 21CFR1020.31). EMC testing adhered to IEC 60601-1-2(2001).
• Non-Clinical and Clinical Testing: It mentions that "Non-clinical testing and Clinical Testing were conducted in accordance with Solid-state X-ray Imaging System Guidance. All test results were satisfied." However, no details about these tests, their methodology, or specific acceptance criteria are provided.
• Image Quality Differences with No Safety Impact: The document transparently lists differences between the XGEO GU60 and its predicate devices in aspects like detector type (phosphor), number of pixels, pixel pitch, high contrast limiting resolution, DQE, and MTF. Crucially, it asserts that these differences "affect the image quality but it has not effect on safety" and "do not have an effect on safety and efficiency compared with the predicate device." This implies that while there might be differences in image quality characteristics, they were considered within an acceptable range for safety and intended use.

Regarding the specific questions, most of the requested information is not present in this 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance: This information is not explicitly provided. The summary states that "All test results were satisfied" for various standards, but it does not specify the quantitative acceptance criteria for device performance (e.g., specific thresholds for spatial resolution, contrast detectability, or dose) or the numerical results achieved by the XGEO GU60.

   • Implied Acceptance Criteria: The implicit acceptance criterion for this 510(k) is "substantial equivalence" to the predicate devices and compliance with the safety and performance standards cited. This means the device performed within the accepted ranges defined by those standards and was deemed to not introduce new safety risks or reduce effectiveness compared to the predicates.

2. Sample Size used for the Test Set and Data Provenance: Not specified in the document.

3. Number of Experts used to establish the Ground Truth for the Test Set and Qualifications: Not specified in the document. The mention of "Clinical Testing" implies human involvement, but no details are given.

4. Adjudication Method for the Test Set: Not specified.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study: Not mentioned or implied. The focus is on the device itself and its equivalence, not on human reader performance with or without AI assistance.

6. Standalone Performance Study: The document implicitly discusses standalone performance through its comparison of technical specifications (DQE, MTF, resolution) with predicate devices and its statement that "all test results were satisfied" for non-clinical and clinical testing. However, a dedicated study specifically labeled as "standalone" with detailed metrics is not described. The document largely speaks to the inherent capabilities of the device in comparison to its predicates.

7. Type of Ground Truth Used: Not specified. For an X-ray imaging system, ground truth for image quality often involves phantom studies (non-clinical) and potentially clinical image review against established diagnostic criteria or other imaging modalities (clinical). The document only broadly mentions "Non-clinical testing and Clinical Testing."

8. Sample Size for the Training Set: Not applicable as this document describes a traditional X-ray imaging system, not an AI/machine learning device that would require a "training set."

9. How the Ground Truth for the Training Set was Established: Not applicable (see point 8).

In conclusion, this 510(k) summary primarily focuses on regulatory compliance and demonstrating substantial equivalence through adherence to recognized standards and a component-level comparison to predicate devices, rather than providing detailed quantitative performance studies with specific acceptance criteria and outcome metrics that would be expected for an AI-powered device or a more novel technology.

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