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The purpose of AcuityUDR is to acquire, store, communicate, display and process medical X-ray images. These radiographic systems are intended for use by a qualified/trained physician or both adult and pediatric subjects for taking diagnostic x-rays. Not for mammography, interventional, or fluoroscopy use.

RadmediX AcuityUDR combines components into a complete stationary x-ray system, including tube stand, tube head, collimator, generator, a digital radiography panel, and image acquisition software. 21CFR §892.1680 Identification. A stationary x-ray system is a permanently installed diagnostic system intended to generate and control x-rays for examination of various anatomical regions. This generic type of device may include signal analysis and display equipment supports, comports, component parts, and accessories.

The provided text is a 510(k) summary for the RadmediX AcuityUDR, which is a stationary X-ray system. The document focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed study proving performance against specific acceptance criteria. Therefore, most of the requested information regarding acceptance criteria, sample sizes for test and training sets, ground truth establishment, expert qualifications, and MRMC studies is not available in the provided text.

Here's what can be extracted based on the information provided:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state quantitative acceptance criteria for image quality or clinical performance. Instead, it refers to compliance with recognized standards and successful non-clinical testing.

2. Sample size used for the test set and the data provenance:

• Sample Size for Test Set: Not explicitly stated. The non-clinical testing involved acquiring DICOM images "from all major body structures." This implies a set of images, but a specific number is not provided.
• Data Provenance: Not explicitly stated. It describes "field tested a complete system" with images acquired from various body structures. This suggests either phantom studies or possibly human subject studies, but the nature (retrospective/prospective, country of origin) is not detailed.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

This information is not available in the provided text. The document states that the acquired images were "clinically acceptable," but it does not specify who made this determination, how many experts were involved, or their qualifications.

4. Adjudication method for the test set:

This information is not available.

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:

A multi-reader multi-case (MRMC) comparative effectiveness study was not done as this device is a stationary X-ray system, not an AI-powered diagnostic tool. The document states, "Clinical testing: Not required for a determination of substantial equivalence."

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

This is not applicable as the device is a hardware X-ray system with accompanying image acquisition software, not a standalone AI algorithm for interpretation. The software "communicates with the digital x-ray detector and allows acquisition and processing of x-ray images."

7. The type of ground truth used:

The text states that the non-clinical testing involved acquiring images that were "of high quality and contrast and clinically acceptable." This implies expert consensus or visual assessment of image quality was used as a form of ground truth, but the specifics of how this was established are not detailed. There is no mention of pathology or outcomes data being used for this device's non-clinical testing.

8. The sample size for the training set:

The concept of a "training set" is not applicable as the device is a hardware X-ray system with image acquisition software, not a machine learning or AI-based diagnostic algorithm that requires a training set. The software is "previously cleared" and "used unmodified."

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

This is not applicable for the same reason as above.

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