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The Quix Digital Radiography Upgrade is based on a solid state imaging device and is intended for use in general radiographic examinations and applications wherever conventional film-screen systems may be used, excluding mammography, fluoroscopy, and angiography.

The Quix Digital Radiography Upgrade enables a conventional film-screen X-ray system to perform digital radiography exams by replacing the film-screen and the film-screen bucky with a digital bucky and operator console. The digital bucky incorporates a selenium-based flat panel detector with 16" x 17" imaging area. Images are displayed in approximately 10 seconds after exposure over a wide range of dose settings. The operator console provides local image storage and communicates with other network devices using DICOM 3.0 protocols.

The provided text is a 510(k) summary for the "Quix Digital Radiography Upgrade" device. This document is a premarket notification to the FDA to demonstrate that the device is substantially equivalent to legally marketed predicate devices. It focuses on comparing the new device's technical specifications and intended use against existing products.

Crucially, this document does not contain a study that proves the device meets acceptance criteria in the format typically used for performance claims of AI/ML-based devices (e.g., sensitivity, specificity, AUC, human reader studies).

Instead, the acceptance criteria for this type of device (a digital X-ray system) and the "study" demonstrating its equivalence are based on a comparison of its physical and performance specifications to those of existing, legally marketed predicate devices. The "acceptance criteria" are implied by the performance metrics of the predicate devices.

Here's an attempt to extract and interpret the information based on the provided text, acknowledging the limitations inherent in a 510(k) for a non-AI/ML device:

1. Table of acceptance criteria and the reported device performance

For a device like the Quix Digital Radiography Upgrade, the "acceptance criteria" are implicitly defined by the performance characteristics of its predicate devices. The "reported device performance" is the specifications of the Quix DR Upgrade.

Note on Acceptance Criteria: The "acceptance criteria" are effectively met if the new device's performance characteristics are considered equivalent to or comparable with the predicate device, especially for key aspects like intended use, anatomical sites, target population, and diagnostic image quality (as implied by spatial resolution, dynamic range, etc.). Some parameters, like pixel size or dynamic range, may differ but are justified as not raising new questions of safety or effectiveness.

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

The document does not describe a specific clinical test set or dataset in the manner of an AI/ML study. The "test set" here refers to the device itself and its specifications. The comparison is based on the technical specifications of the new device against the known specifications of existing predicate devices.

Therefore, there is:

• No specific sample size for a test set of images/patients.
• No data provenance mentioned in terms of country of origin or retrospective/prospective nature of a clinical image dataset.

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

This information is not applicable and not provided in this type of 510(k) submission. Ground truth, in the context of AI/ML device evaluation, usually refers to the verified diagnosis or condition of a patient based on expert review or pathology. For a digital radiography system, the "ground truth" is the physical performance characteristics of the device itself (e.g., resolution measurements, dose response curves), which are measured using standard physics and engineering methods, not expert clinical interpretation of images from a test set.

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

This is not applicable and not provided. Adjudication methods are used in clinical studies where multiple human readers assess a case and their interpretations are then consolidated to establish a robust ground truth or measure inter-reader agreement. This 510(k) does not describe such a clinical study.

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

There was no MRMC comparative effectiveness study described for this device. This document pertains to a new digital X-ray capture system, not an AI-assisted diagnostic tool. Therefore, there's no mention of human reader improvement with or without AI assistance.

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

This is not applicable. The device is a digital radiography upgrade, effectively a hardware system for acquiring X-ray images, not a standalone AI algorithm. Its performance is inherent in the image acquisition and processing capabilities it provides.

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

The "ground truth" for demonstrating substantial equivalence for this device category relies on technical specifications and physical performance measurements (e.g., spatial resolution, dynamic range, image processing speed, pixel size) of the Quix DR Upgrade compared to predicate devices. There is no mention of expert consensus on clinical findings, pathology, or outcomes data in this submission.

8. The sample size for the training set

This is not applicable and not provided. This device is a digital X-ray capture system and does not involve AI/ML technology that would have a "training set" of data.

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

This is not applicable and not provided for the same reasons as point 8.

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