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Indicated for use in general radiographic images of human anatomy. It is intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures, excluding fluoroscopic, angiographic, and mammographic applications.

The PIXX series are digital radiography systems, featuring an integrated flat panel digital detector (FPD). They are designed to perform digital radiographic examinations as a replacement for conventional film. This integrated platform provides the benefits of PACS with the advantages of digital radiography for a filmless environment and improves cost effectiveness. The major functions and principle of operation of the updated panels are the same as our previous panel retaining the Wi-Fi wireless features. The modification involves adding battery power capability to the devices in our previous submission K180976.

Here's a breakdown of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test set sample size: Not explicitly stated for specific image evaluations. The device modification primarily involved adding battery power, and the manufacturer stated that "Clinical images were not required because the panel technology did not change from K180976."
• Data provenance: Not explicitly stated, as no clinical image study was conducted for this submission. The predicate device (K180976) would have had its own testing data.

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

• Not applicable, as no clinical image study requiring expert review of a test set was conducted for this submission.

4. Adjudication method for the test set:

• Not applicable, as no clinical image study requiring adjudication was conducted for this submission.

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:

• No MRMC comparative effectiveness study was done. The submission is for Digital Diagnostic X-ray Receptor Panels, which are imaging hardware, not an AI-enabled diagnostic device.

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

• No standalone algorithm performance study was done or is applicable for this type of hardware device.

7. The type of ground truth used:

• Not applicable for image-based ground truth related to a diagnostic algorithm, as this is a hardware device. The ground truth for this submission was based on bench testing against established engineering and safety standards (e.g., IEC/UL for electrical safety, IEC for EMC, IEC for battery safety) and performance specifications (Pixel Pitch, Limiting Resolution, DQE, MTF, A/D Conversion, etc.) which were determined to be identical to the predicate device. For the new battery feature, battery life testing was conducted to verify its stated performance.

8. The sample size for the training set:

• Not applicable, as this is a hardware device and not an AI/algorithm-based system requiring a training set.

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

• Not applicable, as this is a hardware device and not an AI/algorithm-based system requiring a training set or its associated ground truth establishment.

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Indicated for use in general radiographic images of human anatomy. It is intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures, excluding fluoroscopic, angiographic, and mammographic applications

The PIXX series are digital radiography systems, featuring an integrated flat panel digital detector (FPD). They are designed to perform digital radiographic examinations as a replacement for conventional film. This integrated platform provides the benefits of PACS with the advantages of digital radiography for a filmless environment and improves cost effectiveness. The major functions and principle of operation of the updated panels are the same as our previous panel retaining the Wi-Fi wireless features.

The provided document details the 510(k) summary for PIXXGEN Corporation's Digital Diagnostic X-Ray Receptor Panels. It does not describe a study involving an AI/algorithm that meets specific acceptance criteria. Instead, it focuses on the substantial equivalence of new digital X-ray receptor panels (PIXX 1717, PIXX 1417, PIXX 1212) to a legally marketed predicate device (ATAL 9, K152151).

Therefore, I cannot fulfill the request to describe the acceptance criteria and a study proving an AI/algorithm device meets those criteria based on the provided text. The document primarily discusses the performance of the physical imaging devices and their comparison to a predecessor, not the performance of an AI or algorithm itself.

The clinical "study" mentioned is purely for image quality validation by a radiologist against a predicate device, not for assessing an AI's diagnostic performance. There is no mention of a test set, training set, ground truth acquisition methodology (beyond radiologist evaluation), or any of the detailed metrics typically associated with AI/algorithm performance studies.

However, I can extract information related to the device's technical specifications and the "clinical testing" that was done to demonstrate equivalent image quality for the new panels as compared to the predicate device.

Here's a breakdown of what is available in the document, framed as if it were a "device performance" rather than an "AI performance" study:

Acceptance Criteria and Device Performance (for the X-ray Receptor Panels)

The acceptance criteria for these X-ray receptor panels are based on demonstrating "equal or better image quality" compared to the predicate device, alongside meeting various technical and safety standards.

1. Table of Acceptance Criteria and Reported Device Performance (for X-ray Panels):

Note: The document states "some measurements are slightly higher, and some are very slightly lower" for DQE/MTF, but overall concluded "very similar" and within "possible measurement error." The table reflects the reported values.

2. Sample Size and Data Provenance (for "Clinical Testing" of Image Quality):

• Sample Size for Test Set: Not explicitly stated. The document mentions "Clinical images were acquired." It does not specify the number of images or cases.
• Data Provenance: Not specified regarding country of origin. The study was retrospective in nature, as images were "acquired and evaluated." There is no indication of a prospective study design.

3. Number of Experts and Qualifications for Ground Truth:

• Number of Experts: One.
• Qualifications of Experts: "a board certified radiologist." No mention of years of experience.

4. Adjudication Method for the Test Set:

• Adjudication Method: None. A single board-certified radiologist made the evaluation.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

• MRMC Study: No, an MRMC study was not done. The evaluation was performed by a single radiologist to subjectively compare the image quality of the new panels against the predicate.

6. Standalone Performance (i.e. algorithm only without human-in-the-loop performance):

• This question is not applicable as the document is about physical X-ray receptor panels, not an AI or algorithm. The performance metrics reported (DQE, MTF, Limiting Resolution) are intrinsic to the hardware.

7. Type of Ground Truth Used:

• The "ground truth" for the image quality comparison was established by expert consensus (of a single radiologist), who concluded that "the images from the new panels are as good as or better than the images acquired with the predicate panel." This is a subjective assessment of image quality, not disease presence/absence based on pathology or outcomes data.

8. Sample Size for the Training Set:

• Not applicable. This document describes a medical device (X-ray panels) and does not involve a "training set" for an AI or algorithm. The "clinical images" evaluated were for validation/comparison, not for training.

9. How the Ground Truth for the Training Set was Established:

• Not applicable. As there is no training set for an AI, this question is irrelevant to the provided document.

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