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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 applications

The PIXX2430 is s digital radiography system, featuring an integrated flat panel digital detector (FPD). It is 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 panels (K182533, PIXX 1717, PIXX 1212) retaining the Wi-Fi wireless features and rechargeable battery operation. The scintillator is Csl only. The only size available is 12 x 10 inch. It operates either wirelessly or by hard wired Ethernet connection. The power source is rechargeable battery, which lasts for 360 images or 6 hours in standby. It has a finer than usual pixel pitch at 85 µm (finer resolution). Our imaging software is unchanged from our predicate device, K182533. Image storage functionality: PIXX2430 supports the internal storage of raw image data. Wireless Information: This digital panel employs the same wireless functionality as our predicate panels (K182533) using IEEE802.11ac, backward compatible. The operational characteristics can be summarized this way: Transfer power, ~ 100mW; Frequency: 2.4 gHz, or 5 gHz. Security, WPA2; Signal range: Approximately 100 feet. Both medical and nonmedical devices can use IEEE802.11ac Wi-Fi, and this technology is designed to handle multiple devices using the same technology simultaneously.

The provided text is a 510(k) summary for the PIXX2430 Digital Diagnostic X-Ray Receptor Panel. It focuses on demonstrating substantial equivalence to a predicate device (K182533) rather than defining and proving acceptance criteria for an AI/ML powered device. Therefore, much of the requested information regarding AI/ML acceptance criteria, study design (MRMC, standalone), ground truth adjudication, and training/test set details is not present in the provided document.

However, based on the non-AI device context, I can extrapolate and provide information where available, and indicate where the information is missing.

Here's an attempt to answer your request based on the provided document, highlighting the missing AI/ML specific details:

The PIXX2430 Digital Diagnostic X-Ray Receptor Panel is a digital radiography system intended to replace radiographic film/screen systems for general radiographic images of human anatomy. The acceptance criteria and the study proving the device meets these criteria are framed within the context of demonstrating substantial equivalence to a predicate device (K182533), rather than the performance of an AI/ML algorithm.

1. Table of Acceptance Criteria and Reported Device Performance

Given this is a non-AI device seeking substantial equivalence, the "acceptance criteria" are generally aligned with demonstrating that the new device is "as safe and effective" as the predicate. The performance metrics focus on image quality and physical/electrical characteristics.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: The document mentions "Clinical images collected" and "Clinical images obtained in accordance with Guidance for the Submission of 510(k)s for Solid State X-ray Imaging Devices." However, the specific sample size of the clinical image test set is not provided.
• Data Provenance: Not explicitly stated (e.g., country of origin). The study seems to be internally conducted by Pixxgen.
• Retrospective or Prospective: Not specified whether the clinical images were collected retrospectively or prospectively.

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Number of Experts: "a Board Certified Radiologist" (singular) was used.
• Qualifications: "Board Certified Radiologist." No further detail (e.g., years of experience, subspecialty) is provided.

4. Adjudication Method for the Test Set

• Adjudication Method: "evaluated by a Board Certified Radiologist." This implies a single reader assessment, hence no multi-reader adjudication method (like 2+1 or 3+1) was used or described.

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

• MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done (or at least, not described in this 510(k) summary). The evaluation was by a single radiologist to confirm "excellent diagnostic quality" of the new device's images, comparing them to the predicate's as a basis for equivalence.
• Effect Size: Not applicable, as no MRMC study was performed.

6. Standalone (Algorithm Only) Performance Study

• Standalone Study: Not applicable. This device is a digital X-ray receptor panel, not an AI/ML algorithm. Its performance is assessed as a component producing images for human interpretation, not as an algorithm providing diagnostic outputs independently.

7. Type of Ground Truth Used

• Type of Ground Truth: The ground truth for the "clinical image inspection" was expert consensus (from a single Board Certified Radiologist) on the diagnostic quality of the images produced by the device. It was not based on pathology, outcomes data, or a panel of experts.

8. Sample Size for the Training Set

• The document describes the device itself, not an AI/ML algorithm. Therefore, there is no concept of a "training set" for an algorithm. The device's design and engineering are based on established X-ray detector physics and comparison to a predicate device.

9. How the Ground Truth for the Training Set Was Established

• Not applicable, as there is no AI/ML algorithm with a training set. The device's "training" in the manufacturing sense involves engineering, quality control, and adherence to performance specifications, not data-driven machine learning.

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