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The DIGITAL RADIOGRAPHY CXDI-702C Wireless and CXDI-402C Wireless provides digital image capture for conventional film/screen radiographic examinations. This device is intended to capture, for display, radiographic images of human anatomy, and to replace radiographic film/screen systems in all general purpose diagnostic procedures. This device is not intended for mammography applications.

The CXDI-702C Wireless and CXDI-402C Wireless are solid-state x-ray imagers with approximate imaging areas of 350 x 426 mm and 415 x 426 mm, respectively. The detector intercepts x-ray photons, and the scintillator emits visible spectrum photons that illuminate an array of photodetectors that create electrical signals. After the electrical signals are generated, the signals are converted to digital values and the images will be displayed on monitors. The digital value can be communicated to the operator console via wired or wireless connection.
The subject of this Special 510(k) submission is a change to the Digital Radiography CXDI-710C Wireless and CXDI-410C Wireless to add the X-ray I/F unit option, update to the CXDI control software, change the IP Level, make changes to the case, and remove Standalone mode. The X-Ray I/F unit synchronizes the timing of the X-ray irradiation with the detector's capture and has been included in other Canon devices (CXDI-701C Wireless (K131106)). The X-Ray I/F Unit is an optional unit that allows the proposed device work together with several older units that use the X-ray I/F Unit instead of the multibox. The IP Level was changed from IPX7 to IP54. The Standalone mode was removed from the proposed devices. The imaging process to sharpen images, Edge Enhancement, was included in the Digital Radiography CXDI-710C Wireless and CXDI-410C Wireless, but adjustments of multiple imaging parameters were required to enhance structured edges. The optional feature, Advanced Edge Enhancement, for CXDI-702C Wireless and CXDI-402C Wireless automatically adjusts the six image processing parameters (Enhancement - Edge Enhancement, Enhancement - Edge Frequency, Enhancement - Contrast Boost, Dynamic Range Adjustment - Dark Region, Dynamic Range Enhancement - Bright Region, and Noise Reduction - Effect) by one button to enhance structures. The CXDI control software has been updated to a new version for functional improvements. The material of the casing of the detector has changed from fiberglass to magnesium alloy. Together, these changes to the Digital Radiography CXDI-710C Wireless and CXDI 410C Wireless make up the Digital Radiography CXDI-702C Wireless and CXDI-402C Wireless.

The provided text describes a 510(k) premarket notification for two digital radiography devices, the CXDI-702C Wireless and CXDI-402C Wireless. This submission is a "Special 510(k)," indicating that the changes made to the devices are minor and fall within established performance specifications, meaning a direct comparative effectiveness study with human readers (MRMC) might not have been the primary focus of the submission for overall device clearance, but rather a demonstration of continued equivalence through specific performance tests and comparative data with predicate devices.

The acceptance criteria and study proving the device meets these criteria can be inferred from the "Summary of Non-Clinical / Test Data" section and the "Comparisons with the predicate devices" table.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The submission focuses on demonstrating substantial equivalence to predicate devices (CXDI-710C Wireless and CXDI-410C Wireless) after specific modifications. Therefore, the "acceptance criteria" are primarily that the modified devices maintain performance comparable to the predicate devices and meet relevant safety and performance standards. The "reported device performance" is largely framed as successful verification and validation tests and comparability data.

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

• Sample Size: The document does not explicitly state a numerical sample size for the "non-clinical image comparisons" or "interviews with experienced clinicians." It refers to "tests performed on the models" and "non-clinical image comparisons." For a 510(k) Special submission, the focus is often on demonstrating that the modifications do not adversely affect performance, rather than a large-scale clinical trial.
• Data Provenance: Not explicitly stated. Given that it's a submission for products by Canon, Inc. (Japan), the non-clinical tests would typically be performed internally or by contracted labs. The "interviews with experienced clinicians" likely involved healthcare professionals in a relevant market, but the specific country is not detailed. The data is retrospective in the sense that it evaluates the modified device against known performance of the predicate device.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

• Number of Experts: Not explicitly stated for image evaluation.
• Qualifications of Experts: The document mentions "experienced clinicians" for interviews regarding the usability of the Advanced Edge Enhancement feature. Their specific qualifications (e.g., "radiologist with 10 years of experience") are not detailed. For the image comparisons, it implies internal testing and comparison to established predicate performance rather than requiring independent expert reads to establish ground truth in the same way a diagnostic AI would.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified. For non-clinical image comparisons demonstrating comparability, a formal adjudication process like 2+1 or 3+1 by multiple readers is not typically detailed in this type of 510(k) submission. It's more about objective image quality metrics and visual comparison against predicate performance.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• MRMC Study: No, the document does not indicate that a formal MRMC comparative effectiveness study was conducted. This type of study is more common for novel diagnostic AI devices where the primary claim is an improvement in human reader performance with AI assistance. For a Special 510(k) of a digital X-ray detector with minor modifications, the focus is on maintaining equivalence rather than demonstrating improvement over human readers.
• Effect Size: Not applicable, as an MRMC study was not described.

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

• Standalone Performance: Not applicable in the context of a digital X-ray detector. The device itself (the detector) processes images and provides them for display, but it doesn't have a diagnostic "algorithm" that operates independently to provide outputs like disease detection that would require a standalone performance evaluation in the same way a diagnostic AI software would. The "Advanced Edge Enhancement" is an image processing feature, not a diagnostic algorithm. The document explicitly states the "Standalone mode was removed from the proposed devices" for how the detector operates, meaning it always communicates with an operator console (human-in-the-loop for image review).

7. The Type of Ground Truth Used

• Ground Truth: For the non-clinical image comparisons, the "ground truth" would be established by comparing the images from the modified device against the known and accepted image quality and characteristics of the legally marketed predicate devices. This isn't "expert consensus" or "pathology" in the diagnostic sense, but rather a technical comparison of image properties (e.g., resolution, contrast, noise, and the visual appearance of anatomical structures) to ensure the modifications did not degrade quality. The "usability" of the Advanced Edge Enhancement was evaluated through clinician interviews, where their feedback implicitly serves as a form of ground truth for user experience.

8. The Sample Size for the Training Set

• Training Set Sample Size: The document does not describe a "training set" because the device is a digital X-ray detector, not an AI algorithm that learns from data. The "Advanced Edge Enhancement" is likely a rule-based or engineered image processing algorithm, not a machine learning model that requires a training set.

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

• Ground Truth for Training Set: Not applicable, as there is no mention of a training set for an AI algorithm.

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