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VIVIX-S 1012N (FXRD-1012NA, FXRD-1012NB, FXRD-1012NAW and FXRD-1012NBW) is indicated for digital imaging solution designed as a general radiographic system for human anatomy. It is intended to replace film or screen based radiographic systems in all general purposes of diagnostic procedures. It is not to be used for mammography.

Models FXRD-1012NA, FXRD-1012NB, FXRD-1012NAW and FXRD-1012NBW intercept X-ray photons, and the scintillator emits visible spectrum photons that illuminate an array of photo (a-SI)-detectors that create an electrical signals. After the electrical signals are generated, these are converted to a digital value, and an image will be displayed on the monitor.

These devices should be integrated with an operating PC and an X-Ray generator to digitalize Xray images and transfer the digitalized images for radiography diagnostic.

Advanced digital image processing allows considerably efficient diagnosis, all kinds of information management, and image information sharing on the network.

The provided text does not contain detailed acceptance criteria or a comprehensive study design that allows for the extraction of all requested information. The document is a 510(k) summary for a digital X-ray detector, focusing on demonstrating substantial equivalence to predicate devices rather than a detailed performance study against specific acceptance metrics derived independently.

However, based on the available information, here's a breakdown of what can be inferred and what is missing:

Missing Information:

• Detailed, explicit acceptance criteria values (e.g., specific thresholds for DQE, MTF that had to be met by the new device, beyond "similar"). The document states "...meet the acceptance criteria...", but these criteria themselves are not listed with numerical targets.
• The exact sample size for the clinical test set (only "a single-blinded concurrence study" is mentioned).
• Number and qualifications of experts for ground truth establishment.
• Adjudication method for the test set.
• Details of any Multi-Reader Multi-Case (MRMC) comparative effectiveness study, including effect size.
• Standalone algorithm performance (this device is a digital X-ray detector, not an AI algorithm, so this concept doesn't directly apply).
• Sample size for the training set (as this is a medical device, not an AI model, there isn't a "training set" in the typical sense of machine learning).
• How ground truth for the training set was established (again, not applicable to this type of device).

Inferred Information based on the document:

The study primarily focuses on demonstrating substantial equivalence to predicate devices (K122865 and K122866) by comparing technological characteristics and performance metrics.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly list "acceptance criteria" with numerical thresholds that the new device must meet. Instead, it compares the performance of the subject device (VIVIX-S 1012N models) to the predicate devices. The "acceptance" is implicitly that the performance of the new device is similar or better than the predicate devices, particularly in areas like DQE and MTF.

Here's a table showing the performance comparison provided, which serves as the "reported device performance" against the implicitly accepted "similar to predicate" standard:

Note: For DQE and MTF values, the table for the "Reference Predicate Devices" (K122865) shows "-" for these performance metrics, while the "Primary Predicate Devices" (K122866) has values. The "Comparison test" in section 9 explicitly states comparison was done with K122866.

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

• Sample Size: Not explicitly stated for the clinical test set, only mentioned as "a single-blinded concurrence study."
• Data Provenance: Not specified (e.g., country of origin). The study is described as a "clinical test" which "complied with the requirements specified in the CDRH's Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices," suggesting a prospective or collected dataset for the purpose of the submission. It's not explicitly stated as retrospective or prospective.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified. The study is described as a "single-blinded concurrence study." This implies that evaluators were blinded to which images came from the new device versus the predicate, and their interpretations were compared for "concurrence," but the method of resolving discrepancies or establishing definitive ground truth from multiple readers is not detailed.

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

• Was it done?: The document describes a "single-blinded concurrence study" comparing images from the new device to the predicate device to confirm "equivalent diagnostic capability." This is a form of comparative study involving human readers (though the number of readers is not disclosed), but it is focused on demonstrating equivalence (non-inferiority) rather than improvement with AI assistance.
• Effect Size of Human Reader Improvement: Not applicable, as this is a comparison between two different X-ray detectors, not an AI-assisted workflow vs. unassisted human reading.

6. Standalone (Algorithm Only) Performance

• Was it done?: Not applicable. The device (VIVIX-S 1012N) is a digital X-ray detector, a hardware component that captures images. It is not an AI algorithm. The performance metrics (DQE, MTF, Resolution) are intrinsic to the image capture capabilities of the detector.

7. Type of Ground Truth Used

• Type of Ground Truth: For the "concurrence study," the ground truth was based on the "equivalent diagnostic capability" of the images as assessed by human readers ("experts"). This would typically mean that clinical interpretations of images from the new device were compared to interpretations of images from the predicate device to ensure they did not differ significantly in diagnostic content. It is a form of expert consensus on image diagnostic quality, rather than a definitive "true positive/negative" based on pathology or outcomes data.

8. The Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. This device is a digital X-ray detector, not an AI model requiring a training set.

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

• How Ground Truth Established: Not applicable.

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