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VIVIX-S 1717V series is used for the general-purpose diagnostic procedures, and as well as intended to replace radiographic film/ screen systems. The VIVIX-S 1717V series is not intended for mammography applications.

VIVIX-S 1717V - Models FXRD-1717VA and FXRD-1717VB 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.

VIVIX-S 1717V - Models FXRD-1717VA and FXRD-1717VB are digital X-ray flat panel detectors, and each model has a 17 x 17 inch imaging area.

The scintillator used in FXRD-1717VA is Csl and Gadox was used for FXRD-1717VB.

Here's an analysis of the acceptance criteria and study detailed in the provided document:

Acceptance Criteria and Device Performance

The document doesn't explicitly state "acceptance criteria" in a separate, defined table with specific metrics that needed to be met to demonstrate equivalence. Instead, it relies on demonstrating substantial equivalence to a predicate device (VIVIX-S 1717N, K152894) through a comparison of technological characteristics and performance metrics, primarily DQE and MTF. The underlying acceptance criterion for these performance metrics is that they are "similar" to or within an acceptable range of the predicate device's performance.

Implicit Acceptance Criteria and Reported Device Performance:

The document concludes that "The results of these tests demonstrate that VIVIX-S 1717V - FXRD-1717VA and FXRD-1717VB Digital X-ray detectors meets the acceptance criteria and is adequate for this intended use."

Study Details

The primary study mentioned is a single-blinded concurrence study and a comparison test of non-clinical data.

1. Sample Size used for the test set and the data provenance:

   • Sample Size: Not explicitly stated for the clinical concurrence study. The document mentions "clinical images were provided," but doesn't quantify them. For the non-clinical data (DQE, MTF, spatial resolution), standard phantom images are typically used, but the quantity is not specified.
   • Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). It's likely prospective data gathered for the submission.

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

   • This information is not provided in the document. The term "ground truth" is not used in the context of the clinical study; rather, it refers to a "concurrence study," implying comparison of image interpretations.

3. Adjudication method for the test set:

   • Not explicitly stated. A "single-blinded concurrence study" suggests that readers were unaware of which device generated the image, and their interpretations were compared. However, the method for resolving discrepancies or establishing an ultimate "truth" for evaluation is not detailed.

4. 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, an MRMC comparative effectiveness study was not done in the context of evaluating AI assistance. This study aims to demonstrate substantial equivalence between two X-ray detectors (subject device vs. predicate device), not the impact of AI on human reader performance.

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

   • No, this device is an X-ray detector, not an AI algorithm. Therefore, a standalone performance evaluation of an algorithm is not applicable. The device's performance is assessed through image quality metrics (DQE, MTF, resolution) and a clinical concurrence study with human readers.

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

   • The document implies expert consensus/interpretation for the clinical concurrence study ("provide images of equivalent diagnostic capability"). For the non-clinical performance data (DQE, MTF, Resolution), the "ground truth" is established by physical measurements and adherence to international standards (IEC 62220-1).

7. The sample size for the training set:

   • This device is an X-ray detector, not an AI or machine learning algorithm that requires a "training set." Therefore, this question is not applicable to the information provided.

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

   • As this device does not involve an AI algorithm with a training set, this question is not applicable.

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