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

VIVIX-S VW, a series for of flat panel detectors models named; FXRD-2530VW, FXRD-2530VW PLUS, FXRD-3643VW, FXRD-3643VW PLUS, FXRD-4343VW, FXRD-4343VW PLUS, with imaging areas of 25cm x 30cm, 36cm x 43cm, 43cm, respectively. The device intercepts x-ray photons and the scintillator emits visible spectrum photons that illuminate an array of photo (a-SI)-detectors that create electrical signals. After the electrical signals are generated, it is converted to digital value, and the Software which acquires and processes the data values from the detector. The resulting digital images will be displayed on monitors. These devices should be integrated with an operating PC and an X-Ray generator. It can be utilized to digitalize x-ray images and transfer for radiography diagnostic.

The retrieved text discusses the VIVIX-S VW digital flat panel X-ray detector, which is being reviewed for 510(k) clearance. The focus of the document is to demonstrate "substantial equivalence" of the device to previously cleared predicate devices, rather than establishing acceptance criteria for a new AI/CADe device with associated clinical studies that specifically prove these criteria are met.

Therefore, the provided text does not contain the detailed information required to fill out all the requested fields regarding acceptance criteria in the context of an AI/CADe device, as it is a 510(k) summary for a general X-ray detector. Specifically, it lacks information on:

• Specific acceptance criteria for clinical performance (e.g., sensitivity, specificity, AUC values with thresholds).
• The methodology for establishing ground truth for a test set (e.g., how experts determined disease presence/absence in a test set, number of experts, qualifications).
• Adjudication methods.
• MRMC comparative effectiveness study details (effect size of human readers with/without AI assistance).
• Standalone algorithm performance (since this is an X-ray detector, not an AI algorithm).
• Sample size and ground truth for a training set (which would be relevant for an AI model).

However, based on the information available, here's what can be extracted and inferred to the best extent possible for a general X-ray detector:

1. A table of acceptance criteria and the reported device performance

For an X-ray detector, acceptance criteria are primarily related to physical and imaging performance characteristics, not diagnostic accuracy in the way an AI algorithm would be evaluated. The text focuses on demonstrating equivalence to predicate devices using these metrics.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size for Test Set: Not explicitly stated. The document mentions "a comparison test was conducted" for non-clinical data and "A single-blinded concurrence study" for clinical data, but without specific numbers of images or cases.
• Data Provenance: Not specified.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The study is described as a "single-blinded concurrence study," implying comparison between readers, but details on how ground truth was established are missing.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Adjudication Method: Not specified. The term "concurrence study" implies agreement, but the method for resolving discrepancies or establishing a definitive ground truth is not detailed.

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

• MRMC Study: The document describes a "single-blinded concurrence study" to confirm "equivalent diagnostic capability to the predicate devices." This is not an MRMC study comparing human readers with and without AI assistance, but rather a study comparing the diagnostic capability of images produced by the subject device versus predicate devices.
• Effect Size of Human Reader Improvement: Not applicable, as this was not an AI-assisted diagnostic study.

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

• Standalone Study: Not applicable. This device is a digital X-ray detector, not an AI algorithm. Its performance is inherent in the image quality it produces, which is then interpreted by a human. The "standalone" concept typically applies to AI algorithms.

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

• Type of Ground Truth: Not explicitly stated. For a "concurrence study" comparing diagnostic capability of images, the ground truth would likely be established by expert radiologists, possibly through consensus or by reference to other clinical information, but the method is not described.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable. This device is a hardware X-ray detector, not a machine learning algorithm 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 training set for a hardware device.

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