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The Varian Nexus DR™ Digital X-ray Imaging System is a high resolution digital imaging system intended to replace conventional film techniques, or existing digital systems, in multipurpose or dedicated applications specified below. The Nexus DR™ Digital X-ray Imaging System enables an operator to acquire, display, process, export images to portable media, send images over a network for long term storage and distribute hardcopy images with a laser printer. I mage processing algorithms enable the operator to bring out diagnostic details difficult to see using conventional imaging techniques. Images can be stored locally for temporary storage. The major system components include an image receptor, computer, monitor and imaging software.

The Varian Nexus DR™ Digital X-ray Imaging System is intended for use in general radiographic examinations and applications (excluding fluoroscopy, angiography, and mammography).

The Varian Nexus DR™ Digital X-ray Imaging System is a high resolution digital imaging system designed for digital X-ray imaging through the use of an X-ray detector. The Varian Nexus DR™ Digital X-ray Imaging System is designed to support general radiographic (excluding fluoroscopy, angiography, and mammography) procedures through a single common imaging platform.

The modified device consists of an X-ray imaging receptor, Varian PaxScan 4336Wv4, computer, monitor, and the digital imaging software.

The provided document is a 510(k) premarket notification for the Nexus DR 100 Digital X-ray Imaging System (with PaxScan 4336Wv4). It focuses on establishing substantial equivalence to existing predicate devices.

Based on the provided text, the document primarily discusses non-clinical testing and general validation, rather than a specific study designed to meet predetermined acceptance criteria for a new AI or diagnostic algorithm's performance. The information requested in the prompt is highly relevant for studies proving the performance of AI/CADe/CADx devices. This submission, however, is for a digital X-ray imaging system, which is a hardware and software system for image acquisition and display, and not explicitly an AI-driven diagnostic tool in the sense of the prompt's questions.

Therefore, many of the questions regarding specific acceptance criteria for diagnostic performance, sample sizes for test sets, experts for ground truth, adjudication methods, MRMC studies, and training set details are not fully addressable from this document as it does not describe such a study for the device's diagnostic performance.

However, I can extract information related to the technological characteristics comparison which serves as a form of "acceptance criteria" for substantial equivalence.

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

For this 510(k) submission, "acceptance criteria" are not framed in terms of diagnostic performance metrics like sensitivity or specificity for a specific condition. Instead, the device's performance is compared against predicate devices based on technological characteristics and physical image quality parameters to demonstrate substantial equivalence. The "acceptance" is that these characteristics are equivalent or better than the predicates.

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

The document primarily describes non-clinical tests and a technological characteristics comparison to establish substantial equivalence. It refers to "Validation Protocols" and "predetermined test methods and corresponding acceptance criteria" but does not detail a specific "test set" of clinical images or patients in the sense of a diagnostic performance study. The data presented is characteristic measurements of the detector and system, not image data from patients for a diagnostic evaluation.

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

Not applicable. The document does not describe a clinical study where experts established ground truth for diagnostic decisions.

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

Not applicable. This type of adjudication method is used in diagnostic performance studies, which are not detailed in this submission.

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

Not applicable. This submission is for a digital X-ray imaging system, not an AI-assisted diagnostic device, and thus no MRMC study for AI assistance is described.

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

Not applicable, as this device is an imaging system and not primarily a standalone diagnostic algorithm.

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

Not applicable in the context of a diagnostic performance study. The "ground truth" for the non-clinical tests would be the measured physical properties of the system and detector according to standard testing methodologies (e.g., those detailed in the referenced FDA guidance for solid-state X-ray imaging devices).

8. The sample size for the training set

Not applicable. The document does not describe an AI/ML component with a "training set" for diagnostic performance. The device involves image processing algorithms, but these are typically deterministic or rule-based for image enhancement, not machine learning algorithms trained on large datasets for diagnostic classification.

9. How the ground truth for the training set was established

Not applicable, as there is no mention of a training set for a diagnostic AI/ML algorithm.

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