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ZEN-7000 Digital mobile Imaging system is designed to provide fluoroscopic and spot-film imaging of the patient during diagnostic, surgical and interventional procedures. Examples of clinical application may include cholangiography, endoscopy, urologic, orthopedic, neurologic, vascular, cardiac, critical care and emergency room procedures. The system may be used for other imaging applications at the physician's discretion.

ZEN-7000 mobile fluoroscopy system is an image intensified fluoroscopic system consisting of two mobile units: a Mainframe (C-Arm) and a Workstation. The Mainframe (C-Arm) is comprised of a high voltage generator, x-ray control, and a "C" shaped apparatus, which supports an X-ray tube and an image intensifier. The Mainframe is designed to perform linear and rotational motions that allow the user to position the x-ray imaging components at various angles and distances with respect to the patient. The Workstation is a mobile platform that supports image display monitors and recording devices.

The provided text does not contain detailed information about specific acceptance criteria or an in-depth study proving that the device, a Fluoroscopic X-Ray System (Model: ZEN-7000), meets explicit performance metrics.

Instead, the document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a legally marketed predicate device (GE OEC Medical Systems, Inc. OEC 9800 Plus, K021049). This type of submission generally relies on comparing the technical specifications and indications for use of the new device to the predicate, rather than providing extensive de novo clinical trial data with acceptance criteria and statistical proof.

Here's a breakdown of what can be inferred or is explicitly stated, with limitations due to the nature of the document:

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

The document does not explicitly state acceptance criteria in the format of a table with pass/fail thresholds. Instead, it lists the specifications of the ZEN-7000, implying that these align with or are comparable to those of a predicate device.

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 mentions "bench and clinical evaluation" in a very general sense. It does not provide details on:

• The sample size of patients or images used in any "clinical evaluation."
• The country of origin for any data.
• Whether any data was retrospective or prospective.

Given the nature of a 510(k) for an X-ray system, "clinical evaluation" often refers to demonstrating the system produces images of diagnostic quality, which might involve a limited number of phantom and/or human subject images, but not a large-scale clinical trial with statistical power for "performance."

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)

This information is not provided in the document. The document primarily focuses on technical specifications and substantial equivalence, not a study involving expert readers and ground truth for diagnostic accuracy.

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

This information is not provided in the document. As no specific study with expert readers and ground truth is detailed, no adjudication method would be mentioned.

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

• No, an MRMC comparative effectiveness study was not done (or at least, not reported in this document).
• The device is a conventional fluoroscopic X-ray system, not an AI-powered diagnostic tool. Therefore, the concept of "human readers improve with AI vs without AI assistance" is not applicable to this device as described.

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

• Not applicable. This is a hardware device (X-ray system), not an algorithm or AI software. Therefore, a standalone algorithm performance study is not relevant.

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

This information is not provided. As no specific diagnostic accuracy study is detailed, the type of ground truth is not mentioned. For a conventional X-ray system, "ground truth" for proving efficacy often relates to image quality metrics (resolution, contrast, noise) and dose, which are typically assessed through phantom studies and engineering specifications, rather than clinical diagnostic endpoints requiring pathology or outcome data in the context of a 510(k) submission.

8. The sample size for the training set

• Not applicable. This is a hardware device, not a machine learning algorithm that requires a training set.

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

• Not applicable. This is a hardware device, not a machine learning algorithm.

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