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Artis zee / zeego is a family of dedicated angiography systems developed for single and biplane diagnostic imaging and interventional procedures including, but is not linited to, pedication and obese patients.

Procedures that can be performed with the Artis zee / zeego family include cardiac angiography, neuro-angiography, general angiography, rotational angiography, multipurpose angiography and whole body radiographic/fluoroscopic procedures as well as procedures next to the table for i.e. patient extremities.

Additional procedures that can be performed include angiography in the operating room, image guided surgery by X-ray, by image fusion, and by navigation systems. The examination table as an integrated part of the system can be used for X-ray imaging, surgery and interventions.

Artis zee / zeego can also support the acquisition of position triggered imaging for spatial data synthesis.

The Artis zee / zeego Modular Angiography System is designed as a set of components that may be combined into different configurations to provide specialized angiography systems. For these models, a new SW version VC14 will be available. The new SW version VC14 will allow additional functionality of currently used applications and is extended with new features (i.e. a wide screen display, a new OR table, and laser light crosshair). SW VC14 also includes an extension of the intended use to surgical angiography, including general surgical use for patients in the OR. The Artis zee / zeego Modular Angiography System with SW VC14 is substantially equivalent to the AXIOM Artis Modular Angiography System VC13 with all its components as described in the Device Description, Section 11 and the Substantial Equivalence Section 12.

This 510(k) submission (K090745) for the Siemens Artis zee / zeego family VC14 is a premarket notification for a modification to an existing device, rather than a submission for a novel device requiring extensive clinical performance studies. Therefore, much of the information typically sought for AI/ML device performance (like specific metrics, MRMC studies, and detailed ground truth establishment for a training set) is not present. The submission focuses on demonstrating substantial equivalence to the predicate device.

Here's an analysis based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present specific performance metrics or acceptance criteria for clinical performance that would typically be seen in a novel device's submission for AI/ML features. Instead, the acceptance criteria are implicitly tied to demonstrating that the modifications to the device (SW VC14, new OR table, wide screen display, laser light crosshair, and extended intended use to surgical angiography) do not compromise safety and effectiveness and maintain substantial equivalence to the predicate device.

Implicit Acceptance Criteria & Reported Performance:

The "Performance Data" section states: "Applicable testing was performed to evaluate the modifications to the Artis zee / zeego family. The test results were found to be acceptable as required by the respective test plans and protocols." However, the specific test plans, protocols, and their outcomes (i.e., the actual numerical performance against criteria) are not detailed in this summary. This generally refers to engineering verification and validation testing, not clinical performance studies with specific metrics like sensitivity/specificity for a diagnostic task.

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

Not applicable/Not mentioned. This submission does not describe a clinical test set with patient data for performance evaluation in the context of AI/ML or a diagnostic task. The "testing" mentioned refers to verification and validation of the device modifications.

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

Not applicable/Not mentioned. As there's no clinical test set in the traditional sense for a diagnostic algorithm, there's no mention of experts establishing ground truth for such a set.

4. Adjudication Method for the Test Set

Not applicable/Not mentioned.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

No. The document does not describe an MRMC study. This is a 510(k) for device modifications, not a new diagnostic algorithm requiring human-AI comparative effectiveness.

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

No. This device is an angiographic X-ray system, which is a hardware system with software functionality, not a standalone algorithm. The performance evaluation discussed would be related to the system's operational integrity, image quality, and safety, not an algorithm's diagnostic performance.

7. The Type of Ground Truth Used

Not applicable/Not mentioned. For the type of device and submission, "ground truth" as a clinical reference standard is not a central component of the evaluation described. The "truth" would be whether the device functions according to its specifications and safely.

8. The Sample Size for the Training Set

Not applicable/Not mentioned. This device does not appear to involve an AI/ML algorithm that requires a "training set" in the context of learning from data to perform a diagnostic or predictive task. It's an imaging system where software updates provide new features and functionality.

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

Not applicable/Not mentioned.

In summary: K090745 is a 510(k) submission for modifications (software version upgrades and minor hardware additions) to an existing "Angiographic X-ray System." It focuses on demonstrating that these modifications do not alter the substantial equivalence to the predicate device (Artis zee, Artis zeego SW VC13) and that the device remains safe and effective for its intended use, which has been slightly expanded to include surgical angiography. The regulatory review for this type of submission primarily involves engineering verification and validation testing, not clinical performance studies with patient cohorts, ground truth, or AI/ML specific metrics.

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