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ARTIS is a family of dedicated angiography systems developed for single plane and biplane diagnostic imaging and interventional procedures including, but not limited to, pediatric and obese patients.

Procedures that can be performed with the ARTIS family include cardiac angiography, neuro-angiography, general angtography, rotational angiography, multipurpose angiography and whole body radiographic/fluoroscopic procedures as well as procedures next to the table for i.e. patient extremities. This does not include projection radiography.

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 Xray imaging, surgery and interventions.

The ARTIS systems can also support the acquisition of position triggered imaging for spatial data synthesis.

The ARTIS systems include also the software option DynaCT with following indications for use:

DynaCT is an X-ray imaging software option, which allows the reconstruction of two-dimensional images acquired with a standard angiographic C-arm device into a three-dimensional image format.

DynaCT is intended for imaging both hard soft tissues as well as other internal body structures for diagnosis, surgical planning, interventional procedures and treatment follow-up.

The ARTIS pheno (VE30A) is a multi-axis system, specifically designed to meet the growing demands of high-end imaging for interventional radiology, interventional cardiology, and minimally invasive and hybrid surgery procedures. The stand allows positioning in angular, orbital, lateral, longitudinal, and vertical directions, leveraging the flexible isocenter. The ARTIS pheno (VE30A) is equipped with a robotic multi-axis floor stand, C-arm, flat panel detector, x-ray tube, collimator, high voltage generator, patient table, and image post-processing. The ARTIS pheno is partially coated with an optional anti-microbial coating. syngo Application Software is optional, and available for the support of dedicated clinical workflows.

The ARTIS pheno (VE30A) covers the complete range of angiographic applications, cardiac angiography, neuro-angiography, general angiography, surgery and surgical angiography, multipurpose angiography, rotational angiography, radiographic/fluoroscopic procedures.

The following components are configured to create the floor configuration:

• (1) Floor stand with C-arm, X-ray tube assembly and FD
• (2) Patient table
• (3) Display ceiling suspension with displays
• (4) Footswitch for releasing radiation
• (5) Control console for controlling the stand, patient table, collimator, and imaging system

lmages and operating elements are displayed on screens. Different display variants are used to visualize image and information content. Panoramic display configurations or large displays can be used, configurable to visualize multiple images and information content in various layouts.

Post-processing can be done in the exam room or in the control room that offers monitors as well, with a footswitch location in the exam room or the control room. The ARTIS pheno (VE30A) is capable of 2D and 3D imaging.

Other systems and software synqo Application Software, syngo X Workplace, Sensis Vibe, and or third-party systems may also be integrated into the ARTIS pheno (VE30A) screen configuration. Different screen configurations and layouts are possible in the examination room and the control room.

The provided text describes modifications to an existing device, ARTIS pheno (VE21) to create ARTIS pheno (VE30A). The study is primarily a non-clinical performance evaluation focused on verifying that the changes introduced in VE30A do not negatively impact the safety and effectiveness compared to the predicate device.

Here's a breakdown of the requested information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly list a table of "acceptance criteria" alongside specific "reported device performance" values in a quantitative manner for most of the listed modifications. Instead, it generally states that "All testing results passed" and "All test results met all acceptance criteria" for various tests performed on the modifications.

However, based on the descriptions, we can infer some general acceptance criteria and the reported performance as "met" or "passed".

2. Sample size used for the test set and the data provenance

The document does not specify a quantitative sample size for any test set (e.g., number of images, number of patients, number of cases). The testing appears to be primarily focused on system-level verification and validation of the software and hardware modifications rather than a clinical study involving patient data.

• Test Set: Not explicitly stated as a distinct "test set" in the context of clinical data. The testing involved "software functional, verification, and System validation testing," "NOMSIE algorithm, overlay, configuration, vessel presentation DSA, and roadmap testing," and "CNR image Quality exposure control, fluoro auto exposes values cardiology, DR, CNR, DSA, and neuro testing." These are likely performed on test harnesses, simulated data, and potentially a limited number of test cases or phantoms.
• Data Provenance: Not applicable in the context of clinical data. The testing is non-clinical.

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

This information is not provided. Since the study is a non-clinical performance evaluation, it did not involve clinical experts establishing ground truth in the traditional sense for a diagnostic AI device. The "ground truth" for the non-clinical tests would be the expected functional and performance outcomes defined by engineering specifications.

4. Adjudication method for the test set

Not applicable. There is no indication of multiple readers or an adjudication method for establishing ground truth from human experts, as this was not a clinical study involving diagnostic interpretations.

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The device is an image-intensified fluoroscopic x-ray system, and the changes are related to system software, hardware, and workflow features, not a diagnostic AI component intended for human reader assistance.

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

The device itself is an imaging system. The "algorithms" mentioned (e.g., NOMSIE, Structure Scout) are integrated parts of the overall imaging functionality. While these algorithms perform specific tasks (like image quality enhancement, reconstruction, element identification), their "standalone" performance isn't described in isolation from the system's output. The performance evaluation is inherently within the context of the ARTIS pheno system. It's not an AI model providing a separate diagnostic output.

7. The type of ground truth used

The ground truth used for these non-clinical tests would be based on:

• Engineering specifications and requirements for software functionality.
• Expected image quality parameters (e.g., CNR for Structure Scout) as measured by phantoms or controlled test conditions.
• Defined operational workflows for usability features.
• Compliance with recognized standards for electrical safety, performance, and EMC.

It is not based on expert consensus, pathology, or outcomes data.

8. The sample size for the training set

This information is not applicable and not provided. The document describes modifications to an existing x-ray system, implying traditional software and hardware development and testing, not the training of a machine learning model from a distinct "training set" of data.

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

This information is not applicable and not provided as no machine learning training set is mentioned.

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