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The Ziehm 8000 mobile C-arm is intended for use in providing medical imaging for general populations. The device provides contactless fluoroscopic image capture, temporarily storing, and display of digital subtraction of cine loops during diagnostic, interventional and surgical procedures requiring X-ray imaging both inside and outside the operating room. Examples of clinical application may include cholangiography, endoscopic, urologic, lithotripsy, cardiac, orthopedic, vascular, pain management, angiographic, critical care, and emergency room fluoroscopy procedures.

This device does not support direct radiographic film exposures and is not intended for use in performing mammography.

The Ziehm 8000 mobile fluoroscopy system is comprised of a mobile stand with a C-Profile shaped support with both a mono-block high voltage generator assembly and Image Intensifier. These attach to either end of a C-Profile providing a fixed SID. The device performs 2D medical imaging using 4 axes of manual movement and one vertical axes of motorized movement. A user keyboard provides for concise user selectable anatomical programs and X-ray technique control. Integrated high-resolution flat panel display monitors directly mounted on the monitor cart providing the clinician with a precise angle for visualization of live fluoroscopic images of the patient's anatomy. This visualization helps to localize regions of pathology for surgical procedures. The mobile stand supports a cable bound fluoroscopic footswitch. The optional interface panel of the Ziehm 8000 provides connection of peripheral devices such as external monitors, thermal video printers, and image storage devices (USB, DVD) and DICOM fixed wire and wireless network interfaces.

Here's an analysis of the provided text regarding the acceptance criteria and the study that proves the device meets those criteria:

The document (510(k) K190497 for the Ziehm 8000) does not explicitly state "acceptance criteria" in a quantitative table format that is separate from the "reported device performance." Instead, it focuses on demonstrating substantial equivalence to a predicate device (Ziehm Quantum K051064) by comparing technological characteristics and overall performance. The "acceptance" is implied by demonstrating that the new device is either identical, similar, or improved in ways that do not raise new safety or effectiveness concerns compared to the predicate.

Given this, I will construct a table that reflects the comparison of features, where the predicate's performance or characteristic serves as the de facto "acceptance criteria" for substantial equivalence.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size: The document indicates that anthropomorphic (PMMA material) phantoms and anatomical simulation phantoms were used. It does not specify an exact number of phantoms or image sets, but states that "image comparison sets taken were representative of both the adult and pediatric populations." It refers to "individual image sets" being assessed.
• Data Provenance: The data was generated through non-clinical imaging and dose testing methods conducted in a lab setting by Ziehm Imaging GmbH. The document does not specify a country of origin for the data (beyond Ziehm Imaging GmbH being a German company and Ziehm Imaging, Inc. being in Orlando, FL), but it is implied to be internal testing. The study was retrospective in the sense that it compared the new device's performance to the existing predicate.

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

• Number of Experts: One expert was used.
• Qualifications of Expert: A board-certified Radiologist performed the assessment of individual image sets. No specific number of years of experience is provided.

4. Adjudication Method for the Test Set

• The adjudication method was "none" in the sense of a multi-reader consensus process. The document states, "A Radiologist performed an assessment of individual image sets. Radiologist conclusion, the image quality of the Ziehm 8000 results in a comparable patient care to the Predicate device (K051064)." This suggests a single expert's opinion formed the basis of the conclusion regarding image quality comparison.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The study focused on technical comparisons and the assessment of image quality by a single radiologist using phantom data.
• Effect Size of Human Readers: Not applicable, as no MRMC study was performed with human readers using AI assistance.

6. Standalone (Algorithm Only) Performance Study

• Yes, a standalone performance study was done, but it's important to clarify the context. The "device" in this case is a fluoroscopic X-ray system, which is hardware, not an AI algorithm. The performance evaluation was of the imaging system as a whole (X-ray monoblock generator, image intensifier, software for image processing/control). The "algorithm" aspect would refer to the device's inherent image processing capabilities. The comparison included non-clinical imaging and dose testing, evaluating the device's ability to provide reduced dose while maintaining image quality compared to the predicate, and confirming it met design inputs and user needs.

7. Type of Ground Truth Used

• The ground truth was established through expert assessment (Radiologist's conclusion) of images derived from anthropomorphic and anatomical simulation phantoms, alongside objective laboratory performance data (e.g., DQE, Contrast Ratio, Camera Matrix, dose measurements). The comparison was anchored to the performance of the legally marketed predicate device.

8. Sample Size for the Training Set

• The document does not specify a training set sample size. This is expected for a traditional medical device (like an X-ray system) rather than an AI/Machine Learning device that undergoes explicit "training." The device's "software architecture design is nearly identical" to the predicate, with modifications for "lower dose functionality, processing applications related to the optional low dose range, image, Variable beam limiting device, and device specific features." These refer to software features and processing rather than a learned AI model.

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

• Not applicable. As there's no mention of an explicit AI/ML component with a training set, the concept of a "ground truth for the training set" does not apply in the context of this 510(k) submission for the Ziehm 8000. The device's performance was evaluated against physical and historical (predicate) benchmarks.

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