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The Ziehm Vision FD is intended for use in providing medical imaging for adults and pediatric populations, using pulsed and continuous fluoroscopic imaging. The device provides contactless fluoroscopic image capture, temporarily storing, and display of digital subtraction, and acquisition of cine loops during diagnostic, interventional and surgical procedures. Examples of clinical application may include pediatric, cholangiography, endoscopic, urologic, lithotripsy, orthopedic, neurologic, vascular, cardiac, angiographic, critical care, and emergency room fluoroscopy procedures.

The visualization of such anatomical structures assists the clinical outcome. This device does not support direct radiographic film exposures and is not intended for use in performing mammography. The system is not intended for use near MRI systems.

The Ziehm Vision FD 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 Flat Panel image receptor. 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 touch screen provides for concise user selectable anatomical programs and Xray 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 both a cable bound and optional wireless fluoroscopic footswitch. The Wireless footswitch operation allows for optimum positioning for the surgeon by removing the cable on the floor. The optional interface panel of the Ziehm Vision FD 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.

The provided document, a 510(k) Summary for the Ziehm Vision FD, describes a premarket notification for a modified fluoroscopic x-ray system. The study focuses on demonstrating substantial equivalence to a previously cleared predicate device (K193230), rather than establishing new acceptance criteria or proving clinical efficacy in a traditional sense. The changes primarily involve an additional 8-inch IGZO flat panel detector and updates to software and mechanical components.

Therefore, the acceptance criteria and study detailed below are framed within the context of a substantial equivalence submission, emphasizing performance comparison with a predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a substantial equivalence submission, the "acceptance criteria" are implied by the performance of the predicate device and the new device's ability to demonstrate comparable safety and effectiveness. The "reported device performance" refers to how the modified Ziehm Vision FD performs relative to the predicate.

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

The document describes "non-clinical image comparison with sets of images" and "non-clinical image and dose Lab testing" using "anatomical phantoms."

• Test set sample size: Not explicitly stated as a number of images or cases. It refers to "sets of images" and "anatomical phantoms."
• Data provenance: Non-clinical (phantom-based) lab testing. The country of origin for the data is not specified but the manufacturer is based in Germany. The testing appears to be retrospective in comparison to the predicate, as it's a verification and validation activity for a modified device.

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

• Number of experts: "A Radiologist performed an assessment of individual image sets." This implies one radiologist.
• Qualifications of experts: The document only states "A Radiologist." No specific experience level (e.g., "10 years of experience") is provided.

4. Adjudication Method for the Test Set

The document mentions that "A Radiologist performed an assessment of individual image sets" and provided a "conclusion." This suggests a single expert review rather than a formal adjudication method (like 2+1 or 3+1 consensus).

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

No, a Multi Reader Multi Case (MRMC) comparative effectiveness study was not done. The study described is a non-clinical, phantom-based image comparison assessed by a single radiologist for substantial equivalence (comparability) in image quality and safety, not for an improvement in human reader performance with or without AI assistance. The device is an imaging system, not an AI-powered diagnostic aid meant to directly improve human reader effectiveness.

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

The device itself is a fluoroscopic x-ray system, not an AI algorithm. Therefore, the concept of "standalone performance" for an algorithm doesn't directly apply here. The performance tests described (image quality, dose, safety standards compliance) characterize the device's inherent capabilities without human interpretation as part of the core performance metric.

7. The Type of Ground Truth Used

For the non-clinical image comparison and dose testing, the "ground truth" was established using:

• Anatomical phantoms: These provide a known, controlled imaging target.
• Radiologist assessment: A radiologist's qualitative assessment of image quality and how it compares between the modified and predicate devices.

8. The Sample Size for the Training Set

This submission is for a medical imaging device (hardware and associated software), not a machine learning algorithm that requires a separate "training set." Therefore, the concept of a training set for an AI model is not applicable here. The software updates mentioned are optimizations and version changes, not a new AI model being trained.

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

As explained in point 8, a training set is not applicable to this device submission.

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The Ziehm Solo FD is intended for use in providing medical imaging for adults and pediatric populations, using pulsed and continuous fluoroscopic imaging.

The device provides contactless fluoroscopic image capture, temporarily storing, and display of digital subtraction, and acquisition of cine loops during diagnostic, interventional and surgical procedures. Examples of clinical application may include pediatric, cholangiography, endoscopic, urologic, lithotripsy, orthopedic, neurologic, vascular, cardiac, angiographic, critical care, and emergency room fluoroscopy procedures.

The visualization of such anatomical structures assists the clinician in the clinical outcome. This device does not support direct radiographic film exposures and is not intended for use in performing mammography. The system is not intended for use near MRI systems.

The Ziehm Solo FD uses X-ray imaging technology to visualize the human anatomy. The X-ray tube in the generator produces X-rays that penetrate the patient and then hit a special detector that converts them into digital images. This is done under the control of the user and at the direction of a physician who determines the specific clinical procedure. This visualization assists the physician in localizing pathological areas or during surgical procedures. The device enables real-time image acquisition as well as visualization of in vivo surgical procedures and post-operative results.
The Ziehm Solo FD consist of one mobile unit, the Mobile Stand. Optionally the device can be ordered with a Viewing Station (Monitor Cart). The Mobile Stand incorporates a small compact design making the positioning of the C-arm in relation to the patient easier for the operator. The generator with X-ray tube, advanced heat management system, X-ray control and collimators are assembled in one housing in a mono-block generator. The system control is handled via CAN BUS control system.
The mechanical C-Profile supports the generator, the flat panel detector and an integrated laser positioning device.
The optional available Viewing Station (Monitor Cart) provides a remote touch Solo Center that duplicates the touch Solo Center mounted on the Mobile Stand.
The proposed modified device Ziehm Solo FD employs the same fundamental control, and substantially equivalent scientific technology as that of our predicate device Ziehm Solo FD (K161976). Software architecture design is substantially equivalent to that of the predicate Ziehm Solo FD
The primary modification of the C-Arm includes an 8 inch IGZO (Indium gallium zinc oxide) flat panel detector (FPD The new 8 inch IGZO FPD is an addition to already introduced CMOS FPD. The flat panel detectors have the same outer product design of the housing, both devices use safety shielding for radiation suppression and use solid state x-ray image receptors (SSXI / FPD) 8 inch CMOS and the only difference to the predicate Ziehm Solo FD is the additional 8 inch IGZO panel.

The provided text describes the 510(k) premarket notification for the Ziehm Solo FD, a mobile fluoroscopic C-Arm. The submission asserts substantial equivalence to a predicate device (Ziehm Solo FD K161976) and references a comparative reference device (Ziehm Vision FD K193230) for specific comparisons related to flat panel detector technology (IGZO vs. a-Si).

However, the document does not contain the detailed information necessary to fully answer all aspects of your request regarding specific acceptance criteria for AI/algorithm performance and a comparative effectiveness study (MRMC) demonstrating human reader improvement with AI assistance. This is likely because the device described is an X-ray system, not an AI-powered diagnostic algorithm. The testing described focuses on the device's fundamental imaging capabilities, electrical safety, electromagnetic compatibility, and compliance with X-ray performance standards, rather than the diagnostic performance of an AI algorithm.

The closest relevant information relates to image quality assessment and dose reduction.

Here's a breakdown of the available and missing information based on your request:

Acceptance Criteria and Device Performance (Based on available information)

The document does not explicitly state "acceptance criteria" in the format of specific quantitative metrics for a diagnostic AI algorithm. Instead, it details that the device's safety and performance are confirmed by:

1. Compliance with applicable CDRH 21 CFR subchapter J performance requirements.
2. Adherence to recognized and general consensus standards.
3. Designing and manufacturing under Ziehm Imaging GmbH Quality System (21 CFR 820).
4. System verification and validation testing to ensure the device performs to product specifications and its intended use.
5. Bench testing, including non-clinical imaging and dose testing, demonstrating the device's ability to provide reduced dose while maintaining image quality.

The performance is reported qualitatively through statements of compliance and equivalence.

Study Details (Based on available information)

1. Sample Size used for the test set and the data provenance:

   • Sample Size: Not explicitly stated as a numerical count of images or patients. The document mentions "sets of images" and "anatomical phantoms" representing "both the adult and pediatric populations."
   • Data Provenance: The study used "non-clinical imaging and dose testing methods," implying the use of phantoms rather than real patient data. There is no mention of country of origin or whether it was retrospective/prospective clinical data, as it was a phantom-based bench study.

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

   • Number of experts: Singular. "A Radiologist performed an assessment of individual image sets."
   • Qualifications of experts: "A Radiologist." No further details on years of experience or specialization are provided.

3. Adjudication method for the test set:

   • No explicit adjudication method (like 2+1, 3+1) is mentioned. The assessment was performed by "A Radiologist."

4. 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. The document describes a technical performance validation of an X-ray imaging system, not a study assessing an AI algorithm's impact on human diagnostic performance. The statement "Radiologist conclusion, the image quality of the Ziehm Solo FD results in a comparable patient care to the reference device Ziehm Vision FD (K193230)" indicates an expert's qualitative judgment on image quality itself, not a measure of improved human reader performance with AI assistance.

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

   • This question is not applicable in the context of the provided document, as the device is an X-ray system, and there's no mention of a standalone AI algorithm for diagnostic inference. The "software" updates mentioned are for system control and image processing, not for AI-driven diagnostic tasks.

6. The type of ground truth used:

   • Expert Consensus/Assessment based on Phantoms: For image quality assessment, the "ground truth" was established through the "Radiologist's conclusion" on "anatomical phantoms" and "image comparison sets." This is a form of expert assessment of image quality, not clinical ground truth derived from pathology or patient outcomes.

7. The sample size for the training set:

   • Not applicable/Not provided. This document does not describe the development or training of an AI algorithm in the sense of machine learning, so there is no "training set." The software mentioned is traditional device control and image processing software.

8. How the ground truth for the training set was established:

   • Not applicable/Not provided. As there is no AI algorithm training set discussed, this information is not relevant to the content of the provided document.

Summary of what's provided vs. what's missing for an AI-focused request:

The document details the substantial equivalence of an X-ray imaging system based on hardware modifications and compliance with electrical, mechanical, and X-ray performance standards. It provides limited information regarding formal clinical study design, especially concerning human reader performance or the training/validation of an AI-driven diagnostic algorithm. The "software" sections refer to system operating software, not typically advanced AI for image interpretation that would require MRMC or standalone AI performance metrics.

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