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The ImagingRing m is a mobile x-ray system to be used for 2D planar and fluoroscopic and 3D imaging for adult and pediatric patients. It is intended to be used where 2D and 3D information of anatomic structures such as bony anatomy and soft tissue and objects with high X-ray attenuation such as (metallic) implants is required. The ImagingRing m provides an interface that can be used by system integrators for integration of the ImagingRing m with image guidance systems such as surgical navigation systems.

The ImagingRing m (Version 2.0) is from a technical point of view the same system as its already approved predecessor ImagingRing m (K203281). The only difference is the implementation of a new x-ray source in combination with a software upgrade, which allows for higher power settings. The ImagingRing m functions as a mobile x-ray system to be used for 2D planar and fluoroscopic and 3D imaging for adult and pediatric patients. It is intended to be used where 2D and 3D information of anatomic structures such as bony anatomy and soft tissue and objects with high X-ray attenuation such as (metallic) implants is required. The ImagingRing m (Version 2.0) provides an interface that can be used by system integration of the ImagingRing m (Version 2.0) with image guidance systems such as surgical navigation systems.

The ImagingRing m (Version 2.0) consists of the ring gantry and respective arms carrying the X-Ray source and directly integrates all necessary electronic and components along with low-level software to realize coordinated motion and X-ray emission in the device's ring carrier and legs. The ImagingRing m (Version 2.0) device also provides a detachable Remote Control Panel (RCP) component that provides a display and controls elements such that users can interact with the machine.

This document is a 510(k) Summary for the medPhoton GmbH ImagingRing m (Version 2.0), Loop-X (Version 2.0), and Loop-X Mobile Imaging Robot (Version 2.0). It focuses on demonstrating substantial equivalence to a predicate device, K203281 (ImagingRing m). The provided text describes the changes in the new version (primarily a new X-ray source allowing higher power settings and associated software upgrades) and the testing conducted to support its safety and effectiveness.

Here's an analysis of the acceptance criteria and the study as per your request, based only on the provided text:

Important Note: The provided text is a 510(k) Summary, which is a high-level overview. It does not contain detailed acceptance criteria tables, specific statistical results from performance studies, or granular details about ground truth establishment and expert qualifications often found in a full study report or 510(k) submission. Therefore, some information requested might not be explicitly present and can only be inferred or stated as 'not provided'.

Acceptance Criteria and Study Details

The document states that the testing aimed to evaluate whether the new features (increased kVp, infrared motion compensation, extended Field of View) negatively impact diagnostic accuracy and usability, and to confirm that they provide benefits without altering total radiation dose or degrading image clarity.

Given the nature of a 510(k) submission and the information provided, the "acceptance criteria" are implied to be that the new device's performance, particularly in terms of image quality and diagnostic utility, is non-inferior or superior to the predicate device, especially considering the new features. Specific quantitative acceptance criteria (e.g., minimum SNR, maximum artifacts) are not explicitly listed in the summary.

Here's what can be extracted and inferred:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Sizes: Not explicitly stated with specific numbers of cases or subjects. The text mentions "cadaver studies" and "clinical studies involved patient imaging."
• Data Provenance:
  • Country of Origin: "Cadaver studies (e.g. at Highridge Cadaver Lab in the US, Paracelsus Medical University (PMU) in Salzburg, Austria)." This indicates data from both the US and Austria. "Evaluations at reference customer sites" suggests other clinical sites, but specific locations are not given.
  • Retrospective or Prospective: Not explicitly stated. "Clinical studies involved patient imaging under standard operating conditions to validate the new features' impact in routine diagnostic settings" suggests a prospective approach for the clinical portion, while cadaver studies are inherently prospective for the device testing.

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

• Number of Experts: Not explicitly stated.
• Qualifications of Experts: Not explicitly stated. The text mentions "renowned institutions" and "reference customer sites" implying medical professionals (e.g., radiologists, surgeons) were involved, but their specific roles, experience, or certifications are not provided.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated. The general phrasing "evaluations" and "comparison" does not specify a formal adjudication process.

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

• Was it done? Not explicitly stated as a formal MRMC study. While "comparison of image quality, artifacts, anatomical representation, and dose" was performed, the summary doesn't detail if multiple human readers quantitatively evaluated cases with and without AI assistance (the AI here refers to the software-driven features like motion compensation rather than a traditional AI algorithm for diagnosis). The study design seems to focus on the technical performance and the clinical utility of the device features rather than a direct comparison of human reader performance with and without AI assistance for interpretation.
• Effect Size: Not provided.

6. Standalone Performance (Algorithm only without human-in-the-loop performance)

• The term "standalone" typically applies to AI algorithms making automated diagnoses. Here, the "AI" or advanced features (motion compensation, extended FOV) are integrated into an imaging device. The "performance" described is largely the technical performance of the imaging system with these features. For example, motion compensation improves image quality, which then benefits human interpretation. The text focuses on the device's performance characteristics with these new features, not an AI algorithm's diagnostic output.

7. Type of Ground Truth Used

• For Image Quality/Feature Assessment:
  • Cadaver Studies: Used to assess impact of higher kVp on image quality, and effectiveness of motion compensation in reducing artifacts. The "ground truth" here is the physical reality within the cadavers and the absence/presence of motion artifacts under controlled conditions. Comparison was made against images acquired at different settings or without compensation.
  • Clinical Studies: Used to "validate the new features' impact in routine diagnostic settings" and assess "workflow efficiency." The ground truth for image quality would be subjective clinical assessment by clinicians and objective measurement of image characteristics. For workflow, it would be observed efficiency.
• No explicit "diagnostic ground truth" (e.g., pathology confirmed disease state) is mentioned, as the study focuses on the image acquisition system's performance, not a specific diagnostic task like lesion detection.

8. Sample Size for the Training Set

• Sample Size: Not provided. As this is a 510(k) for an imaging device (updated hardware and software features), not a deep learning AI diagnostic algorithm, the concept of a "training set" in the context of machine learning model development might not directly apply in the same way. The software upgrades are likely to control the hardware parameters and image reconstruction based on engineering principles and pre-defined algorithms, not necessarily a data-driven "training" in the AI/ML sense. If any internal models were trained (e.g., for motion detection), details are not provided.

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

• Since a "training set" in the context of an AI/ML model for diagnosis is not mentioned as part of the submission, how its "ground truth" was established is also not applicable or not provided. The development and verification of the system's features would rely on engineering specifications, phantom studies, and possibly empirical adjustments based on cadaver/clinical imaging, rather than annotated training data for a diagnostic algorithm.

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