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The Discovery XR656 HD is intended to generate digital radiographic images of the skull, spinal column, chest, abdomen, extremities, and other body parts in patients of all ages. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position and the system is intended for use in all routine radiography exams. Optional image pasting function enables the operator to stitch sequentially acquired radiographs into a single image.
The device is not intended for mammographic applications.

The Discovery XR656 HD Radiography X-ray System is designed as a modular system with components that include an Overhead Tube Suspension with tube/collimator, wallstand, Table, X-ray generator, and wireless digital detectors. The System generates diagnostic radiographic images which can be sent through a DICOM network for applications including printing, viewing, and storage. The components may be combined in different configurations to meet specific customer needs. In addition, upgrade configurations are available for predicate devices. The optional image pasting function enables the operator to stitch sequentially acquired radiographs into a single image.

The provided text describes a 510(k) premarket notification for the GE HUALUN MEDICAL SYSTEMS CO. Ltd. Discovery XR656 HD (K172869), a digital radiographic X-ray system. The document focuses on demonstrating substantial equivalence to a predicate device (Optima XR646, K143270) rather than presenting a performance study with detailed acceptance criteria and clinical efficacy results for the entire system.

The core of the submission revolves around the change in detector technology from Ultra Wideband (UWB) to WiFi (802.11) for image transfer, utilizing cleared detectors (PerkinElmer XRpad2 3025 HWC-M Flat Panel Detector, K161942, and PerkinElmer XRpad2 4336 HWC-M Flat Panel Detector, K161966).

Here's an breakdown of the information requested, based on the provided text:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly state quantitative acceptance criteria or specific performance metrics (e.g., sensitivity, specificity, accuracy) for the Discovery XR656 HD as a standalone diagnostic tool. Instead, it relies on demonstrating compliance with recognized standards and successful design verification and validation testing to ensure that the modifications (primarily the change to WiFi-enabled detectors) do not negatively impact the device's safety and effectiveness compared to the predicate.

The "Summary of Non-Clinical Tests" section mentions compliance with several voluntary standards. While these standards implicitly contain performance requirements, the specific numerical acceptance criteria for those requirements and the actual measured performance of the Discovery XR656 HD are not detailed in this summary.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document states, "The subject of this premarket submission, Discovery XR656 HD, did not require clinical studies to support substantial equivalence for the incorporation WiFi (802.11) enabled detectors due to these detectors having their own 510(k) clearance."

Therefore, for the Discovery XR656 HD itself, there was no specific clinical "test set" and corresponding sample size for demonstrating diagnostic performance beyond its cleared components. The evaluation focused on non-clinical design verification and validation. The "testing/documentation" mentioned was "according to... FDA guidance documents" (for software and cybersecurity), and these were "bench" tests.

The cleared detectors (PerkinElmer XRpad2 3025 HWC-M and 4336 HWC-M) would have had their own clinical data for their respective 510(k) clearances (K161942 and K161966), but that data is not provided here for the Discovery XR656 HD system.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Since no clinical studies were performed for the Discovery XR656 HD's diagnostic performance, there was no test set requiring expert-established ground truth in the context of diagnostic accuracy. The ground truth for the training of the system's image processing (if applicable, which falls under "image processing algorithms to accommodate multiple image matrix sizes") is not detailed here.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable as no clinical test set for diagnostic performance was conducted for this submission.

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

Not applicable. This device is a digital radiographic X-ray system, not an AI-assisted diagnostic tool in the sense of providing automated readings or decision support. The "image processing algorithms" mentioned are for accommodating different image matrix sizes and are not described as AI for diagnostic assistance. There is no mention of an MRMC study or AI assistance.

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

Not applicable. This is a medical imaging system, not a standalone algorithm. The "image processing algorithms" are integrated into the system, and their performance is evaluated as part of the overall system's technical validity, not as a standalone diagnostic algorithm.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)

As no clinical study for diagnostic performance was required or conducted for this 510(k) submission, the concept of "ground truth" (in the diagnostic sense) for this specific submission is not present. The "ground truth" for the system's functionality was established through design verification and validation testing against engineering specifications and industry standards.

8. The sample size for the training set

The document does not specify a training set sample size. While "image processing algorithms" are mentioned, implying potential machine learning components, no details on their training are provided. The focus of the submission is on hardware and communication changes, and the safety and effectiveness of the system with these changes.

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

Not applicable. No information is provided about a training set or its ground truth establishment.

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The OEC Elite™ MiniView™ (mobile mini C-Arm) is designed to provide physicians with real time general fluoroscopic visualization of patients of all ages. It is intended to aid physicians and surgeons during diagnostic or therapeutic treatment/surgical procedures of the limbs/extremities and shoulders including, but not limited to, orthopedics and emergency medicine.

The OEC Elite™ MiniView™ is a mobile fluoroscopic mini C-arm system that provides fluoroscopic images of patients of all ages during diagnostic, treatment, and surgical procedures of the shoulders, limbs, and extremities. The system consists of a C-arm attached to an image processing workstation. A CsI(TI) -CMOS flat panel detector and the identical X-ray source monoblock are used for image acquisition.

The C-arm supports the high-voltage generator, X-ray tube, X-ray controls, collimator, and the FPD. The C-arm is capable of performing linear (vertical, horizontal, orbital) and rotational motions that allow the user to position the X-Ray imaging components at various angles and distances with respect to the patient extremity anatomy to be imaged. The C and support arm are mechanically balanced allowing for ease of movement and capable of being "locked" in place using an electronically controlled braking system. The workstation is a stable mobile platform that supports the C-arm, image display monitor(s), image processing equipment/software, recording devices, data input/output devices and power control systems.

The primary technology change for the subject device compared to the unmodified predicate OEC Elite MiniView, was to add an optional wireless footswitch which provides identical functionalities as the wired footswitch to control X-ray on and off.

This document describes the 510(k) summary for the GE OEC Elite™ MiniView™ device, which is a mobile fluoroscopic mini C-arm system. The specific change being submitted for review is the addition of an optional wireless footswitch.

Here's an analysis of the provided text to answer your questions regarding acceptance criteria and the study proving the device meets them:

Crucially, this 510(k) submission states that no clinical studies were required to support substantial equivalence due to the nature of the modification (adding a wireless footswitch). Therefore, many of your questions related to clinical study design (sample size, expert ground truth, MRMC studies, standalone performance) will not be applicable or directly answerable from this document.

The primary method of demonstrating device performance and safety for this submission was through non-clinical testing, including design verification and validation, risk management activities, and compliance with recognized standards.

1. A table of acceptance criteria and the reported device performance

Since this is a submission for a modification (wireless footswitch) and relies on non-clinical testing for substantial equivalence, the "acceptance criteria" are primarily related to product safety, effectiveness, and functional equivalence to the wired footswitch and predicate device.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size for Test Set: Not applicable in the context of clinical data. The "test set" here refers to the device itself and its components undergoing engineering and functional verification/validation testing. This is not a patient-based test set.
• Data Provenance: The testing was conducted internally by GE Hualun Medical Systems Co. Ltd. in Beijing, China, as part of their design control processes. This is prospective engineering and quality assurance testing, not data collected from patients.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not applicable. Ground truth in a clinical sense (e.g., disease presence) was not established. The "ground truth" for this engineering submission is functional correctness and safety established by engineering specifications, international standards, and internal quality processes.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Not applicable. This was not a human reader adjudication study. The "adjudication" was based on successful completion of predetermined engineering tests and compliance with regulatory standards, as overseen by GE's quality management system.

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 MRMC study was done. The document explicitly states: "The subject of this premarket submission, OEC Elite MiniView with the addition of the wireless footswitch, did not require clinical studies to support substantial equivalence." This device is not an AI-assisted diagnostic tool.

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

• Not applicable. This device is a medical imaging system, and the submission is for a hardware control component (wireless footswitch), not a standalone algorithm or AI.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• The "ground truth" used for this submission is based on engineering specifications, functional requirements, and compliance with recognized industry and electrical safety standards. For example, the footswitch must reliably transmit an "X-ray ON/OFF" signal, and the device must maintain its safety characteristics as per IEC 60601 series standards. This is not a clinical ground truth.

8. The sample size for the training set

• Not applicable. This is not a machine learning/AI device, so there is no "training set."

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

• Not applicable. As there is no training set for an AI model, there is no ground truth established for one.

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