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The Jumong General is intended for use by a qualified/trained doctor or technician on both adult and pediatric subjects for taking diagnostic radiographic exposures of the skull, spinal column, extremities, and other body parts. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position. Not for mammography.

This device represents a combination of an already cleared solid state digital x-ray acquisition panel with software and diagnostic x-ray components required to make a complete system. Film cassettes may be employed in place of the x-ray generator has been changed to a Delta Electronics Delta DMP 100R The collimator has been changed to a Fairy Medical Electronics model CRUX FR04. The tubehead has been changed to a Hangzhou Kailong Medical Instruments Co., Ltd H1074X. The system complies with the CDRH Radiological Health performance standard in the Code of Federal Regulations, as well as the voluntary IEC standards IEC 60601-1 and IEC 60601-1-2.

The provided text is a 510(k) Pre-market Notification document for the "Jumong General" stationary x-ray system. This document is a submission to the FDA demonstrating that a new device is substantially equivalent to a legally marketed predicate device.

It's important to understand that in this context, "acceptance criteria" and "study that proves the device meets the acceptance criteria" refer to the demonstration of substantial equivalence, primarily through non-clinical performance testing and comparison to an established predicate device, rather than a clinical study with human-in-the-loop performance measurement of an AI algorithm.

The document states:

• "5. Safety and Effectiveness, comparison to predicate device. This combination device has the same indications for use and very similar technological characteristics as the predicate device, and employs already 510(k) cleared digital panels and software."
• "No software modifications were made to the imaging chain."
• "8. Summary of clinical testing: Not applicable."

This clearly indicates that this submission does not involve an AI algorithm that requires a separate performance study with human readers or standalone algorithm performance assessment. The device is a conventional x-ray imaging system, albeit with some updated components compared to its predicate.

Therefore, many of the requested points related to AI performance, such as MRMC studies, standalone AI performance, ground truth establishment for AI training/testing, and expert adjudication, are not applicable to this specific submission.

However, I can still interpret how the concept of "acceptance criteria" and "proof" apply to this device based on the provided text, focusing on its non-clinical performance and substantial equivalence demonstration.

Acceptance Criteria and Study for Jumong General X-Ray System

Given that the Jumong General is a stationary x-ray system (Product Code: KPR, MQB) and not an AI-powered diagnostic device, the "acceptance criteria" and "proof" focus on demonstrating substantial equivalence to a previously cleared predicate device (K150816, Jumong Series Stationary Radiographic System). This is achieved primarily through non-clinical performance testing, safety standards compliance, and direct comparison of functional characteristics. The document explicitly states "No software modifications were made to the imaging chain," and "Clinical testing: Not applicable."

The "acceptance criteria" for demonstrating substantial equivalence primarily revolve around meeting recognized performance standards, electrical safety, EMC compliance, and demonstrating comparable image quality and intended use to the predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

The new components are:

• Generator: Delta Electronics DMP 100R (same kVp range as CPI CMP 200 DR predicate)
• Collimator: Fairy Medical Electronics CRUX FR04 (changed from Ralco Model R225)
• Tubehead: Hangzhou Kailong Medical Instruments Co., Ltd H1074X (changed from Varian RAD14)

Digital Panel Models, Image acquisition panel specifications, DICOM, Image acquisition software, and Power Source are all SAME as predicate. (page 5, comparison chart).

Conclusion: "This combination device has the same indications for use and very similar technological characteristics as the predicate device, and employs already 510(k) cleared digital panels and software." (page 4, section 5) |
| Safety & Performance Standards | Compliance with relevant US Radiation Safety Performance Standards and IEC standards for medical electrical equipment. | - Conforms to the US Performance Standard. (page 6, section 7)

• New generator complies with: IEC 60601-2-54:2009+A1+A2, IEC 60601-1:2005+Corrigendum 1+Corrigendum 2+A1, IEC 60601-1-3:2008+A1, and IEC 60601-1-6:2010+A1. (page 6, section 7)
• EMC testing complies with IEC 60601-1-2:2014. (page 6, section 7)
• New collimator tested for compliance with EN/IEC 60601-2-54 for radiation leakage. (page 6, section 7)
• New tubehead tested to IEC 60613:2010 and IEC 60336:2005. (page 6, section 7)
• "Every unit is tested for electrical safety, input power, display of operation and exposure factors, collimator operation, reproducibility, and accuracy." (page 6, section 7) |
  | Image Quality | Image quality should be diagnostically acceptable and comparable to the predicate device. | "Test images were acquired which showed excellent diagnostic quality." (page 6, section 7) |
  | Risk Analysis | Assessment of risks associated with modifications and demonstration of acceptable risk profile. | "A risk analysis was performed with regard to the modifications. No software modifications were made to the imaging chain." (page 6-7, section 7) The conclusion states the device is "as safe and effective as the predicate device." (page 7, section 9) |

As this is a conventional X-ray system submission and not an AI/CADe device, the following points are largely "Not Applicable."

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

• Not Applicable in the traditional sense of a clinical test set for an AI algorithm. The "test set" here refers to the physical system undergoing non-clinical verification and validation tests. The document references "test images" acquired for image quality assessment, but the sample size or specific provenance of these images is not detailed as it would be for a clinical AI study. The testing is described as non-clinical ("integration and image quality testing").

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

• Not Applicable. Ground truth, in the context of diagnostic accuracy for AI, is not relevant here. The "diagnostic quality" of images was assessed, presumably by qualified personnel, but this is a standard engineering and quality assurance assessment for image generators, not an AI ground truth establishment process.

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

• Not Applicable. This pertains to clinical AI studies for diagnostic accuracy.

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 is specific to AI-assisted diagnostic devices. The document explicitly states "Summary of clinical testing: Not applicable."

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

• Not Applicable. This is specific to the performance of an AI algorithm. The device is a hardware x-ray system, and no AI algorithm's standalone performance is being evaluated.

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

• Not Applicable. Ground truth for diagnostic accuracy is not relevant here as it's a hardware device demonstrating fundamental imaging capability and safety, not a diagnostic interpretation tool.

8. The sample size for the training set

• Not Applicable. There is no AI algorithm being trained for this device as per the submission details ("No software modifications were made to the imaging chain").

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

• Not Applicable. As there is no AI algorithm being trained by the applicant, this point is not relevant.

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The Garion is intended to be used and operated by: adequately trained, qualified and authorized health care professionals who have full understanding of the safety information and emergency procedures as well as the capabilities and functions of the device. The device is used for radiological guidance and visualization during diagnostic, interventional and surgical procedures on all patients, except neonates (birth to one month), within the limits of the device is to be used in health care facilities both inside and outside the operating room, sterile as well as non-sterile environment in a variety of procedures.

GARION is mobile fluoroscopy system is designed to provdie fluoroscopic and spot-film images of thepatient during diagnostic, surgical procedures. This device is a digital X-ray radiographic equipment that consists of high voltage generator, X-ray control unit, X-ray tube unit, Collimator, image processing unit. The image processing unit consists of medical image detector, power supply, medical image collecting unit and relevant software.

The provided text describes a 510(k) premarket notification for a medical device called "Garion", an image-intensified fluoroscopic x-ray system. The document focuses on demonstrating substantial equivalence to a legally marketed predicate device, "Zenition 70".

Here's an analysis of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document focuses on demonstrating substantial equivalence to a predicate device rather than defining specific performance acceptance criteria for the Garion device against a set of quantitative metrics. The "performance" is implicitly assessed through a comparison of technical characteristics to the predicate.

The acceptance criterion, in this context, is that the Garion device's technological characteristics are substantially equivalent to the predicate device, implying similar safety and effectiveness. The reported performance is the direct comparison of these characteristics.

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

The document explicitly states: "9. Summary of clinical testing: Not required for a finding of substantial equivalene." This indicates that no clinical test set was used to evaluate the device's performance against specific acceptance criteria. The evaluation relies on comparing the technical specifications of the new device to the predicate device and adherence to relevant IEC standards.

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

Since no clinical test set was used, there were no experts establishing ground truth for a test set.

4. Adjudication Method for the Test Set:

Not applicable, as no clinical test set was used.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:

No, an MRMC comparative effectiveness study was not done. The document states that clinical testing was not required for substantial equivalence.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

The "Garion" is an X-ray system, not an AI algorithm. Therefore, the concept of "standalone (algorithm only)" performance is not applicable in the same way as it would be for a pure software device. The evaluation is of the hardware and integrated software for image acquisition and display.

7. The Type of Ground Truth Used:

The "ground truth" for the substantial equivalence claim is effectively the established safety and effectiveness of the legally marketed predicate device (Zenition 70), supported by the device's compliance with applicable performance standards (IEC and FDA Radiation Safety Performance Standard). The comparison of technical specifications aims to show that the Garion device is sufficiently similar to the predicate to assume similar safety and effectiveness.

8. The Sample Size for the Training Set:

The concept of a "training set" is typically associated with machine learning or AI algorithms. Since this submission is for an X-ray system and does not mention any AI components requiring supervised learning, there is no training set in the conventional sense.

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

Not applicable, as there is no mention of a training set or AI algorithms requiring ground truth establishment.

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