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The GC80 Series Digital X-ray imaging System is intended for use in generating radiographic images of human anatomy by a qualified/trained doctor or technician. This device is not intended for mammographic applications.

The GC80 digital X-ray imaging system consists of High voltage generator (HVG), Ceiling Suspension, X-ray tube, Collimator, Detector, AEC, DAP, CIB(Control Interface Box), Wall Stand, Patient Table, Collimator, Detector, Remote controller, Grid, Foot switch, Barcode scanner and Auto-stitching stand. This system is used to capture images by transmitting X-ray to a patient's body. The X-ray passing through a patient's body is sent to the detector and then converted into electrical signals. These signals go through the process of amplification and digital data conversion in the signal process device being sent to the S-Station (Operation Software) and saved in DICOM file, a standard for medical imaging. The captured images are sent to the Picture Archiving & Communication System (PACS) server, and can be used for reading images.

The provided document is a 510(k) Premarket Notification for the SAMSUNG ELECTRONICS Co., Ltd. Digital Diagnostic X-ray System (GC80). It primarily focuses on demonstrating substantial equivalence to a predicate device (XGEO GC80) and does not detail specific acceptance criteria and a study dedicated to proving the device meets those criteria in the typical sense of a novel AI/CAD device.

Instead, the submission for the GC80 X-ray system focuses on confirming that its performance, despite the addition of a new detector, remains consistent with established medical device standards and is substantially equivalent to a previously cleared device. Therefore, the "acceptance criteria" here are effectively the performance standards met by the predicate device and relevant international/national standards, and the "study" is a comparative assessment.

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

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

The document does not explicitly present a table of acceptance criteria for specific diagnostic performance metrics (e.g., sensitivity, specificity for disease detection) that would be common for AI/CAD devices.
Instead, it focuses on the equivalence of technical specifications and image quality. The implicit acceptance criteria are that the device performs as well as the predicate device and meets relevant international standards.

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

• Sample Size for Clinical Data: The document states, "Clinical images were provided," but it does not specify the sample size (number of images or patients) used for this clinical evaluation.
• Data Provenance: Not explicitly stated in terms of country of origin or whether it was retrospective/prospective. Given "Clinical images were provided," it implies a retrospective collection of existing images for evaluation.

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)

• The document states, "In clinical data, the radiologists evaluate the image of GC80 is substantially equivalent, and superior in some images to the predicate device."
• Number of experts: Not specified (plural "radiologists" suggests more than one).
• Qualifications of experts: Not specified beyond "radiologists."

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

• The document does not specify an adjudication method. It only mentions that "radiologists evaluate the image."

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, an MRMC comparative effectiveness study was not done. This submission is for an X-ray system, not an AI/CAD device. The "clinical data" involved radiologists evaluating the images themselves, not evaluating AI output or comparing their performance with and without AI. Therefore, there's no discussion of human reader improvement with AI assistance.

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

• Not applicable. This device is an X-ray imaging system, not an AI algorithm. Its performance is inherent in its ability to generate radiographic images, not in its interpretation.

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

• For the clinical evaluation, the "ground truth" for the image quality assessment was the expert evaluation of radiologists. The goal was to confirm that the images generated by the GC80 were of comparable, or superior, quality for diagnostic purposes as those from the predicate device.

8. The sample size for the training set

• Not applicable / Not specified. As this is an X-ray imaging system and not an AI/CAD device, there is no "training set" in the context of machine learning. The system's performance is based on its hardware and software engineering meeting physical and technical standards.

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

• Not applicable. See point 8.

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