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The GC70 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 GC70 digital X-ray imaging system is a stationary x-ray system designed for general radiography and used to capture images by transmitting X-ray to a patient's body. The Xray 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 on the S-station, which is the Operation Software (OS) of Samsung Digital Diagnostic X-ray System, and save in DICOM file, a standard for medical imaging. The captured images are tuned up by an Image Post-processing Engine (IPE) which is exclusively installed in S-station, and sent to the Picture Archiving & Communication System (PACS) sever for reading images.

The GC70 digital X-ray imaging system consists of HVG (High Voltage Generator), ceiling suspension, X-ray tube, collimator, detector, wall stand, patient table, ACE (Auto Exposure Control), DAP (Dose Area Product), CIB (Control Interface Box), remote controller, grid, foot switch, barcode scanner, auto-stitching stand, weight distribution cap and workstation for S-station including Image Post-processing Engine (IPE).

The GC70 digital X-ray imaging system was previously cleared under K180543, and some hardware options and three software features are added to the predicate device GC70. The changes are as follows:

• Two High Voltage Generators
• . Two detectors
• . Slim wall stand
• . Software features called as S-Enhance, PEM (Pediatric Exposure Management) and Remote View
  • The S-Enhance is optional software to improve clarity of a foreign body (e.g. tube, line) and stone in chest, abdomen and L-spine images. With a single onscreen click, the companion image is created without additional settings or xray exposure, streamlining the process.
  • Pediatric Exposure Management is subdivided patient size and exposure conditions especially for pediatric patients based on weight and protocols. It follows same methodologies to define preset of patient size compare to preset of standard patient size from predicate device but specially optimized for pediatric patients.
  • The Remote view function provided images on another PC, not just on the device.

The Samsung GC70 Digital X-ray Imaging System, under K182647, is intended for generating radiographic images of human anatomy. This submission is a special 510(k) for changes to a previously cleared GC70 device (K180543), adding hardware options and three software features (S-Enhance, PEM, and Remote View) identical or similar to those cleared in predicate device GC85A (K181629).

1. Table of Acceptance Criteria and Reported Device Performance

No specific acceptance criteria or quantitative device performance metrics (e.g., sensitivity, specificity, accuracy) are provided in the document for the new features. The submission relies on the assertion that the new hardware and software features are identical or similar to previously cleared predicates and have undergone verification and validation testing to meet requirement specifications, functioning as safely and effectively.

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

The document does not specify a separate "test set" in the context of image data for evaluating the clinical performance of the software features. The verification and validation of the software features were conducted "in accordance with internal design change procedure." The non-clinical testing data, including MTF and DQE measurements, were provided in conformance to FDA guidance for solid-state X-ray imaging devices. However, no specific sample size of images or patients is reported, and there is no information on the country of origin or whether data was retrospective or prospective.

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

This information is not provided. The submission focuses on technical equivalence and verification/validation against internal specifications, rather than clinical performance studies requiring expert-adjudicated ground truth.

4. Adjudication method for the test set

This information is not provided. Given the nature of the submission (special 510(k) for hardware/software additions to an already cleared device, asserting equivalence), a formal adjudication process for a clinical test set is not described.

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 comparative effectiveness study is mentioned or provided. The document explicitly states: "The application of these hardware options and software features, cleared with K181629, to the proposed device GC70 do not require clinical data." This indicates that no studies involving human readers or AI assistance were deemed necessary for this submission.

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

No standalone algorithm performance study is described in the provided text. The software features (S-Enhance, PEM, Remote View) are described as tools or enhancements to the imaging system, rather than standalone diagnostic algorithms requiring independent performance metrics like sensitivity or specificity. Their verification and validation were conducted against "internal design change procedure" and "requirement specifications."

7. The type of ground truth used

The document does not detail the type of ground truth used as it does not report clinical efficacy studies. The "ground truth" for the non-clinical tests (MTF, DQE) would be based on physical measurements and established phantom standards (e.g., IEC 62220-1). For the software features, the "ground truth" was likely defined by internal "requirement specifications" within Samsung's design change procedures, focusing on functional correctness and qualitative improvements as described (e.g., "improve clarity," "subdivided patient size").

8. The sample size for the training set

This information is not provided. Since the software features are described as "optional software to improve clarity" or "subdivided patient size and exposure conditions" and are stated to be "identical or similar" to features in a predicate device, it's possible they involve image processing or rule-based systems rather than deep learning models that require large training sets. Even if machine learning was used, the training data details are not disclosed.

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

This information is not provided. Similar to the training set sample size, the methodology for establishing ground truth for any potential training data is not detailed.

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