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The GM85 Digital Mobile 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 GM85 Digital Mobile X-ray Imaging 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 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 send to the Picture Archiving & Communication System (PACS) sever for reading images.

The GM85 Digital Mobile X-ray imaging System was previously cleared with K222353, and through this premarket notification, we would like to add more configurations in the previously cleared GM85 as a detector, accessories are newly added and software is updated for user convenience.

The new detector added in the proposed device is designed to achieve a higher IP rating of Dust and Water and reduce weight while maintaining durability, functionality and operation like the detector of the predicate device. The new detector and predicate device's detector are both an x-ray conversion device using an amorphous silicon flat panel and absorb incident x-rays, converts it to a digital signal, and then transmits it to the Samsung Digital X-ray System like that of the predicate device.

The provided text is a 510(k) Premarket Notification for the GM85 Digital Mobile X-ray Imaging System. It describes changes made to a previously cleared device (K222353) and argues for substantial equivalence.

Based on the provided text, the device is the GM85 Digital Mobile X-ray Imaging System. It is an X-ray imaging system, and the study focuses on the performance of a new detector (F4343-AW) and other accessories and software updates compared to the predicate device, also named GM85.

Here's an analysis of the acceptance criteria and study information:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state "acceptance criteria" in a quantitative format for specific imaging metrics. Instead, it focuses on demonstrating that the proposed device, with its new detector, accessories, and software, is substantially equivalent to the predicate device (GM85, K222353). The performance is assessed by comparing technical specifications and qualitative evaluation by experts.

The key performance characteristics and comparisons are as follows:

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

• Test Set Sample Size: The document refers to "Anthropomorphic phantom images" but does not specify the number of phantom images used for evaluation. It also notes that clinical data was not required.
• Data Provenance: The study is non-clinical. The "Anthropomorphic phantom images" would have been generated in a controlled testing environment, likely at the manufacturer's facility. The country of origin of the data is implicitly South Korea, where SAMSUNG ELECTRONICS Co., Ltd. is located. The study is a prospective evaluation of the new detector and modified system against the predicate.

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

• Number of Experts: The document states that phantom images "were evaluated by professional radiologists." It does not specify the number of radiologists who participated in this evaluation.
• Qualifications of Experts: The experts are described as "professional radiologists." No further details on their experience level (e.g., years of experience, subspecialty) are provided.

4. Adjudication Method for the Test Set

The document states that phantom images "were evaluated by professional radiologists and found to be equivalent to the predicate devices" and that there was "no significant difference in the average score of image quality evaluation." This suggests a comparative scoring or assessment. However, the specific adjudication method (e.g., consensus, majority vote, independent reads with statistical comparison) is not described.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly done, nor was there a "human readers improve with AI vs without AI assistance" component. The study for substantial equivalence focused on comparing the image quality of the proposed device (with new detector) against the predicate device using phantom images, evaluated by radiologists. The purpose was to show equivalence of the device's output, not the improvement of human readers with AI assistance.

6. Standalone Performance Study (Algorithm Only)

The provided text describes the device as a "Digital Mobile X-ray Imaging System," which includes hardware (X-ray generator, detector) and software for image processing (IPE, S-Station). The evaluation primarily focuses on the entire system's ability to generate radiographic images with equivalent quality to the predicate, particularly with the new detector.

While software features are mentioned (S-Share, S-Enhance, SimGrid, PEM, QAP, Bone Suppression, Remote View, Mirror View, RFID, Value-up Package), and software was updated for user convenience, the study does not report a standalone algorithm-only performance (without human-in-the-loop performance) in terms of diagnostic accuracy or reader improvement for specific diagnostic tasks. The "phantom image evaluation" evaluates the quality of the images produced by the overall system, not an AI algorithm's diagnostic output.

7. Type of Ground Truth Used

The ground truth for the phantom image evaluation was established based on expert consensus/evaluation of image quality metrics. The "professional radiologists" evaluated the anthropomorphic phantom images. This is not pathology, nor outcomes data.

8. Sample Size for the Training Set

The document does not provide information on the sample size for the training set. Training data would typically be applicable if this were an AI/CADe device with a specific machine learning model for diagnostic tasks, which is not the primary focus of this 510(k) for a basic X-ray imaging system with incremental changes. The "Image Post-processing Engine (IPE)" and features like "Bone Suppression" would have been developed using some form of training data previously, but details are absent here for this particular submission.

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

As no training set sample size is provided, the method for establishing ground truth for a training set is not mentioned in this document.

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