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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 K180543, and through this premarket notification, we would like to add more configurations in the previously cleared GM85 as a fixed column type and two detectors are newly added, and three software features are newly added as stated below.

S-Enhance is renamed from Tube & Line Enhancement (TLE), which was cleared before with GM85 at K171119, to enhance visibility of tubes and lines and provide enhanced images separately from original images. In this submission, the scope of S-Enhance is expanded from tubes and lines on chest images to foreign body (e.g. tubes, lines and needles) and urinary stones on chest, abdomen, and L-spine. And Manual Stitching to capture a body part that is larger than the detector's by capturing multiple images and Remote View function to remote access to view the current image on the workstation through a web browser. It was determined that the level of concern for the software contained in the GM85 Digital Mobile X-ray imaging System was Moderate in accordance with the FDA guidance, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Device".

The provided text does not contain detailed acceptance criteria or a comprehensive study report specifically outlining acceptance criteria with reported device performance metrics in a tabular format. However, it does discuss performance characteristics, non-clinical data, and clinical data used to establish substantial equivalence.

Here's an attempt to extract and infer the information based on the provided document.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical acceptance criteria. Instead, it focuses on demonstrating substantial equivalence to a predicate device. The performance is generally reported as "equivalent to the predicate devices" or "satisfying the standards."

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

• Test Set Sample Size: The document does not specify a numerical sample size for the clinical image evaluation. It only mentions "Anthropomorphic phantom images were provided."
• Data Provenance: The document does not specify the country of origin of the data. The study involved "Anthropomorphic phantom images." It is a prospective evaluation using phantoms, rather than retrospective patient data.

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

• Number of Experts: "a professional radiologist" (singular)
• Qualifications of Experts: The document specifies "a professional radiologist." It does not provide additional details such as years of experience.

4. Adjudication Method for the Test Set

The document states that the phantom images "were evaluated by a professional radiologist." It does not mention any adjudication method (e.g., 2+1, 3+1) involving multiple readers or a consensus process.

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, a multi-reader multi-case (MRMC) comparative effectiveness study focusing on human readers improving with AI vs. without AI assistance was not explicitly described or reported. The study focused on demonstrating the equivalenc of the device's image quality and the S-Enhance feature's ability to provide clear visibility.

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

The S-Enhance feature and new detectors were evaluated, suggesting an assessment of the algorithm's output (the enhanced image) and the detector's image characteristics. While not a purely "standalone algorithm" in the sense of a diagnostic AI making decisions, the evaluation of the S-Enhance feature generating "companion images which provide clear visibility" effectively assesses the algorithm's performance without direct human interaction during the image generation process. The radiologist then evaluates these generated images.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

For the clinical data, the ground truth was based on the inherent properties of the anthropomorphic phantom images, which simulate human anatomy and disease conditions. The radiologist's evaluation of the clarity and visibility in these phantom images served as the assessment against this simulated ground truth.

8. The Sample Size for the Training Set

The document does not provide any information regarding the sample size used for the training set for features like S-Enhance.

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

The document does not provide details on how the ground truth for training data was established for the S-Enhance feature or any other software components. It mentions that S-Enhance is the same image processing technology as a previously cleared feature (TLE) and extends its scope. This implies that previous development and potentially associated training (if applicable to the algorithm) would have been used.

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