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The GM60A 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 GM60A Digital Mobile X-ray imaging system consists of High voltage generator (HVG), Xray tube, Collimator, Detector, DAP and Barcode scanner. 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 before being sent to the 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 describes a 510(k) premarket notification for the SAMSUNG GM60A Digital Mobile X-ray Imaging System (GM60A-32S & GM60A-40S). The filing is to add a new detector (S3025-W) to the existing device (predicate device K142492). The focus of the study is to demonstrate substantial equivalence to the predicate device, not necessarily to set new performance criteria in an AI context.

Therefore, the acceptance criteria and study detailed below relate to demonstrating the new detector's performance is equivalent to the previously cleared predicate device's detectors. It's important to note this is not an AI device in the sense of an algorithm for diagnosis, but rather an imaging system.

Acceptance Criteria and Reported Device Performance

The core of the acceptance criteria is demonstrating that the new S3025-W detector, when incorporated into the GM60A system, performs comparably to the previously cleared detectors (S4343-W and S4335-W) of the predicate device.

Study Details

1. Sample size used for the test set and the data provenance:

   • Sample Size: The document does not specify a numerical sample size for the clinical image test set. It states "Clinical images were provided."
   • Data Provenance: Not explicitly stated, but it would typically be prospective data collected specifically for the testing of the new detector with the GM60A system prior to submission. There is no mention of country of origin, but the manufacturer is based in the Republic of Korea.

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

   • Number of Experts: "a radiologist" (singular).
   • Qualifications: "with equivalent U.S. board certification."

3. Adjudication method for the test set:

   • Method: Not applicable in the context of this submission. The evaluation was a qualitative assessment of equivalence by a single radiologist. There was no consensus or multi-reader adjudication process described.

4. 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:

   • MRMC Study: No, an MRMC comparative effectiveness study was not done.
   • AI Improvement Effect Size: Not applicable. This device is a mobile X-ray imaging system, not an AI diagnostic algorithm. The comparison is between different detectors within an X-ray system, evaluating their inherent imaging performance, not reader performance with or without AI.

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

   • Standalone Performance: Not applicable. This document pertains to an X-ray imaging system, not a standalone AI algorithm. The performance evaluation assesses the imaging system's output and characteristics.

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

   • Type of Ground Truth: The ground truth for the clinical images was the qualitative assessment of image equivalence by a radiologist with U.S. board certification, comparing images acquired with the new detector to those from the predicate device's detectors. For non-clinical performance (MTF, DQE), the "ground truth" or reference was established by standard measurements conforming to IEC 62220-1 and comparison to the predicate device's established performance.

7. The sample size for the training set:

   • Sample Size: This device is a hardware imaging system, not an AI algorithm that requires a training set in the typical sense. There is no mention of a training set as would be relevant for machine learning.

8. How the ground truth for the training set was established:

   • Ground Truth for Training Set: Not applicable, as there is no training set for an AI algorithm described in this submission.

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The GM60A 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 GM60A Digital Mobile X-ray imaging system consists of High voltage generator (HVG), X-ray tube, Collimator, Detector, DAP and Barcode scanner. 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 before being sent to the 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 text describes the 510(k) summary for the GM60A Digital Mobile X-ray Imaging System. However, it does not contain specific acceptance criteria or a detailed study proving the device meets those criteria, as typically found in comprehensive clinical study reports. It primarily focuses on demonstrating substantial equivalence to predicate devices for regulatory approval.

Here's an analysis of the information that is present and what is missing based on your request:

1. Table of Acceptance Criteria and Reported Device Performance:

• Missing: The document does not explicitly state quantitative acceptance criteria (e.g., specific thresholds for sensitivity, specificity, accuracy, or image quality metrics) that the device was required to meet for regulatory approval.
• Present (Implicit Performance Comparison): The "Summary of Technological characteristic of the proposed device compared with the predicate device" table ({5}-{6}) provides a comparison of technical specifications (e.g., Max. Power, kVp Range, mA Range, Exposure Time, Tube Moving Range, Rotation Range, Detector Area, Number of pixels, Pixel Pitch, High Contrast Limiting Resolution, Communication, Grid Lines/cm). For many of these, the proposed device's performance is stated as "Same as" or "Same or better" than the predicate device. This indirectly suggests that the performance observed for the predicate device forms an implicit benchmark for acceptance.
• Present (Non-clinical Data): "MTF and DQE were tested and measured by IEC 62220-1. The proposed device shows same curves and measurements of MTF and DQE than the predicate device (K140334) at all spatial frequencies tested." This indicates that the MTF (Modulation Transfer Function) and DQE (Detective Quantum Efficiency) performance of the proposed device were found to be equivalent to the predicate device, serving as a non-clinical performance benchmark.
• Present (Clinical Data): "clinical images were obtained in accordance with FDA guidance for the submission of 510(k)'s for Solid State X-ray Imaging Devices. They were evaluated by professional radiologists and found to be equivalent to the predicate device." This indicates a clinical performance outcome of "equivalence" to the predicate.

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

• Missing: The sample size for the clinical test set (i.e., number of images or patients) is not specified.
• Missing: The data provenance (e.g., country of origin, retrospective or prospective) is not specified.

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

• Present (Partial): "They were evaluated by professional radiologists".
• Missing: The exact number of radiologists is not specified.
• Missing: The qualifications of the radiologists (e.g., years of experience, subspecialty) are not specified.

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

• Missing: The adjudication method used for the radiologists' evaluation is not specified.

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:

• Missing: This document describes an X-ray imaging system, not an AI-assisted diagnostic tool. Therefore, an MRMC comparative effectiveness study comparing human readers with and without AI assistance is not applicable and not mentioned. The clinical data section only states that images were "evaluated by professional radiologists and found to be equivalent to the predicate device."

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

• Missing: This is an X-ray imaging system, not a standalone AI algorithm. Therefore, a standalone algorithm performance study is not applicable and not mentioned. The performance reported relates to the imaging system's ability to produce images comparable to predicate devices.

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

• Present (Implicit, for clinical evaluation): The "ground truth" for the clinical evaluation seems to be the interpretations by professional radiologists, who assessed the "equivalence" of the images from the proposed device to those of the predicate device. This suggests a form of expert consensus or comparison against an established benchmark (the predicate device's image quality).
• Missing (Definitive Ground Truth): The document does not describe a definitive "ground truth" derived from pathology, long-term outcomes, or a strict consensus process, which would be more typical for diagnostic accuracy studies of an AI algorithm. For this type of imaging device, the focus is on image quality and its diagnostic adequacy.

8. The sample size for the training set:

• Missing: This document pertains to the regulatory submission for an X-ray imaging system itself, not an AI algorithm that requires a training set. Therefore, a training set and its sample size are not applicable and not mentioned.

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

• Missing: As no training set for an AI algorithm is mentioned, this information is not applicable.

In summary:

The provided text focuses on demonstrating the substantial equivalence of the GM60A X-ray system to legally marketed predicate devices based on technical specifications and a general statement of clinical image equivalence as assessed by radiologists. It does not provide the detailed parameters of a study (like sample size, specific acceptance criteria, expert qualifications, or adjudication methods) that would be expected for a comprehensive performance study of an AI diagnostic device.

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