(30 days)
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.
| Acceptance Criterion (Implied) | Reported Device Performance (GM60A with S3025-W) |
|---|---|
| Technological Characteristics | |
| Pixel Pitch (um) | 140 um (Same as predicate detectors) |
| High Contrast Limiting Resolution (LP/mm) | 3.57 LP/mm (Same as predicate detectors) |
| Detector Type | CsI Indirect (Same as predicate detectors) |
| Communication | Wired / Wireless (Same as predicate detectors) |
| Non-clinical Performance | |
| MTF (Modulation Transfer Function) | "do not differ from the predicate device" (Shows curves and measurements are comparable) |
| DQE (Detective Quantum Efficiency) | "do not differ from the predicate device" (Shows curves and measurements are comparable) |
| Other electrical, mechanical, environmental safety, EMC, and wireless function requirements | "All test results were satisfied as the standard" (ES 60601-1, IEC 60601-1-2, IEC 60601-1-3, IEC 60601-2-28, IEC 60601-2-54, ISO14971, 21CFR1020.30, 21CFR1020.31, IEC 60601-1-2:2007, and FDA guidance for Wireless Technology in Medical Devices). |
| Clinical Performance (Image Quality) | |
| Clinical image quality evaluation by a qualified radiologist for equivalence to predicate device | "Clinical images were provided... and found to be equivalent to the predicate device." The submission states "these images were not necessary to establish substantial equivalence based on the modifications to the device but they provide further evidence in addition to the laboratory performance data to show that the complete system works as intended." This implies the primary acceptance for clinical performance was a qualitative assessment of equivalence by an expert. |
Study Details
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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.
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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."
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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.
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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.
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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.
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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.
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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.
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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.
§ 892.1720 Mobile x-ray system.
(a)
Identification. A mobile x-ray system is a transportable device system intended to be used to generate and control x-ray for diagnostic procedures. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.