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510(k) Data Aggregation
(25 days)
1717SGC_127um and 1717SGC_140um
1717SGC 127um and 1717SGC 140um are indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic systems in all general purpose diagnostic procedures. Not to be used for mammography.
1717SGC 127um X-ray detector is identical to 1717SGC (K122182). Both 1717SGC 127um and 1717SGC 140um are digital solid state X-ray detectors based on flat-panel technology. These radiographic image detectors and processing unit consist of a scintillator coupled to an a-Si TFT sensor. Both devices are connected to the user PC via wired LAN (ethernet cable) and need to be integrated with a radiographic imaging system. Both devices do not operate as an X-ray generator controller but can be utilized to convert X-ray to light and light to electrical signals for image data digitization.
The RAW files can be further processed as DICOM compatible image files by separate console SW (K160579 / Xmaru View V1 and Xmaru PACS/ Rayence Co.,Ltd.) for a radiographic diagnosis and analysis.
This document describes a 510(k) premarket notification for the Rayence 1717SGC_127um and 1717SGC_140um digital flat panel X-ray detectors. The submission aims to demonstrate substantial equivalence to predicate devices, namely 1717SGC (K122182) and 1717SGN (K150150).
Here's an analysis of the provided information regarding acceptance criteria and the supporting study:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly present a formal table of defined acceptance criteria with numerical thresholds. Instead, it relies on comparative performance against predicate devices and qualitative assessments.
| Performance Metric | Acceptance Criteria (Implicit) | Reported Device Performance (1717SGC 140µm vs. Predicate 1717SGN) |
|---|---|---|
| Intended Use | Same as predicate device (general radiography for human anatomy, not mammography). | Met: "same indications for use" |
| Material / Form Factor / Safety | Similar to predicate devices. | Met: "same... material, form factor, performance, and safety characteristics" |
| Non-clinical Performance (MTF, DQE, NPS) | Performance at least equivalent to, or better than, the predicate device (1717SGN), based on IEC 62220-1. | Met: "performed better compared with each respective predicate device." "1717SGC 140um has higher MTF and DQE performance at high spatial frequencies, especially from 2 lp/mm." "The comparison of the MTF and DQE for 1717SGC 140um detector demonstrated that the performed almost same with 1717SGN." |
| Clinical Image Quality | Images from the subject device should be diagnostically equivalent, or superior, to those from the predicate device. | Met: "images obtained with the 1717SGC 140µm are superior to the same view obtained from a similar patient with the predicate devices, 1717SGN and 1717SCN." "soft tissues on extremity films were seen with better clarity. There is little difficulty in evaluating a wide range of anatomic structures necessary to provide a correct conclusion." |
| Electrical, Mechanical, Environmental Safety | Conformity to IEC 60601-1:2005 (3rd Edition) + CORR. 1:2006 + CORR. 2:2007 + A1:2012 and EMC testing to IEC 60601-1-2: 2007. | Met: "All test results were satisfactory." |
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 images used in the expert review. It mentions "sample radiographs of similar age group and anatomical structures."
- Data Provenance: The provenance of the clinical images (e.g., country of origin, retrospective or prospective) is not explicitly stated. The non-clinical test report refers to IEC 62220-1, which suggests standardized phantom-based testing, but doesn't specify data provenance for the clinical images.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
- Number of Experts: One expert was used for the clinical review: "reviewed by a licensed US radiologist to render an expert opinion."
- Qualifications of Experts: The expert was a "licensed US radiologist." No specific experience (e.g., years) is provided.
4. Adjudication Method for the Test Set
- Adjudication Method: None mentioned. As only one radiologist reviewed the images, there was no need for an adjudication process.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done
- No, a MRMC study was not explicitly described. The clinical review was performed by a single licensed US radiologist comparing images from the subject device and the predicate.
- Effect Size of Human Readers Improve with AI vs. Without AI Assistance: Not applicable, as this device is a digital X-ray detector, not an AI-powered image analysis tool for human readers.
6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) was Done
- Yes, standalone performance was assessed for non-clinical metrics. MTF, DQE, and NPS tests were conducted "by using the identical test equipment and same analysis method described by IEC 62220-1." These are objective, quantitative measurements of the detector's image quality performance, independent of human interpretation for diagnostic purposes.
7. The Type of Ground Truth Used
- Non-clinical Testing: The ground truth for MTF, DQE, and NPS is established by the standardized measurement methods defined in IEC 62220-1, using phantoms or controlled experimental setups.
- Clinical Testing: The ground truth for clinical image quality assessment was based on expert opinion/consensus (though only one expert was involved). The radiologist's assessment of "better clarity" and "little difficulty in evaluating a wide range of anatomic structures" served as the basis for concluding diagnostic equivalence/superiority.
8. The Sample Size for the Training Set
- Not Applicable. The document describes a medical device (X-ray detector) and its performance validation, not a machine learning or AI algorithm that requires a training set. The device itself is hardware that generates images.
9. How the Ground Truth for the Training Set Was Established
- Not Applicable. As there is no training set for an AI algorithm, there is no ground truth establishment for such a set.
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