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1717SCV and 1717SGV X-ray detectors, 127um and 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.

1717SCV / 1717SGV is a digital solid state X-ray detector that is based on flat-panel technology. This radiographic image detector and processing unit consists of a scintillator coupled to an a-Si TFT sensor. This device needs to be integrated with a radiographic imaging system. It can be utilized to capture and digitalize X-ray images for radiographic diagnosis.

1717SCV and 1717SGV have the same Hardware, Software and components.

The type of scintillator layer are different: Cesium Iodide for 1717SCV and Gadolinium Oxsulfide for 1717SGV. Scintillator is a phosphor that produces scintillations.

The subject detectors are not wireless, but they are connected to a viewing station by ethernet connection. Also, the subject detectors have an Automatic Exposure Control (AEC) feature.

The RAW files can be further processed as DICOM compatible image files by separate console SW (K190866 / Xmaruview V1 (Xmaru Chiroview, Xmaru Podview)/ Rayence Co.,Ltd.) for a radiographic diagnosis and analysis.

The software used with the subject detectors is the same as the software XmaruView V1 used with the predicate K190866.

Here's an analysis of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not contain explicitly defined acceptance criteria (e.g., a specific threshold for MTF or DQE that the device must meet). Instead, it states that the devices (1717SCV / 1717SGV) have "similar MTF and DQE performance" when compared to their predicate devices (1717SCC / 1717SGC). The implicit acceptance criterion is that the subject device's performance should be comparable to, or not significantly worse than, the legally marketed predicate devices.

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

The document states that "The non-clinical test report for each subject device was prepared and submitted to FDA separately to demonstrate the substantial equivalency..." It then lists the types of tests performed (MTF, DQE, NPS). However, it does not specify the sample size for the test set or the data provenance (e.g., country of origin, retrospective/prospective). These are non-clinical performance evaluations, likely performed in a lab setting rather than on patient data.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The provided text does not mention any human expert review for establishing ground truth. The performance evaluation is based on technical metrics (MTF, DQE, NPS) derived from physical measurements of the devices, not from interpretation of clinical images by experts.

4. Adjudication Method for the Test Set

As the evaluation is based on technical metrics and not human interpretation of images, an adjudication method for a test set of images is not applicable and therefore not mentioned.

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

No, a MRMC comparative effectiveness study was not done. The study described focuses on the physical performance characteristics of the X-ray detectors themselves (MTF, DQE, NPS) rather than their impact on human reader performance or the diagnostic accuracy of images.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

The performance testing described (MTF, DQE, NPS) is a standalone (algorithm/device only) performance evaluation. These metrics assess the intrinsic image quality and efficiency of the detector itself, independent of human interpretation.

7. Type of Ground Truth Used

The ground truth used for these performance metrics is based on physical measurements and standardized testing protocols (specifically, IEC 6220-1) rather than expert consensus, pathology, or outcomes data. The "ground truth" for MTF, DQE, and NPS refers to the actual physical properties and performance of the detector under controlled conditions.

8. Sample Size for the Training Set

The document does not mention a training set sample size. The devices (1717SCV / 1717SGV) are X-ray detectors, not AI algorithms that would require a training set in the conventional sense. The performance evaluation focuses on the inherent characteristics of the hardware.

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

Since there is no mention of a training set for an AI algorithm (as this is a medical device clearance for an X-ray detector), the method for establishing ground truth for a training set is not applicable and therefore not described.

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1012WCC and 1012WGC Digital Flat Panel X-Ray Detector are indicated for digital imaging solution designed for human anatomy including head, neck, cervical spine, arm, leg and peripheral (foot, hand, wrist, fingers, etc.). They are intended to replace film based radio diagnostic systems and provide a case diagnosis and treatment planning for physicians and other health care professionals. Not to be used for mammography.

1012WCC / 1012WGC is a wired/wireless digital solid state X-ray detector that is based on flat-panel technology. The wireless LAN(IEEE 802.11a/g/n/ac) communication signals images captured to the system and improves the user operability through high-speed processing. This radiographic image detector and processing unit consists of a scintillator coupled to an a-Si TFT sensor. This device needs to be integrated with a radiographic imaging system. It can be utilized to capture and digitalize X-ray images for radiographic diagnosis. The RAW files can be further processed as a DICOM compatible image file by a separate console SW program (K160579 / Xmaru View V1 and Xmaru PACS/ Rayence Co.,Ltd.) for a diagnostic analysis.

The provided text describes the 510(k) premarket notification for the Rayence Co., Ltd.'s 1012WCC and 1012WGC Digital Flat Panel X-Ray Detectors. The document focuses on demonstrating substantial equivalence to a predicate device (1012WCA) and a reference device (1210SGA), rather than establishing novel acceptance criteria for a new device type.

Therefore, the acceptance criteria are implicitly defined by demonstrating equivalence to the performance of the predicate and reference devices. The "study" here refers to the performance testing conducted to prove this equivalence.

Here's an analysis of the provided information, framed to address your questions about acceptance criteria and the supporting study:

Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly stated as numerical thresholds to be met, but rather as demonstrating substantial equivalence to the predicate (1012WCA) and reference (1210SGA) devices in key performance metrics. These metrics are:

Study Information (Performance Testing)

The document describes "non-clinical test" and "clinical consideration test" to demonstrate substantial equivalence.

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

   • Clinical Consideration Test: The document states "sample radiographs of similar age groups and anatomical structures." It does not specify the exact number of images or patients (sample size) in the clinical test set.
   • Data Provenance: Not explicitly stated regarding the origin (e.g., country) of the clinical data. It is a "clinical consideration test," meaning it's likely a small comparative review rather than a large clinical trial. The images are "taken from both subject devices" (1012WCC/WGC) and compared to images from the predicate/reference devices (1012WCA/1210SGA). This suggests a prospective collection of comparison images, not necessarily a large retrospective dataset.

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

   • Only one expert was used: "a licensed US radiologist."
   • Qualifications: "a licensed US radiologist." No further details on years of experience or subspecialty.

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

   • There was no formal adjudication method described. The review was conducted by a single "licensed US radiologist" who rendered an "expert opinion." This falls into the "none" category for a multi-expert adjudication process.

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:

   • No MRMC study was done. This study solely focuses on the image quality of the device itself (hardware) compared to predicate devices, not on the interaction of human readers with AI assistance. The devices are flat-panel X-ray detectors, not AI algorithms for interpretation.

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

   • Yes, in essence, standalone technical performance was done. The non-clinical tests (MTF, DQE, NPS) are standalone evaluations of the device's image quality metrics, independent of human interpretation.
   • The "clinical consideration test" is a standalone evaluation of the image quality from the devices by an expert, focusing on diagnostic utility, rather than an "algorithm only" performance. The device itself is the "algorithm" in terms of image generation.

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

   • For the non-clinical tests (MTF, DQE, NPS): The ground truth is based on physical measurements and established international standards (IEC 62220-1) for image performance.
   • For the clinical consideration test: The ground truth for image quality assessment was the expert opinion/review of a single licensed US radiologist. There's no mention of pathology or outcomes data to establish clinical ground truth.

7. The sample size for the training set:

   • Not applicable / Not explicitly mentioned. The document describes a comparison between new devices and predicate devices, demonstrating substantial equivalence for hardware. It does not describe a machine learning model that would require a "training set." The closest analogy might be the development data used for the original predicate devices, but that's not detailed here.

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

   • Not applicable. As there's no mention of a machine learning model or training set, this question is not addressed.

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