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Intended for use by a qualified/trained doctor or technologist on both adult and pediatric patients for taking diagnostic radiographic exposures of the skull, spinal column, extremities, and other body parts on both adult and pediatic patients. Applications can be performed with patient sitting, standing or lying in the prone or supine positions. Not intended for mammography.

The Aquarius 8600 1717TG and Aquarius 8600 1417TG are digital flat panels, specifically termed solid state digital X-Ray detector. This technology couples a scintillator with an a-Si TFT sensor, and through integration with a radiographic imaging system, x-ray images can be captured and digitalized. The resulting RAW files are DICOM 3.0 compatible allowing image files to be processed by IDC Magellan software.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

Study Information:

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

   • Sample Size: Not explicitly stated as a number of images or cases in the provided text for the DQE, MTF, NPS, or visual assessment tests. For the visual assessment, it mentions "laboratory images using phantoms were acquired." These are likely a set of standardized phantom images.
   • Data Provenance: The data for DQE, MTF, and NPS are from "measured" curves, implying direct testing of the device hardware. The visual assessment used "laboratory images using phantoms." This suggests the data is prospectively generated from controlled laboratory settings, not from patient data. The country of origin for the testing is not specified, but the applicant company is located in Canada.

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

   • Number of Experts: One (a single Radiological Technologist).
   • Qualifications: "A Radiological Technologist certified in the United States of America and Canada." Specific experience level (e.g., years) is not provided.

3. Adjudication method for the test set:

   • No adjudication method is described for the visual assessment. A single expert made the determination without mention of a consensus or tie-breaking process. For the DQE, MTF, and NPS, these are quantitative measurements that do not require expert adjudication.

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 comparative effectiveness study was done. The submission states: "No clinical testing was performed for this special 510(k) submission." The visual assessment mentioned was a comparison of phantom images by a single technologist, not a clinical study involving multiple readers assessing patient cases. Also, this device is an X-ray detector, not an AI-assisted diagnostic tool, so improvement with AI assistance is not applicable in this context.

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

   • This is not applicable as the device is a digital X-ray detector, not a standalone algorithm. Its performance is measured directly through physical parameters (DQE, MTF, NPS) and its ability to produce diagnostic images.

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

   • For the DQE, MTF, and NPS measurements, the "ground truth" is generally considered to be the intrinsic physical properties of the detector, measured against established standards and methodologies.
   • For the visual assessment, the "ground truth" was the subjective opinion of a single certified Radiological Technologist comparing images from the proposed device to the predicate device using phantoms.

7. The sample size for the training set:

   • Not applicable/Not provided. This device is a hardware component (digital flat panel detector) and associated software for image acquisition and processing. It does not employ machine learning or AI models that require specific "training sets." The IDC Magellan software underwent "full system level verification, validation and regression testing" as part of its development, but this is software testing, not ML model training.

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

   • Not applicable, as there is no specific "training set" in the context of machine learning model development for this device. The software validation relies on established software engineering principles and testing against specifications inherent to its function.

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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.

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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