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CareView 1800R X-ray Flat Panel Detectors is indicated for digital imaging solution designed for providing general radiographic system in all general purpose diagnostic procedures. CareView 1800R X-ray Flat Panel Detectors is a component of a digital imaging system. Properly integrated into a completed X-ray system, the digital X-ray imaging intended for medical application.

This product is not intended for mammography applications and dental X-ray applications.

CareView 1800R X-ray Flat Panel Detectors supports the single frame mode, with the key component of TFT/PD image sensor flat panel of active area: 43cm×43cm. The sensor plate of CareView 1800R X-ray Flat Panel Detectors is direct-deposited with Csl scintillator to achieve the conversion from X-ray to visible photon. The visible photons are transformed to electron signals by diode capacitor array within TFT panel, which are composed and processed by connecting to scanning and readout electronics, consequently to form a panel image by transmitting to PC through the user interface.

The major function of the CareView 1800R X-ray Flat Panel Detectors detector is to convert the X-ray to digital image, with the application of high resolution X-ray imaging. This detector is the key component of DR system, enables to complete the digitalization of the medical X-ray imaging with the DR system software.

Here's a breakdown of the acceptance criteria and study information for the CareView 1800R X-ray Flat Panel Detector, based on the provided text:

Acceptance Criteria and Device Performance

The acceptance criteria are not explicitly stated, but rather performance characteristics are compared to a predicate device (Canon CXDI-40EG). The reported device performance for CareView 1800R is presented alongside the predicate device's performance in the table below. The underlying assumption for acceptance is that the CareView 1800R performs comparably or better than the predicate.

Study Information

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

• Sample Size: 30 clinical images.
• Data Provenance: Not specified (e.g., country of origin). The study is described as a "concurrence study," which implies a retrospective comparison of images.

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

• This information is not provided in the document. The document states "There was no significant difference between the images of the CareView 1800R and those of the predicate device," but does not detail how this "no significant difference" was determined or by whom.

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

• This information is not provided in the document.

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:

• A multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly performed or described in this document. The clinical study was a "concurrence study of 30 clinical images... to compare the performance... to that of the predicate device." This suggests a direct comparison of image sets rather than assessing human reader performance with or without AI assistance.
• The device is an X-ray flat panel detector, not an AI-powered diagnostic system, hence "human readers improve with AI vs without AI assistance" is not relevant for this type of device.

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

• While the device itself is a standalone component (the detector), the "concurrence study" involved clinical images, which are implicitly interpreted by humans. However, the performance metrics (MTF, DQE) are standalone algorithm/device performance. Therefore, yes, standalone performance was assessed through non-clinical laboratory testing and then clinically supported by the concurrence study.

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

• For the non-clinical laboratory tests (MTF, DQE, etc.), the "ground truth" would be objective physical measurements based on established standards.
• For the clinical concurrence study, the "ground truth" implicitly relies on the assessment that there was no significant difference between images from the proposed device and the predicate device. The method or criteria for this assessment (e.g., comparison against established radiographic quality, diagnostic findings) is not explicitly detailed.

8. The sample size for the training set:

• The CareView 1800R is an X-ray flat panel detector, which is hardware, not a machine learning algorithm. Therefore, there is no training set in the context of AI/ML.

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

• As there is no training set (see point 8), this question is not applicable.

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