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Model#'s 3543A, 4343A, 2929A and A843B; Solid State X-ray Imager (Flat Panel/Digital Imager) are indicated for use in generating radiographic images of human anatomy. They are intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures, excluding dental, fluoroscopic, and mammographic applications. As prescribed by a licensed physician

Our device is used in medical x-ray imaging systems. The product can only be used by trained personnel of medical facilities. The product is only used for single image diagnosis applications. The flat panel detector consists of GOS scintillator screen and thin-film transistors. The scintillator screen converts the x-rays into visible light. Thin-film transistors convert the visible light to an electrical charge. The flat panel detector can then obtain a digital image by analog to digital conversion and associated circuits.

The provided text describes a 510(k) premarket notification for X-ray imagers (Models 3543A, 4343A, 2929A, and A843B). It focuses on demonstrating substantial equivalence to predicate devices rather than proving the device meets specific acceptance criteria through a clinical study. Therefore, much of the requested information regarding acceptance criteria and performance evaluation against those criteria is not explicitly detailed in the way a clinical study report would present it.

Here's an attempt to extract and infer the information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly defined as pass/fail metrics in a table. Instead, the document compares the technical characteristics of the applicant's device to those of predicate devices to demonstrate "substantial equivalence." The implicit acceptance criterion is that the applicant's device's performance should be comparable to or better than the predicate devices across several key technical specifications.

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

• Test set sample size: Not explicitly stated as a number of images or patients. The document mentions "comparison images" were used.
• Data provenance: Not specified (e.g., country of origin, retrospective or prospective).

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

• This information is not provided. The "comparison images" suggest qualitative evaluation rather than a formal ground truth establishment process by experts.

4. Adjudication method for the test set:

• Not applicable/Not provided. There is no mention of an adjudication process.

5. Multi-reader multi-case (MRMC) comparative effectiveness study:

• No, a formal MRMC comparative effectiveness study was not done. The document states: "We have included comparison images using our devices Models 3543A, 4343A, 2929A and A843B and a Atlaim Atal 9 cleared device (K132151) as direct comparison of the image quality of our devices in order to support substantial equivalence." This suggests a visual comparison of image quality rather than a study with human readers evaluating images with and without AI assistance.
• Effect size: Not applicable, as no MRMC study was conducted.

6. Standalone (algorithm only without human-in-the-loop performance) study:

• Yes, in a way. The "Non-Clinical data" section details various technical performance metrics (DQE, MTF, NPS, spatial resolution, etc.) for the device itself, demonstrating its standalone physical and technical capabilities. These are objective measurements of the device's image acquisition properties. The "comparison images" also likely fall under this, as they compare the raw output of the device to a predicate.

7. Type of ground truth used:

• For the non-clinical technical performance data (DQE, MTF, etc.), the ground truth is based on physical phantom measurements and industry standards for evaluating X-ray detector performance.
• For the "comparison images," the ground truth implicitly is the visual quality and diagnostic utility as interpreted by the applicant, compared to images from a predicate device. This is a qualitative assessment rather than a formally established ground truth like pathology or expert consensus.

8. Sample size for the training set:

• Not applicable. This device is an X-ray imager (hardware and associated basic software for image acquisition and viewing), not an AI algorithm that requires a training set in the typical sense for image interpretation. The software aspects mentioned (DICOM, image processing, basic editing) are standard functionalities and not indicative of a deep learning model that needs a training set of labeled images.

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

• Not applicable, as there is no mention of a training set or AI model that requires ground truth labeling.

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