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Digital Flat Panel X-ray Detector, HAD1717MC is intended for use in general projection radiographic applications wherever conventional screen-film systems or CR systems may be used. This device is not intended for mammographic applications.

This is a wired digital flat panel x-ray detector, a fast and efficient digital radiography system. The detector is used in combination with a TFT glass and scintillator (CSI) and supports automatic trigger signal detection technology that can be used without generator integration. The incident X-rays are converted into visible light that produces electron hole pairs in a photometer biased by scintillator material. The charge carier is stored in the photodiode capacity. By pulse processing the gates of the TFT lines in the matrix, charges in all the columns are transferred in parallel with the signal output. All signals on the column are amplified by a custom Read Out IC for further processing. The amplified signal is digitized using an A / D converter integrated into the ROIC. The digitized signal is transmitted to the PC. The device software is new software. When transferred to a PC, the software can acquire and store digitized images and view the images.

The provided text is a 510(k) summary for a medical device (Digital Flat Panel X-ray Detector, HAD1717MC). It outlines the device description, technological characteristics, and non-clinical testing. However, it does not contain acceptance criteria for device performance nor a detailed study proving the device meets specific acceptance criteria in the format requested.

The "Performance data" section vaguely states that non-clinical testing data included MTF and DQE measurements "as tested by IEC 6220-1" and that "All test results were satisfying with the IEC6220-1 standards." This implies the standard itself sets the acceptance criteria, but these criteria are not explicitly listed, nor are the reported performance values. The "Clinical Data" section states that clinical images were obtained and confirmed "equivalent diagnostic capability to the predicate device and its results demonstrate similar equivalence or slightly better," but no specific metrics, acceptance criteria, or detailed study methodology are provided.

Therefore, I cannot fully complete the requested table and answer all questions based solely on the provided text. I will extract what information is present and indicate where information is missing.

1. A table of acceptance criteria and the reported device performance

Missing Information: The specific acceptance criteria within IEC 6220-1 are not explicitly stated in the document. The document only mentions that the results satisfied the standard.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

Missing Information: The document states "clinical images were obtained," but does not specify the sample size for the test set of clinical images, the country of origin, or if the data was retrospective or prospective.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Missing Information: This information is not provided in the document.

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

Missing Information: 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

No. The document does not describe an MRMC comparative effectiveness study where human readers used AI assistance. The study described focused on the diagnostic capability of the device's images compared to a predicate device.

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

Yes, partially. The "Performance data" section refers to non-clinical testing for metrics like MTF and DQE, which are standalone measurements of the device's image quality performance. These are algorithm-only or device-only measurements without human interpretation in the loop. The "Clinical Data" section, however, implies human interpretation for diagnostic capability comparison.

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

Missing Information: The document vaguely mentions "clinical images were obtained... to confirm that the subject x-ray detector provides images of equivalent diagnostic capability to the predicate device." However, it does not specify how the "ground truth" for diagnostic capability in these clinical images was established (e.g., confirmed diagnosis by a panel of experts, pathology reports, patient outcomes).

8. The sample size for the training set

Not Applicable (or Missing Information): This device is a digital flat panel x-ray detector, not an AI or machine learning algorithm in the context of image interpretation that would typically have a "training set" of images in the way an AI diagnostic tool would. It produces images. If "training set" refers to data used for optimizing the device's image processing parameters, that information is not provided.

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

Not Applicable (or Missing Information): As above, the concept of a "training set" and associated "ground truth" is not directly applicable to this device in the context of the provided document.

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