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ADD (Digital Flat Panel X-Ray Detector) is indicated for digital imaging solution designed for providing general radiographic diagnosis of human anatomy targeting both adult and children. It is intended to replace film based radiographic diagnostic systems. Not to be used for mammography.

The ADD are wired or wireless digital flat panel detectors that have been designed for faster, more streamlined approach to digital radiography systems. The ADD detector utilize a combination of propriety TFT glass and scintillators(CsI), and those and electronics are housed in one package. The detectors support an auto-trigger signal sensing technology that allows the detectors to be used without generator integration.

The flat panel sensors of the ADD are fabricated using thin film technology based on amorphous silicon technology. Electronically, the sensors are much like conventional photodiode arrays. Each pixel in the array consists of a light-sensing photodiode and a switching Thin Film Transistor (TFT) in the same electronic circuit. Amorphous silicon photodiodes are sensitive to visible light, with a response curve roughly comparable to human vision. The sensitivity of amorphous silicon photodiodes peaks in green wavelengths, well matched to scintillators such as CsI. The response has the excellent linearity of a charge-integrating-biased photodiode.

SDK-MCW is the software of Detector that performs image acquisition, image correction, and preprocessing.

The provided text is a 510(k) summary for the ADD Digital Flat Panel X-Ray Detector. It establishes substantial equivalence to a predicate device (K203188) and does not contain detailed information about a study proving the device meets specific acceptance criteria in the manner expected for a clinical performance study with predefined metrics like sensitivity, specificity, or AUC, typically found in AI/CAD device approvals.

Instead, the submission focuses on non-clinical performance tests, software validation, and a comparison of technological characteristics to demonstrate substantial equivalence to the predicate device. The "clinical test summary" section explicitly states that clinical images were provided, but “these images were not necessary to establish substantial equivalence based on the differences from the predicate… but they provide further evidence… that the subject digital detector works as intended.” This implies that the primary basis for equivalence is non-clinical.

Therefore, many of the requested items related to clinical study design (sample size, expert qualifications, adjudication, MRMC studies, standalone performance with specific metrics like sensitivity/specificity) are not explicitly present or are not applicable in the context of this 510(k) summary. The acceptance criteria are primarily derived from compliance with standards and demonstrating similar performance to the predicate device.

Here's an attempt to answer your questions based on the provided text, acknowledging where information is not available:

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

The acceptance criteria are primarily based on demonstrating performance similar to or meeting the specified values of the predicate device and compliance with relevant industry standards for electrical safety, EMC, and imaging performance (DQE, MTF, resolution).

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

The document mentions that "Clinical images were provided" but explicitly states they "were not necessary to establish substantial equivalence." This suggests that if there was a "test set" of clinical images, it was for supplementary evidence rather than a primary determinant of substantial equivalence, and its specifics (size, provenance, retrospective/prospective nature) are not detailed in this summary. The primary basis for comparison was non-clinical technical data.

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)

Not applicable or not provided. Since the clinical images were "not necessary to establish substantial equivalence," specific details about ground truth establishment by experts for a dedicated clinical performance test set are not given.

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

Not applicable or not provided. (See point 3).

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 MRMC comparative effectiveness study is mentioned. The device is a digital X-ray detector, not an AI or CAD system designed to assist human readers. The clinical images provided were to demonstrate the detector "works as intended," not to show improvement in human reader performance.

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

The ADD is a digital flat panel X-ray detector, which captures images. It is not an algorithm for diagnosis, so the concept of "standalone performance" in the context of an AI algorithm is not directly applicable. Its performance is measured by imaging characteristics like DQE, MTF, and resolution.

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

For the non-clinical performance tests (DQE, MTF, resolution), the "ground truth" refers to established physical standards and measurement protocols, not clinical ground truth derived from expert consensus, pathology, or outcomes data. For any "clinical images" that might have been reviewed, the ground truth source is not specified because its review was stated as "not necessary."

8. The sample size for the training set

Not applicable. The ADD is a hardware device (detector) with associated software for image acquisition, correction, and preprocessing. While the software was developed and validated, this is not an AI/ML algorithm that requires a "training set" of diagnostic images in the conventional sense. The "training set" concept is typically relevant for machine learning models, which this device does not appear to be.

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

Not applicable. (See point 8).

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