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The BSD3543W (BT-DA22W/BT-DB22W) detector is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic system in all general-purpose diagnostic procedures. It is not to be used for mammography.

BSD3543W (BT-DA22W/BT-DB22W) is a digital X-ray flat panel detector which intercepts x-ray photons and the scintillator (BT-DB22W(Gdos) / BT-DA22W(CsI)) emits visible spectrum photons that illuminate an array of photo (a-SI)-detector that creates electrical signals. After the electrical signals are generated, it is converted to digital values, and the images will be displayed on the monitor. This device should be integrated with an operating PC and an X-Ray generator. It can digitalize x-ray images and transfer them for radiography diagnostics. Advanced digital image processing allows considerably efficient diagnosis, all kinds of information management, and sharing of image information on network. The BSD3543W is a wireless version of the predicate device, the BSD3543.

The provided document K200787 describes the BSD3543W digital flat panel X-ray detector. It is a 510(k) submission, comparing the device to a predicate device (BSD3543, K162487) and a reference device (ClearVision Exam Vue Flat Panel Detector, K160143) to demonstrate substantial equivalence.

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

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

The document does not explicitly present a formal "acceptance criteria" table with pass/fail thresholds for clinical performance. Instead, it relies on demonstrating substantial equivalence to a predicate device (BSD3543) and a reference device (ClearVision ExamVue Flat Panel Detector). The performance criteria are implicitly defined by matching or exceeding the predicate device's specifications for various technical parameters and by demonstrating "similar quality overall" in clinical image comparison.

Here's a table summarizing the key comparative data presented, which serves as the basis for demonstrating equivalence and thus "meeting acceptance criteria" in a 510(k) context:

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

• Sample Size: The document does not specify a numerical sample size for the clinical image comparison. It generally states, "Images taken from the predicate and subject devices were reviewed and rated in comparison..."
• Data Provenance: Not explicitly stated whether the images were retrospective or prospective, nor their country of origin. Given it's a 510(k) for a hardware device (X-ray detector), the emphasis is primarily on technical equivalence and image quality rather than efficacy in a clinical trial.

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

• Number of Experts: "an American board-certified radiologist" (singular, implied to be one).
• Qualifications of Experts: "American board-certified radiologist." No mention of years of experience or subspecialty.

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

Not applicable/None. Only one radiologist reviewed the images, so no adjudication method was needed or performed.

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, a multi-reader, multi-case (MRMC) comparative effectiveness study was not reported. This device is a digital X-ray detector, not an AI-based diagnostic tool. The clinical study was a qualitative comparison of image quality by a single radiologist.
• Therefore, there's no discussion of human reader improvement with AI assistance.

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

• This question is more applicable to AI algorithms. For this device (an X-ray detector), "standalone performance" refers to its technical imaging specifications (MTF, DQE, resolution, pixel pitch, etc.) measured in a lab setting, independent of a human interpreter.
• Yes, standalone performance was done. The document presents detailed technical specifications and performance results (MTF, DQE, resolution, pixel matrix, etc.) which are intrinsic to the detector itself, measured in a "non-clinical performance testing" environment (Page 8). These are direct measurements of the detector's physical and technical capabilities.

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

For the clinical image comparison, the "ground truth" was implicitly expert opinion/qualitative comparison by a single American board-certified radiologist, who judged the images from the subject device to have "similar quality overall" with no "radiographic abnormalities and any issue with diagnostic images" compared to the predicate. There is no mention of pathology or outcomes data as "ground truth."

8. The sample size for the training set

• Not applicable. This device is a digital X-ray detector (hardware), not a machine learning/AI model that requires a "training set" in the conventional sense.
• The "viewing software" mentioned is described as identical to the predicate and undergoes standard software verification and validation, not machine learning training.

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

• Not applicable, as there is no training set for an AI model.
• For the software, "ground truth" for its validation would be that it processes and displays images correctly according to specifications, which is established through standard software verification and validation testing, not clinical "ground truth" from a diagnostic perspective.

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