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The Models 2200DR and 1600DR Digital Stationary Radiographic Systems are intended for use by a qualified/trained doctor or technician on both adult and pediatric subjects for taking diagnostic radiographic exposures of the skull, spinal column, chest. abdomen, extremities, and other body parts. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position. Not for mammography.

This device represents a new combination of an already cleared solid state digital x-ray acquisition panel and software with the diagnostic x-ray compnents required to make a complete system. The digital panel is the Toshiba wired flat panel detector FDX-4343R, 17 in x 17in. (Cleared in K 143257 as well as other submissions.) The system has been tested with only this model of flat panel detector. The purchaser can select either a "C" arm configuration (1600DR) or an overhead tube crane configuration (2200DR). The x-ray generator is a CPI CMP 200DR. The x-ray tubes are supplied by Toshiba (E7252X Series), and the collimator is the Ralco R302A. The system complies with the CDRH Radiological Health performance standard in the Code of Federal Regulations, as well as the voluntary IEC standards IEC 60601-1-2, and IEC60601-2-54. The systems include an AEC feature.

The provided text describes a 510(k) premarket notification for the Beijing Polycon Medical Engineering Company's Models 2200DR and 1600DR Digital Stationary Radiographic Systems. The document focuses on demonstrating substantial equivalence to a predicate device, rather than proving the device meets specific acceptance criteria through a traditional clinical study with defined performance metrics.

However, based on the information provided, we can infer some aspects and address the questions to the best of our ability within the context of a 510(k) submission for an X-ray system.

1. Table of acceptance criteria and the reported device performance:

Since this is a 510(k) submission for an X-ray system, the "acceptance criteria" are generally related to demonstrating that the device is as safe and effective as a legally marketed predicate device, rather than specific performance metrics against a medical condition's diagnostic accuracy. The key performance metrics for an X-ray system are related to image quality and safety.

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

• Sample Size: The document mentions "Clinical images were provided" for the clinical testing of the ECOM software and to show the complete system works as intended. However, no specific sample size (number of patients or images) for this clinical testing is mentioned.
• Data Provenance: Not explicitly stated. Given that the manufacturer is Beijing Polycon Medical Engineering Company, it is plausible the data originated from China, but this is not confirmed. It is also not specified if the data was retrospective or prospective. The statement "Clinical images were provided" suggests pre-existing images were used rather than a newly designed prospective study.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The document only mentions that the device is "intended for use by a qualified/trained doctor or technician."

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

• Adjudication Method: Not specified.

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:

• MRMC Study: No. This document describes a 510(k) submission for a digital X-ray system, not an AI-powered diagnostic tool. Therefore, a MRMC comparative effectiveness study comparing human readers with and without AI assistance was not conducted or reported.

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

• Standalone Performance: Not applicable. This is an X-ray imaging system, not a standalone diagnostic algorithm. The "clinical testing" mentioned was likely to verify the proper operation of the image acquisition software within the system.

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

• Type of Ground Truth: Not specified. For a standard X-ray system's performance evaluation, ground truth would typically relate to image quality assessment by radiologists or technical performance metrics rather than disease-specific pathology or outcomes, but the document does not elaborate on how the "clinical images" were evaluated or what constituted their "ground truth" for proper system operation.

8. The sample size for the training set:

• Sample Size for Training Set: Not applicable in the context of this 510(k) summary. This device is an X-ray hardware system with imaging software, not an AI/ML algorithm that requires a "training set" in the conventional sense. The ECOM software would have undergone standard software validation and testing, not AI training.

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

• How Ground Truth for Training Set was Established: Not applicable. (See point 8).

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