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The "GXR-ES/ECS" System is intended for use in generating radiographic images of human anatomy. The Diagnostic Xray System consisting of a high voltage (HV) generator, a tube support unit, an X-ray beam limiting device, patient table, wall Bucky stand, and a tube, operates on a high-frequency inverter method, and is primarily used in a hospital for diagnosis of diseases in skeletal, respiratory and urinary systems. Such as 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

The "GXR-ES/ECS" System is intended for use in generating radiographic images of human anatomy. The Diagnostic Xray System consisting of a high voltage (HV) generator, a tube support unit, an X-ray beam limiting device, patient table, wall Bucky stand, and a tube, operates on a high-frequency inverter method, and is primarily used in a hospital for diagnosis of diseases in skeletal, respiratory and urinary systems. Such as 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. The system offers stable operation and good performance, delivering state-of-the-art image quality with good quality images. The flat-panel detectors are not part of the subject device. The operator control console is designed in two types: one with a large graphic LCD panel display and soft membrane switch, and the other with a large graphic LCD panel display and intuitive GUI configuration with Touch function. The high frequency X-ray generator features excellent accuracy, reproducibility and long-term stability with UPS functionality. APR (Anatomical Programming) and optional AEC (Automatic Exposure Control) features provide controlled exposure factors. Automatic Calibration function minimizes calibration time. The control console offers graphic waveform and data of x-ray exposure. Remote Diagnosis Software can make reports and transfer them via internet.

The provided document, K232178, is a 510(k) summary for the DRGEM GXR-ES/ECS Diagnostic X-Ray System. It does not describe a study involving an AI/Machine Learning algorithm for diagnostic purposes, nor does it detail acceptance criteria for such an algorithm. Instead, it focuses on demonstrating substantial equivalence of a new X-ray hardware system to a predicate device based on non-clinical performance and safety standards.

Therefore, I cannot provide the requested information regarding acceptance criteria and a study proving an AI/Machine Learning device meets them, as this information is not present in the provided text. The document pertains to the clearance of a traditional X-ray imaging system.

The closest relevant information available in the document, which might be mistaken for "acceptance criteria" in the context of an AI study, are the non-clinical testing standards and their successful completion for the X-ray hardware system. However, these are general electrical, safety, and performance standards for an X-ray machine, not diagnostic accuracy metrics for an AI algorithm.

Here's a breakdown of why the requested information cannot be extracted from this document:

• No AI/Machine Learning Component: The device described is a "Diagnostic X-Ray System" which is a hardware unit (generator, tube support, beam limiter, table, Bucky stand, tube). It explicitly states that "The flat-panel detectors (a necessary component of a fully-functional diagnostic X-ray system) are not part of the subject device." and that the software is for "system control such as the collimation size, filter selection, Control of Generator." and is "identical to the predicate device". This indicates traditional X-ray equipment, not an AI-powered diagnostic tool.
• No Diagnostic Performance Study: The document describes non-clinical "Validation Test Plan" which "was designed to evaluate input functions, output functions, and actions performed by the subject device." It refers to meeting general safety and EMC standards (IEC 60601 series, ISO 14971, etc.) for an X-ray machine. There is no mention of a diagnostic performance study, reader studies, or ground truth establishment for diagnostic accuracy.
• "Acceptance Criteria" for Hardware: The "acceptance criteria" mentioned refer to successfully passing the listed safety, electrical, and performance standards for the X-ray system, not diagnostic accuracy of an algorithm.

In summary, the provided document is a 510(k) summary for an X-ray imaging device hardware, not an AI/Machine Learning diagnostic device. Therefore, the information requested about acceptance criteria and study data for an AI algorithm is not present.

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