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The 'TOPAZ Mobile X-ray System' is intended for use in obtaining human anatomical images of patients who cannot be moved to the radiology department for medical diagnosis.

"TOPAZ" system is a system providing state-of-the-art image quality, user interface. "TOPAZ" system may be moved quietly and smoothly with motor drive mechanism "TOPAZ" system has a basic type column, and a collapsible type column option with a trendy design that allows driving without disturbing the front view. The core part of x-ray source adopts high quality tube assembly, motorized x-ray collimator. HV cable assembly and High Voltage X-Ray Generator. Touch screen LCD based x-ray control console provides user-friendly interface and easy technique selection. Collimator supports high accuracy for selected x-ray field size over any SID. Direct radiography via flat panel detector improves-exam speed and comfort with efficiency. Digital flat panel detector with Csl screen provides spatial resolution, MTF, DQE and stability based on fine pixel pitch. Selection of an anatomical study on the Digital Imaging Software automatically sets up the x-ray generator's preprogrammed exposure technique. The types of "TOPAZ" system are divided into TOPAZ-32D, and TOPAZ-40D according to maximum power and mA. The higher the maximum output, the wider the mA range to choose from, giving the user more technical options to choose from. The "TOPAZ Mobile X-ray System" consists of a tube assembly. x-ray collimator. High Voltage X-Rav Generator, detector and mechanical parts for mobility.

The provided text is a 510(k) Summary for the TOPAZ Mobile X-ray System, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting a performance study with acceptance criteria in the format typically used for AI/CADe devices. This document describes the device, its intended use, technological characteristics, and differences from the predicate, along with non-clinical testing for safety and EMC standards.

Therefore, the specific information about "acceptance criteria and the study that proves the device meets the acceptance criteria" as requested for AI/CADe devices (including details like sample size for test sets, data provenance, number of experts for ground truth, adjudication methods, MRMC studies, standalone performance, type of ground truth, and training set details) is not present in this 510(k) Summary.

This document primarily asserts that the "TOPAZ Mobile X-ray System" is substantially equivalent to the predicate device "TOPAZ Mobile DR System (K201124)" based on:

• Identical intended use.
• Similar technological characteristics, with modifications thoroughly tested for safety and effectiveness against international standards.
• Nonclinical testing results provided in the 510(k) demonstrating that predetermined acceptance criteria were met for safety (electrical safety, EMC, radiation protection) and software validation.

The "study that proves the device meets the acceptance criteria" in this context refers to the nonclinical testing against various recognized international and FDA standards, not a clinical performance study with human readers or pathology, as would be expected for AI/CADe systems.

Here's a summary of the available information regarding acceptance criteria and testing, tailored to what is provided in the document:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly present a table of acceptance criteria and reported device performance in the typical format for clinical accuracy for AI/CADe. Instead, it states that the device was assessed, tested, and passed predetermined testing criteria during validation testing, aligning with the risk analysis. It also confirms that the device meets "all the requirements listed in the Standards" (see the Standards table below). The "device performance" reported is its conformance to these standards and its substantial equivalence to the predicate.

Nonclinical Standards Met (acting as acceptance criteria for safety and effectiveness):

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

• Not Applicable/Not Provided. The document describes non-clinical engineering and software validation testing against standards, not a clinical study involving a "test set" of patient data for diagnostic performance. The focus is on the device's hardware, software (RADMAX), and new flat panel detectors meeting safety and electrical standards.

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/Not Provided. Ground truth establishment by experts is relevant for clinical performance studies, which this document does not describe.

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

• Not Applicable/Not Provided. This is relevant for clinical performance studies.

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. This document describes a 510(k) for an X-ray system, not an AI/CADe system. No MRMC study was performed or is mentioned.

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

• Not Applicable/No. The device itself is an X-ray system, not an algorithm, and its performance is assessed in terms of meeting engineering and regulatory standards, not standalone diagnostic performance. The imaging software (RADMAX) is mentioned as identical to the predicate and has a "Basic Documentation Level" of concern, implying human interpretation of images.

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

• Not Applicable/Not Provided. For the non-clinical testing described, "ground truth" would relate to the correct functioning of the hardware and software according to specifications and standards, not clinical diagnostic accuracy.

8. The sample size for the training set:

• Not Applicable/Not Provided. This is relevant for AI/ML models. While the device contains software, it is not described as an AI/ML diagnostic algorithm needing a training set. The software changes are primarily GUI and image processing module updates, verified for impact on safety and effectiveness (not AI training).

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

• Not Applicable/Not Provided. As above, this is not an AI/ML submission requiring a training set.

In summary, the provided 510(k) document is for a mobile X-ray system and demonstrates substantial equivalence through nonclinical testing against recognized performance, safety, and EMC standards, rather than a clinical performance study with acceptance criteria related to diagnostic accuracy, which would be typical for AI-powered diagnostic devices.

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