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The GXR-Series Diagnostic X-ray System is intended for use in obtaining human anatomical images for medical diagnostic by using X-rays.

The GXR Series Diagnostic X-ray System consists of a combination of an x-ray generator, and associated equipment such as tube stand, patient table, and, digital imaging system.

Here's an analysis of the provided text regarding acceptance criteria and supporting studies for the GXR-series diagnostic x-ray system:

It's important to note that the provided document is a 510(k) Summary, which is a regulatory filing for a medical device seeking clearance from the FDA based on substantial equivalence to a predicate device. It typically focuses on demonstrating that the new device is as safe and effective as a legally marketed device, rather than proving absolute performance against specific clinical acceptance criteria in a comprehensive clinical study. Therefore, the details provided often lean towards non-clinical testing and comparison with established standards.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of "acceptance criteria" for clinical performance. Instead, it relies on demonstrating adherence to recognized safety and performance standards, and comparison of technical characteristics to a predicate device. The "performance" is implicitly deemed acceptable if the device meets these standards and is substantially equivalent to the predicate.

Here's a generalized interpretation based on the document's content, focusing on what would typically be implied performance requirements for an X-ray system:

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size: The document does not specify a "test set" sample size in terms of clinical images or patient cases for performance evaluation against specific acceptance criteria. The testing discussed is primarily non-clinical, related to hardware and software verification and validation.
• Data Provenance: The document implies that the testing data is generated from laboratory testing and verification during the development and modification of the device. There is no mention of clinical data or patient data being used for the performance evaluation in this 510(k) summary. Given the context of a 510(k), particularly for an X-ray system, the primary focus is on engineering and performance testing against standards, rather than large-scale clinical studies. The data is thus likely prospective in terms of being generated specifically for this submission but is non-clinical in nature.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

This information is not provided in the document. As the evaluation is non-clinical, there is no mention of "ground truth established by experts" in the context of diagnostic performance evaluation. The "ground truth" for non-clinical testing would typically be the expected technical output or adherence to a standard, rather than expert interpretation of images.

4. Adjudication Method for the Test Set

This information is not provided as the testing described and implied is non-clinical and does not involve expert adjudication of diagnostic findings.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and effect size

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned or performed as part of this 510(k) submission. The document focuses on demonstrating substantial equivalence through technical comparisons and compliance with standards, not on proving improved reader performance with or without AI assistance.

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

This is not applicable in the sense of an AI algorithm's standalone performance. The device is a diagnostic X-ray system, which is inherently designed to be used with a human interpreter (a medical professional). While it has image processing software ("RADMAX"), this software enhances images for human diagnosis, not to provide an automated diagnosis itself.

7. The Type of Ground Truth Used

The "ground truth" for the non-clinical testing is adherence to technical specifications and international standards. For example, for radiation output, the ground truth is that the device delivers the specified kVp and mA, and for electrical safety, that it meets the requirements of IEC 60601-1. For image quality, it refers to intrinsic properties like spatial resolution, MTF, and DQE, which are measured objectively, not subjective expert consensus on diagnostic findings.

8. The Sample Size for the Training Set

This information is not applicable and therefore not provided. The device is an X-ray imaging system, not an AI/ML diagnostic algorithm that requires a training set of medical images in the conventional sense. The "training" for the device would involve calibration and configuration during manufacturing and installation to ensure it meets its technical specifications. The "RADMAX" software has image processing modules, but the document does not suggest these are deep learning models trained on vast datasets.

9. How the Ground Truth for the Training Set Was Established

This information is not applicable for the same reasons as #8. If any parameters for the image processing modules are "learned" or optimized, the document does not elaborate on this process or the ground truth used for such optimization.

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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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Intended for use by a qualified/trained doctor or technician on adult 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.

The Digital Radiography System (MobileApex 60E, MobileApex 60F, MobileApex 60G, MobileApex 60H) is comprised of a High Voltage Generator with a maximum power output of 63kW, the Digital Radiography System (MobileApex 60E, MobileApex 60F, MobileApex 60C, MobileApex 60H) can meet different exposure needs for varying positions and body mass. With the Digital Radiography System's (MobileApex 60E, MobileApex 60F, MobileApex 60G, MobileApex 60H) state-of-the-art design and powerful 63kW generator coupled with the Digital Detector (CareView 1500Cwe, CareView 750Cw, Mars1717X, Mars1417X, Luna 1012X) and Digital Radiography Operator Console, users can obtain clear images quickly and easily.

Based on the provided text, the device in question is a Digital Radiography System (MobileApex 60E, MobileApex 60F, MobileApex 60G, MobileApex 60H). The acceptance criteria and the study proving the device meets these criteria are focused on demonstrating substantial equivalence to a predicate device, the MX40 Mobile Digital X-ray System (K181874), rather than proving a specific, quantitative performance metric for a clinical application (like detection of a disease).

The documentation primarily discusses non-clinical performance tests related to electrical safety, electromagnetic compatibility, radiation protection, software validation, and usability. There is no mention of a clinical study to assess diagnostic performance (e.g., accuracy in detecting specific conditions).

Therefore, the following information is extracted and where information is not provided in the text, it is explicitly stated as "Not provided in the text."

Acceptance Criteria and Device Performance for Digital Radiography System

The acceptance criteria for this device are based on demonstrating substantial equivalence to a predicate device in terms of intended use, technological characteristics, and safety and effectiveness, primarily through compliance with recognized standards.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample size for test set: Not applicable/Not provided. The summary describes non-clinical engineering and performance testing against standards, not a clinical test set with patient data for diagnostic performance.
• Data provenance: Not applicable/Not provided. The testing described is type testing and verification against standards, not clinical data collection.

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

• Not applicable/Not provided. Ground truth in a diagnostic sense (e.g., for disease detection) was not established as part of this submission's described testing, which is focused on technical performance and safety.

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

• Not applicable/Not provided. No clinical test set requiring adjudication of ground truth is described.

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. An MRMC study was not done. This device is a mobile X-ray system, not an AI-powered diagnostic software.

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

• Not applicable. This is a hardware device (X-ray system), not a standalone algorithm.

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

• Not applicable for diagnostic performance. For the described non-clinical performance tests (e.g., electrical safety, radiation output), the "ground truth" is defined by the requirements and specifications of the relevant international and FDA performance standards (e.g., IEC 60601 series, 21 CFR 1020.30/31).

8. The sample size for the training set

• Not applicable/Not provided. This document describes a traditional medical device (X-ray system) and its non-clinical testing, not an AI/ML product that would require a training set.

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

• Not applicable/Not provided. (See point 8).

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