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Wireless digital flat panel detector is indicated for digital imaging solutions designed to provide general radiographic diagnosis for human anatomy including both adult and pediatric patients. It is intended to replace film/screen systems in all general-purpose diagnostic procedures. The device is not intended for mammography or dental applications.

Mars1013X Wireless Digital Flat Panel Detector (Hereinafter referred to as Mars1013X) is the kind of wireless digital flat panel detector. It supports the single frame mode, with the key component of TFT/PD image sensor flat panel of active area: 33.18cm×25.28cm. The sensor plate of Mars1013X is direct-deposited with CsI(Cesium Iodide) scintillator to achieve the conversion from X-ray to visible photon. The visible photons are transformed to electron signals by diode capacitor array within TFT panel, which are composed and processed by connecting to scanning and readout electronics, consequently to form a panel image by transmitting to PC through the user interface. The major function of the Mars1013X is to convert the X-ray to digital image, with the application of high resolution X-ray imaging. Both kinds of detectors are the key component of DR system, enable to complete the digitalization of the medical X-ray imaging with the DR system software. iRay SDK(include iDetector) is intended to supply API interface for DR system manufacturers. DR system manufacturer control the detector by SDK interface. SDK is not intend to be used directly by other users beside DR system manufacturers.

The provided text is a 510(k) summary for the iRay Technology Taicang Ltd. Wireless Digital Flat Panel Detector (Mars1013X). It describes the device, its intended use, and its comparison to predicate devices to demonstrate substantial equivalence.

However, the document does NOT contain information about specific acceptance criteria related to a study proving the device meets those criteria in the context of AI/algorithm performance. It primarily focuses on the device's physical and technical specifications, electrical safety, biological evaluation, and comparison to predicate devices, but not on clinical performance metrics that would typically be established for an AI-powered diagnostic device.

The provided text describes a "Wireless Digital Flat Panel Detector", which is a hardware component for X-ray imaging, not an AI or algorithmic diagnostic device. Therefore, the questions related to AI-specific acceptance criteria, test sets, ground truth establishment, expert adjudication, MRMC studies, and standalone algorithm performance are not applicable to the information contained in this document.

The document states:

• "Mars1013X Wireless Digital Flat Panel Detector is the kind of wireless digital flat panel detector. It supports the single frame mode, with the key component of TFT/PD image sensor flat panel of active area: 33.18cm×25.28cm"
• "The major function of the Mars1013X is to convert the X-ray to digital image, with the application of high resolution X-ray imaging."

The performance metrics discussed (Spatial Resolution, MTF, DQE) are physical imaging characteristics of the detector itself, not diagnostic performance of an AI analyzing the images.

Therefore, it is not possible to extract the requested information regarding acceptance criteria and a study proving an AI device's performance from the provided text. The document pertains to the clearance of an X-ray detector, not an AI diagnostic algorithm.

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The TOPAZ Mobile DR System, is a mobile X-ray imaging system, for the purpose of acquiring X-ray images of the desired parts of a patient's anatomy.

This device is not intended for mammography, bone density, fluoroscopy and angiography applications.

The TOPAZ Mobile DR System, (TOPAZ), is a mobile x-ray system and is a modification of the predicate device. There are 2 models for TOPAZ: TOPAZ-32D (32KW) and TOPAZ-40D (40KW). TOPAZ, may be moved quietly and smoothly with a motor drive mechanism.

The core part of x-ray source is a tube assembly, motorized x-ray collimator, HV cable assembly and high frequency x-ray generator. A touch screen LCD based x-ray control console provides a user-friendly interface and technique selection. The Collimator supports high accuracy for selected x-ray field size over any SID. Selection of an anatomical study on the imaging software automatically sets up the x-ray generator's pre-programmed exposure technique.

The X-ray passing through a patient's body is sent to the detector and then converted into electrical signals. These signals go through the process of amplification and digital data conversion in the signal process on the workstation and saved in a DICOM file for review on the device or on a Picture Archiving & Communication System (PACS) workstation.

The provided text is a 510(k) summary for the DRGEM TOPAZ Mobile DR System. It discusses the device, its intended use, and its substantial equivalence to a predicate device. However, it does not contain any information about a study that proves the device meets specific acceptance criteria related to a human-in-the-loop or standalone AI performance study.

The "PERFORMANCE DATA" section explicitly states that "Nonclinical testing results are provided in the 510(k). Validation testing indicated that as required by the risk analysis, designated individuals performed all verification and validation activities and that the results demonstrated that the predetermined acceptance criteria were met." This refers to engineering and safety testing against recognized standards (e.g., IEC 60601-2-54, ANSI AAMI ES60601-1, IEC 62304 for software), not clinical performance studies involving AI or human readers.

The key information missing to answer your request is data from a clinical performance study (e.g., diagnostic accuracy, reader study results). The device described is a mobile X-ray imaging system, which captures images. There is no mention of any integrated AI component that would require an AI-specific performance study with acceptance criteria for diagnostic performance.

Therefore, I cannot populate the table or answer most of your questions as the necessary information is not present in the provided document.

Here's what can be inferred or stated based on the provided text, and what cannot:

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

Cannot populate. The document focuses on regulatory compliance and engineering standards for the X-ray system itself, not diagnostic performance metrics (e.g., sensitivity, specificity, AUC) for an AI component. The "acceptance criteria" mentioned are for non-clinical engineering and safety tests, not clinical performance.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

Not applicable/Not mentioned for clinical performance. The "test set" referenced in the document pertains to engineering validation and verification tests of the X-ray system's functions, not a clinical data set for evaluating diagnostic performance of an AI.

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 mentioned. Ground truth establishment by experts (e.g., radiologists) is relevant for diagnostic performance studies. This document reports on non-clinical engineering tests.

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

Not applicable/Not mentioned. Adjudication methods are used in reader studies or for ground truth establishment in clinical performance. This document does not describe such 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. The document does not describe any MRMC study.

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

No. The document does not describe any standalone algorithm performance testing related to diagnostic accuracy.

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

Not applicable/Not mentioned for clinical performance. For engineering tests, the "ground truth" would be the successful operation of the device according to its specifications and regulatory standards.

8. The sample size for the training set

Not applicable/Not mentioned. This information would be relevant for an AI device. The document describes an X-ray imaging system, not an AI.

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

Not applicable/Not mentioned. This information would be relevant for an AI device. The document describes an X-ray imaging system, not an AI.

Summary of the Device and its Testing as per the document:

The TOPAZ Mobile DR System is a mobile X-ray imaging system. The 510(k) submission primarily focuses on demonstrating its substantial equivalence to an existing predicate device (K183292, also a TOPAZ Mobile DR System from DRGEM). The main difference for this new 510(k) (K201124) is the addition of five previously cleared digital X-ray detectors to the TOPAZ system.

The "Performance Data" section details that the system underwent non-clinical testing against various recognized international and national standards (e.g., IEC 60601 series for medical electrical equipment safety, radiation protection, usability; IEC 62304 for medical device software; DICOM standards, JPEG standards, etc.). The acceptance criteria for these tests were met, demonstrating the device's safety and effectiveness as an X-ray imaging system, and its performance as well as the predicate device. This refers to the engineering and regulatory compliance of the hardware and integrated software for image acquisition and handling, not the diagnostic interpretation or AI assistance.

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The purpose of Acuity is to acquire, store, communicate, display and process medical X-ray images. These radiographic systems are intended for use by a qualified/trained physician or technician on both adult and pediatric subjects for taking diagnostic x-rays. Not for mammography, angiography, interventional, or fluoroscopy use.

RadmediX Acuity combines components into a complete stationary x-ray system, including software, tube stands, tube heads, collimators, generators, tables, and digital radiography panels.

Here's a breakdown of the acceptance criteria and the study information based on the provided text:

Acceptance Criteria and Device Performance:

The document describes the Acuity SDR Standard, Acuity SDR Plus, and Acuity FDR Standard as stationary x-ray systems. The core claim for substantial equivalence is that the device performs the same functions using the same technological methods as the predicate device (Visaris Vision®) to produce diagnostic x-ray images.

Study Information:

1. Sample Size and Data Provenance:

   • Test Set Sample Size: Not explicitly stated as a numerical sample size of clinical cases for a formal clinical trial. Instead, the non-clinical testing involved acquiring DICOM images from "all major body structures" using one AcuitySDR system configuration.
   • Data Provenance: The document does not specify the country of origin for the image acquisition or whether it was retrospective or prospective. It was a "field tested" system, implying real-world or simulated real-world scenarios.

2. Number of Experts and Qualifications (for Ground Truth):

   • Number of Experts: Not specified.
   • Qualifications of Experts: Not specified, beyond the statement that images were generated for "diagnostic x-rays" and assessed as "clinically acceptable." This implies evaluation by trained personnel (e.g., radiologic technologists, radiologists), but their specific qualifications or number are not detailed.

3. Adjudication Method:

   • No formal adjudication method is described. The assessment of image quality was based on whether images were "of high quality and contrast and clinically acceptable."

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • No MRMC comparative effectiveness study was performed or described. The submission focuses on demonstrating substantial equivalence through technical characteristics and non-clinical image quality assessment, not on human reader performance improvement with or without AI assistance.

5. Standalone Performance Study (Algorithm Only):

   • Yes, a standalone study (non-clinical testing) was performed. The algorithm (software) alongside the hardware components was tested to acquire and process images. The output (DICOM images) was then evaluated for inherent quality and acceptability. The assessment "all images were of high quality and contrast and clinically acceptable" refers to the output of the integrated system.

6. Type of Ground Truth Used:

   • Expert Consensus / Clinical Acceptability: The ground truth for image quality was established through subjective expert assessment of "high quality and contrast and clinically acceptable." It does not mention pathology or outcomes data.

7. Training Set Sample Size:

   • Not applicable. This device is an X-ray system, not an AI/ML algorithm that requires a training set in the typical sense for image interpretation or diagnosis. The "software" components mentioned (AccuVueMED, AccuVue) are image acquisition and processing software, which are generally deterministic and not "trained" on data in the way a diagnostic AI would be. They are "previously cleared software supplied with the system."

8. How Ground Truth for Training Set Was Established:

   • Not applicable, as there is no specific "training set" for an AI/ML algorithm in this submission. The software components were previously cleared by the FDA, implying their performance was established through other means at the time of their original clearance (K152172 and K141440).

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