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The ExamVue Apex flat panel x-ray detector system is indicated for use in general radiology including podiatry, orthopedic, and other specialties, and in mobile x-ray systems. The Exam Vue Apex flat panel x-ray detector system is not indicated for use in mammography.

The ExamVue Apex flat panel x-ray detector system consists of a line of 3 different models of solid state x-ray detectors, of differing size and characteristics, combined with a single controlling software designed for use by radiologists and radiology technicians for the acquisition of digital x-ray images. The ExamVue Apex flat panel x-ray detector system captures digital images of anatomy through the conversion of x-rays to electronic signals, eliminating the need for film or chemical processing to create a hard copy image. ExamVue Apex flat panel x-ray detector system incorporates the ExamVue Duo software, which performs the processing, presentation and storage of the image in DICOM format. All models of the ExamVue Apex flat panel x-ray detector system use a Si TFTD for the collection of light generated by a CsI scintillator, for the purpose of creating a digital x-ray image.

The three available models are:

EVA 14W, with a 14x17in (35x43cm) wireless cassette sized panel
EVA 17W, with a 17x17in (43x43cm) wireless cassette sized panel
EVA 10W, with a 10x12 (24x30cm) wireless cassette sized panel

The provided text describes the regulatory clearance of a digital X-ray detector system, the "ExamVue Apex," and its substantial equivalence to a predicate device. However, it does not contain specific acceptance criteria or an analytical study proving the device meets those criteria, as one would typically find for an AI/ML medical device submission with defined performance metrics (e.g., sensitivity, specificity, AUC).

Instead, the submission focuses on demonstrating substantial equivalence through:

• Bench Testing: Comparing engineering specifications like resolution, sensitivity, and dynamic range to the predicate device.
• Software Validation: Ensuring the software adheres to relevant standards (IEC 62304) and performs expected functions.
• Clinical Testing: An ABR-certified radiologist visually evaluating image quality as equivalent or better than the predicate device.

Therefore, many of the requested fields regarding a detailed statistical study (sample size, ground truth, expert adjudication, MRMC study, standalone performance) cannot be filled from the provided text because such a study, with quantitative acceptance criteria, does not appear to have been performed or reported in this 510(k) summary. The submission relies more on demonstrating equivalence through technical specifications and expert opinion on image quality rather than rigorous statistical performance criteria for an AI algorithm.

Here's a breakdown of what can be extracted and what cannot:

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

The provided text does not define explicit quantitative acceptance criteria for device performance in the typical AI/ML sense (e.g., a target sensitivity of X% or specificity of Y%). Instead, the "acceptance criteria" are implied by demonstrating "similar or greater imaging characteristics" compared to the predicate device and that the software "performs the same required basic functions."

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

• Sample Size: Not specified. The clinical testing merely states "Clinical data was provided with the submission to demonstrate equivalence with the predicate device. This data includes images of all the relevant ROI." It doesn't quantify the number of images or patients.
• Data Provenance: Not specified (country, retrospective/prospective).

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)

• Number of Experts: "an ABR certified radiologist" (singular, implied to be one).
• Qualifications: "ABR certified radiologist." No mention of years of experience.

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

• Adjudication Method: Not specified. Since only one radiologist is mentioned, it's likely "none" in the sense of a consensus or adjudication process among multiple readers. The single ABR-certified radiologist provided the evaluation.

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, an MRMC comparative effectiveness study was not explicitly stated or described. This submission is for a general X-ray detector system, not specifically an AI-powered diagnostic algorithm designed to assist human readers. The "AI" mentioned is the software components related to image acquisition, processing, and management, not a diagnostic AI.

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

• Standalone Performance: Not applicable in the context of an AI diagnostic algorithm. This device is a digital X-ray detector system. Its "performance" refers to image quality and functionality, not a diagnostic output from an algorithm that would then require standalone performance metrics (e.g., sensitivity/specificity for disease detection). The software handles image processing, presentation, and storage, not automated diagnosis.

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

• Type of Ground Truth: The "ground truth" for the clinical testing was the visual evaluation of image quality by an ABR-certified radiologist. It's not a diagnostic ground truth (like pathology for cancer detection) but rather an assessment of whether the images produced by the new device are diagnostically acceptable and equivalent/superior to those from the predicate device.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable/not specified. This device is an X-ray detector and associated software for image acquisition and processing, not a deep learning AI model that requires a "training set" to learn features for interpretation. The software's development (as per IEC 62304) involves validation and verification, but not "training" in the machine learning sense.

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

• Ground Truth for Training Set: Not applicable. (See #8).

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ExamVue Duo is a software for the acquisition, processing, storage and viewing of digital radiology studies. ExamVue Duo is intended for use by a qualified/trained doctor or technician on both adult and pediatric subjects. ExamVue Duo is indicated for use in general imaging including podiatry, orthopedic, and other specialties, and in mobile x-ray systems.

ExamVue Duo is not indicated for use in mammography.

The ExamVue Duo software is designed for use by radiologists and radiology technicians for the acquisition of digital x-ray images. It interfaces with 3rd party digital x-ray detectors and (optionally) generators and manufacturer supplied software for the acquisition and storage of digital x-ray studies. The ExamVue Duo software then provides a user interface for the viewing, annotating, and other workstation functions.

Exam Vue DR includes the ability to receive patient information and send studies to remote destinations using the DICOM 3.0 protocol.

I apologize, but the provided text from the FDA 510(k) Premarket Notification for the ExamVue Duo device does not include the specific details of a clinical study or performance data that would enable me to fulfill your request.

The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device (ExamVue DR) rather than providing detailed acceptance criteria and a standalone study proving the device meets those criteria.

Here's why I cannot provide the requested information based on the given text:

• No Acceptance Criteria Table or Reported Performance: The document lists characteristics for comparison between the proposed device (ExamVue Duo) and the predicate (ExamVue DR), but it doesn't define quantitative acceptance criteria for parameters like sensitivity, specificity, accuracy, or other performance metrics. It primarily states "SAME" or lists technical specifications (e.g., RAM, Processor) rather than clinical performance data.
• Absence of a Clinical Study Description: The document states "Bench tests reports and clinical data have been provided, detailing the direct comparison of functions between the ExamVue Duo and predicate device" (Page 4). However, the actual reports or detailed methodology of such "clinical data" are not included in this 510(k) summary. It vaguely refers to "direct comparison of functions," which likely means comparing the implementation of features rather than a clinical outcome study.
• No mention of AI/Algorithm-Specific Performance: The document describes ExamVue Duo as "software for the acquisition, processing, storage and viewing of digital radiology studies." While it mentions "image processing," there is no indication that the device includes an AI/ML algorithm that would necessitate specific performance metrics like those typically associated with AI-driven diagnostic aids (e.g., AUC, sensitivity, specificity for disease detection). The "added functions" like "Auto Stitching" and "Bone Suppression (option)" appear to be standard image processing features, not necessarily AI.
• No Details on Ground Truth Establishment, Experts, Adjudication, or MRMC Studies: Since no clinical study evaluating specific performance metrics is described in the provided text, there are no details about sample size, data provenance, number or qualifications of experts, adjudication methods, MRMC studies, or how ground truth was established for a test set.

In summary, the provided document is a 510(k) submission showing substantial equivalence, not a detailed clinical study report. It compares the technical specifications and general functions of the new device to a previously cleared one. Therefore, I cannot extract the specific information you are looking for regarding acceptance criteria and a study proving those criteria are met for an AI/CAD/similar device.

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Exam Vue DR is a software for the acquisition, processing, storage and viewing of two-dimensional digital radiology images. Exam Vue DR is intended for use by a qualified/trained doctor or technician on both adult and pediatric subjects. ExamVue DR is indicated for use in general radiology, specialist radiology including podiatry, orthopedic, and other specialties, and in mobile x-ray systems.

ExamVue DR is not indicated for use in mammography.

The Exam Vue DR software is designed for use by radiologists and radiology technicians for the acquisition of digital x-ray images. It interfaces with 3tt party digital x-ray detectors or CR scanners and manufacturer supplied software for the acquisition and storage of digital x-ray images. The ExamVue software then provides a user interface for the viewing, annotating, and other workstation functions. ExamVue DR includes the ability to receive patient information and send x-ray images to remote destinations using the DICOM 3.0 protocol.

The provided text is a 510(k) premarket notification for the ExamVue DR device. It describes the device's indications for use, comparison to a predicate device, and general statements about safety and performance testing. However, it does not contain specific acceptance criteria, detailed results of a study proving the device meets those criteria, or the other requested information regarding sample sizes, expert qualifications, or ground truth establishment.

The section titled "7. Safety, EMC and Performance Data" states:
"Safety testing and documentation was performed in accordance with IEEE 1012-2012, Standard for System and Software Verification and Validation. We have also provided performance and clinical testing using example X-ray detectors, as recommended by the FDA guidance document "Guidance for Industry and/or for FDA Reviewers/Staff and/or Compliance: Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices""

This indicates that performance and clinical testing were conducted, but the details of these tests, including the acceptance criteria and results, are not provided in this document.

Therefore, I cannot fulfill your request for:

1. A table of acceptance criteria and the reported device performance
2. Sample sizes used for the test set and data provenance
3. Number of experts used to establish ground truth and their qualifications
4. Adjudication method
5. MRMC comparative effectiveness study results or effect size
6. Standalone performance details
7. Type of ground truth used
8. Sample size for the training set
9. How ground truth for the training set was established

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The Clear Vision DR7000F product is 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.

The Clear Vision DR7000F system is intended to be used in medical clinics and hospitals for emergency, orthopedic, chiropractic, and other medical purposes. This device is not indicated for use in mammography.

The Clear Vision DR7000F system is a high-resolution digital imaging system designed for digital radiography. It is designed to replace conventional film radiography techniques. This system consists of a tube head/collimator assembly mounted on a U-Arm, along with a generator, generator control, and a detector, operating software.

The detector which is used proposed device is LTX240AA01-A (K090742) and LLX240AB01 (K102587) of Samsung Mobile Display Co., Ltd. These detectors are cleared by FDA 510(k).

The provided 510(k) summary for the Clear Vision DR7000F does not contain information about acceptance criteria or a study proving the device meets specific performance criteria related to AI or algorithm-only performance.

The document describes a digital radiography X-ray system, which is a hardware device, not an AI or algorithm-based diagnostic tool. The submission focuses on demonstrating substantial equivalence to predicate hardware devices and compliance with electrical, mechanical, environmental safety, and performance standards for X-ray systems.

Therefore, most of the requested information regarding AI/algorithm performance, ground truth establishment, expert review, and training/test set sizes is not applicable to this document.

Here's a breakdown of what can be extracted or inferred from the provided text, and where information is missing / not applicable:

1. Table of Acceptance Criteria and Reported Device Performance

Regarding specific questions related to AI/Algorithm performance:

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

• Not applicable / Not provided. This device is an X-ray imaging system, not an AI algorithm. The "clinical testing" mentioned for the X-ray detectors likely refers to performance evaluation under clinical conditions, not an algorithm's diagnostic accuracy on a test set of images.

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. No specific "ground truth" establishment for an algorithm's performance is mentioned. Evaluation of an X-ray system focuses on image quality, radiation dose, safety, and functionality, which are assessed against technical specifications and clinical utility, rather than diagnostic "ground truth" for an AI model.

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

• Not applicable / Not provided.

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

• Not applicable / Not provided. This submission is for a medical imaging device, not an AI-assisted diagnostic tool. No MRMC study is mentioned.

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

• Not applicable / Not provided. No standalone algorithm performance is discussed.

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

• Not applicable / Not provided. For an X-ray device, "ground truth" generally relates to physical measurements (e.g., spatial resolution, contrast-to-noise ratio, MTF, DQE) and clinical image quality (diagnostic acceptability) rather than a pathology reference for an AI diagnosis.

8. The sample size for the training set

• Not applicable / Not provided. No AI training set is mentioned.

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

• Not applicable / Not provided. No AI training is mentioned.

Summary regarding the device:

The Clear Vision DR7000F is a digital radiography X-ray system. The study proving it meets acceptance criteria primarily involves engineering and performance testing against established international standards (EN/IEC 60601 series) for medical electrical equipment, as well as specific guidance for solid-state X-ray imaging devices. The acceptance criteria relate to electrical, mechanical, environmental safety, electromagnetic compatibility (EMC), and the technical performance and clinical utility of the X-ray detectors. The "study" mentioned is the compilation of these satisfactory test results conducted by the manufacturer, demonstrating compliance and substantial equivalence to existing cleared predicate hardware devices.

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The Clear Vision DR 2000 product is 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.

The Clear Vision DR 2000 system is intended to be used in medical clinics and hospitals for emergency, orthopedic, chiropractic, and other medical purposes. This device is not indicated for use in mammography.

The Clear Vision DR2000 system is a high-resolution digital imaging system designed for digital radiography. It is designed to replace conventional film radiography techniques. This system consists of a tube head/collimator assembly mounted on a U-Arm, along with a generator, generator control, and a detector, operating software.

The detector which is used proposed device is QXR9 (K073056) and QXR16 (K080553) of Vieworks Co., Ltd. These detectors are cleared by FDA 510(k).

The provided 510(k) summary for the Clear Vision DR 2000 does not contain information about explicit acceptance criteria for diagnostic performance, nor does it detail a specific study proving the device meets such criteria in terms of clinical accuracy or reader performance.

Instead, the submission focuses on demonstrating substantial equivalence to predicate devices through technical specifications, safety, and electromagnetic compatibility (EMC) testing. The "performance data" mentioned refers to these engineering and safety tests rather than clinical performance for diagnostic accuracy.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Not available in the provided text. The submission focuses on demonstrating technical compliance and substantial equivalence to predicate devices, not on quantitative diagnostic performance metrics.

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

Not applicable. There is no mention of a clinical "test set" for diagnostic performance evaluation. The "testing" referred to in the document pertains to electrical safety, mechanical, and EMC tests.

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

Not applicable. No ground truth establishment for diagnostic performance is described.

4. Adjudication Method for the Test Set

Not applicable. No diagnostic performance test set or adjudication method 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

Not applicable. The Clear Vision DR 2000 is a digital radiography X-ray system, not an AI-powered diagnostic tool. Therefore, an MRMC study assessing AI assistance is not relevant to this submission.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Not applicable. The Clear Vision DR 2000 is a hardware system for image acquisition, not a standalone diagnostic algorithm.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, Etc.)

Not applicable. No diagnostic performance evaluation requiring ground truth is described.

8. The Sample Size for the Training Set

Not applicable. The Clear Vision DR 2000 is a medical imaging acquisition device; it does not inherently involve a "training set" in the context of machine learning or AI algorithms for diagnostic purposes.

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

Not applicable. As no training set is mentioned in the context of diagnostic algorithms, the establishment of ground truth for such a set is not relevant.

Summary of the Study Discussed in the 510(k) Submission:

The study detailed in this 510(k) submission is a series of engineering and safety tests to ensure the Clear Vision DR 2000 system meets relevant industry standards and is substantially equivalent to predicate devices. These tests include:

• Electrical, mechanical, environmental safety and performance testing according to standards EN/IEC 60601-1, EN/IEC 60601-1-1, EN/IEC 60601-1-3, EN/IEC 60601-2-7, EN/IEC 60601-2-28, and EN/IEC 60601-2-32.
• EMC testing in accordance with standard EN/IEC 60601-1-2(2007).

The acceptance criteria for these tests would be compliance with the specific requirements outlined in each of those EN/IEC standards. The reported device performance is that "All test results were satisfactory," indicating that the device met the specified engineering and safety criteria for each standard.

The focus of this 510(k) is to demonstrate that the device is safe and effective for its intended use as a digital radiography X-ray system, primarily by showing that its technical characteristics and safety features align with established standards and legally marketed predicate devices, rather than through a clinical study of diagnostic accuracy. The use of pre-cleared detectors (QXR9 and QXR16) also supports the claim of substantial equivalence.

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