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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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The ClearVision ExamVue Flat Panel detector is indicated for use in general radiology, specialist radiology including podiatry, orthopedic, and other specialties, and in mobile x-ray systems.

The ClearVision ExamVue Flat Panel detector is not indicated for use in mammography.

The ClearVision ExamVue Flat Panel Detector consists of a line of 3 different models of solid state x-ray detectors, of differing size and characteristics, designed for use by radiologists and radiology technicians for the acquisition of digital x-ray images. The ClearVision ExamVue Flat Panel Detector 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. The ClearVision ExamVue Flat Panel Detector incorporates the ExamVueDR software, which performs the processing, presentation and storage of the image in DICOM format.

All models of the ClearVision ExamVue Flat Panel Detector use aSi 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:

• a. A 14x17in (35x43cm) tethered cassette sized panel
• b. A 14x17in (35x43cm) wireless cassette sized panel with automatic exposure detection
• c. A 17x17in (43x43cm) tethered panel for fixed installations.

The provided document is a 510(k) premarket notification for a medical device called the "ClearVision ExamVue Flat Panel Detector". This document primarily focuses on establishing substantial equivalence to previously cleared predicate devices rather than proving a device meets specific clinical acceptance criteria through a dedicated study.

Therefore, many of the requested elements for describing specific acceptance criteria and study details cannot be fully extracted from this document. The document presents laboratory performance data and mentions "clinical images" but does not detail a formal clinical study with specific acceptance criteria as one would find in a clinical trial.

However, based on the information provided, here's what can be inferred and stated:

Acceptance Criteria and Reported Device Performance

The document does not specify formal clinical acceptance criteria (e.g., sensitivity, specificity, accuracy targets that the device must meet for a specific diagnostic task). Instead, the "acceptance criteria" are implied by demonstrating substantial equivalence to predicate devices through technical specifications and performance characteristics, as well as indications for use and safety. The primary "study" proving the device meets these (implied) acceptance criteria is the comparison of its technical specifications and general performance to those of the predicate devices.

Study Details

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

   • The document mentions "Clinical images were provided" and "clinical testing of the hardware" but does not specify a separate "test set" in terms of number of cases/patients used to evaluate performance against specific diagnostic endpoints or ground truth.
   • Data provenance (country of origin, retrospective/prospective) is not specified for these "clinical images". The application is from JPI Healthcare Co., LTD, based in Seoul, South Korea, so the data may originate from there, but this is not explicitly stated.

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

   • This information is not provided. The "clinical images" and "clinical testing" are used to show the device "works as intended" in addition to laboratory data, rather than for a formal evaluation against expert-derived ground truth for diagnostic accuracy.

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

   • Not applicable as a formal adjudication process for a diagnostic performance test set is not described.

4. 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 MRMC comparative effectiveness study is mentioned. This device is an X-ray detector, not an AI diagnostic algorithm, so "human readers improve with AI vs without AI assistance" is not applicable in this context.

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

   • The device itself is a hardware component (Flat Panel Detector) and not an "algorithm" in the sense of an AI/CAD system. Its performance is assessed standalone through technical specifications and image quality metrics (DQE, MTF, Limiting Resolution) in a laboratory setting, and "clinical images" are used for qualitative assessment.

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

   • For the "clinical images" mentioned, the type of ground truth is not specified. Given the context, it's likely qualitative assessment by radiologists that the images are of diagnostic quality for their intended use, rather than a comparison to a definitive clinical ground truth for specific pathologies. For the technical specifications (DQE, MTF, Limiting Resolution), these are objective measurements derived from physical phantom or test object images, not clinical ground truth.

7. The sample size for the training set:

   • This device is hardware; it does not have a "training set" in the context of machine learning algorithms. The associated software (ExamVueDR) processes and presents images, but details about its own training data (if any for image processing algorithms) are not provided here.

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

   • Not applicable, as this is hardware and not a machine learning algorithm.

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