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FlexView Diagnostic is a software application used for reference and diagnostic viewing and analysis of multi-modality medical imaging and non-imaging data (e.g., video information) with associated reports and information. FlexView Diagnostic enables qualified healthcare professionals, including (but not limited to) physicians, surgeons, nurses, and administrators to receive and view patient images, documents and data. FlexView Diagnostic allows qualified users to perform simple and complex image manipulations (including window/level, markups, 3D visualization) and measurements.

Not intended for primary diagnosis of mammographic images.

Not intended for diagnostic use on mobile devices.

FlexView Diagnostic is a stand-alone Web-based DICOM medical image viewer which allows downloading, reviewing, manipulating, and visualizing medical multi-modality image data in DICOM format and other data. FlexView Diagnostic is a server-based solution that may integrate with healthcare facility software systems and display DICOM images. FlexView Diagnostic enables healthcare professionals to access, manipulate, and measure DICOM images and collaborate using diagnostic quality medical images without installing client software.

The FlexView Diagnostic application has the following primary features and functions:

• Zero-footprint Web browser based
• Medical image upload, transfer and display of medical images between facilities
• Allows access to images for all participants in the healthcare process, including radiologists, technologists, physicians, nurses and other patient care practitioners
• Serves as an information and data management system for DICOM and non-DICOM medical images
• Contains functionality to view, annotate and measure images
• Contains industry-standard tools for image manipulation, annotation and measurements
• Advanced image manipulation functions like view synchronization across series, 3D visualization like MIP and MPR
• Encrypted transmission of medical images through secured networks
• HIPAA-compliant data management, including centralized storage of user activities via audit trails

FlexView Diagnostic consists of configurable software-only modules that display and process digital medical images, and associated medical information to aid in the day-to-day operations and workflow of clinicians and healthcare practitioners. The web browser based medical image viewer serves as the frontend module with which users interact when viewing the imaging data and other data.

The backend module handles the connection and processing of data from a variety of sources within the health system, in view of preparing visualizations to be rendered by the viewer.

FlexView Diagnostic can connect and access the medical images across different sources in a health system: such as an existing PACS, cloud storage or local server-based storage. The data connection and imaging data processing is handled by the FlexView backend module which supports the standardized transmission protocol as defined in the DICOM standard.

Users interact with FlexView Diagnostic through a standard web browser, thus providing access to full quality images from anywhere with an Internet access. Flex View Diagnostic utilizes authorization and authentication mechanisms to ensure that only authorized users can access the imaging data. The system extends beyond the hospital and its internal network. With proper authorization, FlexView Diagnostic can be accessed by clinical users outside of the hospital network. This way referring physicians can easily call up the imaging data of their patients or external expert accessing the imaging data for additional opinions.

After carefully reviewing the provided document, I cannot fulfill your request to describe the acceptance criteria and the study that proves the device meets them with the specified details. The document provides a 510(k) summary for the "FlexView Diagnostic" device, which focuses on demonstrating substantial equivalence to a predicate device ("ZeeroMED View").

While it mentions "Acceptance criteria for this test included statistical analyses such as mean, standard deviation, standard error and a one tailed t-test," it does not provide the actual numerical acceptance criteria (e.g., "mean delta must be less than X units").

Here's a breakdown of why I cannot complete your request with the desired information:

1. Table of Acceptance Criteria and Reported Device Performance:

   • The document states, "Acceptance criteria for this test included statistical analyses such as mean, standard deviation, standard error and a one tailed t-test." However, it does not specify the actual numerical acceptance criteria (e.g., the threshold for the mean delta, the p-value for the t-test, or the target standard deviation).
   • Similarly, it states, "Verification testing for all measurement tools passed the predetermined acceptance criteria," but it does not report the device's actual performance metrics (e.g., the calculated mean delta for length measurements, the standard deviation observed).
   • Therefore, I cannot create the table you requested with specific numerical values for acceptance criteria and reported device performance.

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

   • The document mentions "phantom (calibrated and uncalibrated) DICOM images" for measurement accuracy testing but does not specify the number of images or individual measurements included in the test set.
   • Data provenance (country of origin, retrospective/prospective) is not mentioned.

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

   • The document states, "measurement accuracy testing involved evaluating the difference (i.e., delta) between the expected length and the measurement entered by the user." This implies that a "user" performed the measurements. However, it does not specify if these "users" were experts, their number, or their qualifications. It also doesn't explicitly state who established the "expected length" (ground truth) for the phantom images.

4. Adjudication method for the test set:

   • No adjudication method is described. The testing involves comparing user measurements to an "expected length," not a consensus among multiple readers.

5. Multi-reader multi-case (MRMC) comparative effectiveness study:

   • No MRMC study is mentioned. The testing described is a standalone measurement accuracy test, not a comparative study of human readers with or without AI assistance.

6. Standalone (algorithm only without human-in-the-loop performance) study:

   • The "measurement accuracy testing" is for the device's measurement tools, which are part of the software's functionality. The description states it involved "manual testing" and "measurement entered by the user." This indicates it assesses the device's functionality with a human-in-the-loop (the user performing measurements). It is not a standalone algorithm performance test in the typical sense of an AI interpreting images on its own.

7. Type of ground truth used:

   • For the measurement accuracy testing, the ground truth was the "expected length" from "phantom (calibrated and uncalibrated) DICOM images." This can be considered a known, objective measurement established from the phantom characteristics.

8. Sample size for the training set:

   • The document does not mention a training set sample size or any details about a training set. The device, FlexView Diagnostic, is described as a "Medical Image Management And Processing System" and a "DICOM Viewer" with tools for image manipulation and measurement. It doesn't appear to be an AI/ML device that requires a training set in the typical sense for learning predictive models.

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

   • Since no training set is mentioned, this information is not available.

In summary, the provided document focuses on general software verification and validation, and specific measurement accuracy testing of the device's tools. It demonstrates that the device performs equivalently to a predicate device based on these non-clinical tests, but it does not delve into the detailed quantitative performance metrics or study designs typically found for AI/ML diagnostic algorithms.

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