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RadioVision is a Macintosh-based image management database, or system used primarily by dentists to acquire, archive, display, enhance, print, email, and import/export digital images.

RadioVision is a Macintosh-based image management database, or system used primarily by dentists to acquire, archive, display, enhance, print, email, and import/export digital images. Records are stored in a SQL database, searchable by any of the database table column names. The database stores basic patient information and digital image data without compression. Basic image enhancement information is also stored to the database so that the images can be re-displayed at a later time with enhancements applied, without altering the original image data. Images may be assembled into layouts which can be customized as required. RadioVision includes standard enhancements such as brightness, contrast, sharpening and false color. Additional advanced controls are also available such as histogram equalization and the CrystalView and Highlight filters. RadioVision includes an automatic backup feature that allows the database to be backed up once each day at a specific time, and will create time-stamped copies of the database so that one can return to a specific point in time. Radio Vision also includes the ability to restore a previously created database backup.

The provided text describes a 510(k) Pre-market Notification for the RadioVision device, a Macintosh-based image management database system for dental use. However, it does not contain the detailed information required to answer all aspects of your request regarding acceptance criteria and the comprehensive study proving the device meets those criteria.

Specifically, the document focuses on demonstrating substantial equivalence to a predicate device (VixWin Pro) and outlining basic safety and functionality. It lacks specific performance metrics, detailed study designs, sample sizes for test sets, expert qualifications, or comparative effectiveness results.

Here's a breakdown of what can and cannot be answered based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

This information is not provided in the document. The submission states a "Hazard Analysis was performed...which led to the development of Software Requirement Specifications (SRS). The SRS was used to develop a Verification & Validation (V&V) plan executed through a series of Test Cases. The V&V testing was passed, demonstrating that RadioVision performs as indicated." However, the specific acceptance criteria (e.g., accuracy, speed, specific image quality metrics) and the quantitative results of these tests are not detailed.

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

This information is not provided. The document mentions "a series of Test Cases" for V&V, but there is no mention of a "test set" in the context of clinical or performance data with associated sample sizes or data provenance.

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

This information is not provided. Without a defined test set for performance evaluation, there's no mention of experts establishing ground truth.

4. Adjudication Method for the Test Set

This information is not provided.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of Human Reader Improvement with AI vs. Without AI Assistance

This type of study was not conducted or reported. The RadioVision device is an "image management database, or system," not an AI-assisted diagnostic tool. Its primary functions are image acquisition, archiving, display, enhancement, printing, emailing, and import/export. The document does not describe any AI capabilities or studies involving human readers and AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

The device is a software system for image management and enhancement, not a standalone diagnostic algorithm. Therefore, "standalone performance" in the context of diagnostic accuracy is not applicable or reported. The V&V testing would have assessed its functional performance (e.g., image display, enhancement application, data storage integrity) rather than diagnostic accuracy.

7. The Type of Ground Truth Used

Given the nature of the device (image management and enhancement), the "ground truth" for its V&V testing would likely relate to the correctness and integrity of its core functions:

• Image fidelity: Ensuring that images are displayed, stored, and exported accurately without loss of information.
• Enhancement correctness: Verifying that applied enhancements (brightness, contrast, sharpening, false color, histogram equalization, CrystalView, Highlight filters) function as intended and produce the expected visual changes without introducing artifacts.
• Database integrity: Confirming that patient and image data is stored, retrieved, and backed up correctly.
• System functionality: Ensuring features like email, print, import/export work as described.

However, the document does not explicitly state the type of "ground truth" used for its V&V, only that tests were passed. It would be based on functional verification rather than clinical outcomes or pathology.

8. The Sample Size for the Training Set

This information is not provided. As RadioVision is an image management system and not an AI/machine learning model for diagnostic interpretation, the concept of a "training set" in the context of algorithm development for clinical decision support is not applicable. Its development would involve software engineering and testing, not machine learning model training.

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

This information is not applicable as there is no "training set" in the context you are asking about.

In summary, the provided 510(k) summary for RadioVision focuses on its intended use, device description, and comparison to a predicate device, primarily addressing software functionality and safety through a V&V process. It does not include the detailed performance study information, acceptance criteria, or ground truth methodologies typically associated with AI-powered diagnostic devices.

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