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FiatLux Visualize™ is a medical diagnosis software application that allows physicians, radiologists, medical technicians, nurses, and other trained medical professionals to select, review and analyze DICOM images acquired from CT and MR devices. It provides a suite of tools for 2D/3D reconstruction based on the input image dataset.

FiatLux Visualize™ software must be installed on a suitable commercial computer platform. It is the user's responsibility to ensure the monitor quality and ambient light conditions are consistent with the clinical applications. Mammographic and compressed images are not supported for viewing.

FiatLux Visualize™ is a medical device software application that provides multidimensional visualization of CT and MR medical images. FiatLux Visualize allows the user to select and retrieve a patient series, display and view the images and data, interactively manipulate the images to visualize anatomy and pathology, and analyze the images using a set of tools. It reads DICOM CT and MR images transferred using removable media or Microsoft Windows networking.

This 510(k) summary for the FiatLux Visualize™ device, a medical diagnosis software application for viewing and analyzing CT and MR images, does not contain the detailed information necessary to fully address all aspects of your request regarding acceptance criteria and a study proving the device meets those criteria.

The document primarily focuses on establishing substantial equivalence to predicate devices based on intended use and technological characteristics, rather than on detailed performance metrics from a specific study.

However, I can extract and infer some information based on the provided text.

Inferences and Missing Information:

The document explicitly states: "Software development for the FiatLux Visualize™ software follows documented processes for software design, verification and validation testing. A risk assessment has been completed to identify potential design hazards that could cause an error or injury based on the use of the quantification results. Appropriate steps have been taken to control all identified risks for this type of medical device." This indicates that verification and validation activities were performed, which would typically involve testing against acceptance criteria, but the specific criteria and study details are not provided in this 510(k) summary.

Here's what can be extracted/inferred and what is explicitly missing:

1. Table of Acceptance Criteria and Reported Device Performance

Important Note: The acceptance criteria listed are primarily inferred from the "Device Description" and "Substantially Equivalent Device Comparison" sections, which highlight the device's intended functionalities and operational scope. The document does not provide quantitative performance metrics (e.g., specific accuracy, speed, or measurement precision values) that would typically be associated with a detailed performance study.

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

• Sample Size for Test Set: Not specified in the provided document.
• Data Provenance: Not specified in the provided document (e.g., country of origin, retrospective or prospective).

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• MRMC Study: The document does not indicate that an MRMC comparative effectiveness study was performed. The device is a viewer/analysis tool, and the submission focuses on its equivalence in functionality to existing products to aid human interpretation, rather than a quantifiable improvement in human reader performance with the AI.
• Effect Size of Human Reader Improvement: Not applicable, as no MRMC study is mentioned.

6. Standalone (Algorithm Only) Performance Study

• Standalone Study: The document does not describe a standalone performance study. The device is described as a "medical diagnosis software application" that "allows physicians, radiologists... to select, review and analyze DICOM images." It's an assistive tool for human interpretation, not an automated diagnostic algorithm acting independently. The text also states, "A physician, providing ample opportunity for competent human intervention, interprets images and information being displayed."

7. Type of Ground Truth Used

• Type of Ground Truth: Not specified. Given the nature of the device (image viewer and analysis tool), the "ground truth" for its verification and validation would likely be focused on the accuracy of its display, reconstruction, and measurement tools against known DICOM standards and image processing algorithms, rather than diagnostic outcomes.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. This device is described as an "Image Processing System, Radiology" and a "medical diagnosis software application that provides multidimensional visualization of CT and MR medical images." It is a software tool for display and analysis, not an AI/Machine Learning algorithm that typically requires a distinct "training set" in the sense of learning from labeled data to make predictions. Its development would involve software engineering and testing, not AI model training.

9. How Ground Truth for the Training Set Was Established

• How Ground Truth for Training Set Was Established: Not applicable, as this is not an AI/ML device that uses a training set in the typical sense. Its "ground truth" for development would relate to accurate implementation of image processing algorithms and display functionalities.

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