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Volume Interactions Pte Ltd's Image Processing System is a medical device for the display and visualization of 3D medical image data derived from CT and MRI scans. It is intended to be used by qualified and trained medial professionals, after proper installation.

Volume Interactions Pte Ltd's Image Processing System is not intended to be used in direct contact with the patient nor is it intended to be connected to equipment that is used in direct contact with the patient.

Volume Interactions Image Processing System reads DICOM 3.0 format medical image data sets (and other formats) and displays 3D image reconstructions of these data sets through various user selectable industry standard rendering methods and algorithms. The clinical users can spatially manipulate, process to highlight structures and volumes of interest, and measure distances and volumes in the 3D image reconstructions. The processed data can be stored either as 3D image data in a proprietary format. or as 2D picture projections of the 3D image data in TIFF image format. The system runs on commercially available IBM PC compatible computers and hardware components with the Microsoft Windows NT and 2000 operating systems.

The system consists of three product modules namely, VizDexter™ 2.0, Dextroscope™ and DextroBeam™. The modules are described as follows:

VizDexter™ 2.0 is software that processes tomographic (e.g.: Computer Tomography, Magnetic Resonance Imaging) data and produces stereoscopic 3D renderings for surgery planning and visualization purposes. The software user selectable industry standard rendering methods and algorithms.

Dextroscope™ is an interactive console and display system that allows the user to interact with two hands with the 3D images generated by the VizDexter™ software. The Dextroscope™ user works seated, with both forearms positioned on armrests. Wearing stereoscopic glasses, the user looks into a mirror and perceives the virtual image within comfortable reach of both hands for precise hand-eye coordinated manipulation. The hardware uses various industry standard components.

DextroBeam™ is an interactive console intended for group collaborative discussions with 3D images using a stereoscopic projection system. The DextroBeam™ system uses the base of the Dextroscope™ as the 3D interaction interface with the virtual objects. The monitor of the Dextroscope™ is replaced by a screen projection system, so instead of looking into the mirror of the Dextroscope™, the user looks at large stereoscopic screen projections while working with the virtual data in reach of his hands. This enables the discussion of 3D data sets with other specialists in stereoscopic 3D. The hardware uses various industry standard components.

The provided text is a 510(k) summary for the Volume Interactions Image Processing System (VizDexter™ 2.0, Dextroscope™, and DextroBeam™). This document focuses on demonstrating substantial equivalence to predicate devices rather than proving performance against specific acceptance criteria through a study.

Therefore, the document does not contain the acceptance criteria or a study that proves the device meets specific performance criteria in the way a clinical validation or standalone performance study would. It primarily compares the technological characteristics and intended use of the new device with existing, legally marketed devices.

However, based on the information provided, here's what can be inferred or explicitly stated regarding the device's nature and the lack of specific performance study details:

1. Table of Acceptance Criteria and Reported Device Performance:

The 510(k) summary does not present specific quantitative acceptance criteria or a direct study measuring device performance against such criteria. The "performance" demonstrated is substantial equivalence to predicate devices in functionality and safety. The tables compare features, not quantitative performance metrics.

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

No information is provided regarding a "test set" in the context of a performance study. The submission relies on a comparison of technological characteristics with predicate devices.

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

Not applicable, as no performance study with a test set requiring ground truth establishment is described.

4. Adjudication Method for the Test Set:

Not applicable, as no performance study with a test set requiring adjudication is described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:

No MRMC study is mentioned in the document. The comparison is between the technological features of the device and its predicates, not a comparative effectiveness study involving human readers with and without AI assistance.

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

No standalone performance study for the algorithm itself (VizDexter™ 2.0) is described in terms of specific performance metrics. The document focuses on the system as a whole, including the human interaction components (Dextroscope™, DextroBeam™). The functions described are image processing and visualization, which inherently involve human interpretation.

7. The Type of Ground Truth Used:

Not applicable, as no performance study is detailed that would require a ground truth for evaluation.

8. The Sample Size for the Training Set:

No information is provided regarding a "training set." The device is an "Image Processing System" that utilizes "industry standard rendering methods and algorithms," implying it's not a machine learning model that would require a dedicated training set in the modern sense.

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

Not applicable, as no training set is mentioned.

In summary:

This 510(k) submission is a "substantial equivalence" filing for an image processing and visualization system from 2002. It focuses on demonstrating that the device has similar technological characteristics and intended use as already marketed devices. It does not present clinical performance data, acceptance criteria, or studies of the kind typically expected for AI/ML-driven diagnostic devices today. The "proof" of meeting acceptance criteria is implicitly through the FDA's determination of substantial equivalence based on the comparison of features and the device's classification as a Picture Archiving and Communications System, where the clinician retains responsibility for interpretation.

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