Search Results

ILUMAVision is a software application used for the display and 3D visualization of medical image files from scanning devices, such as CT, MRI, PET or 3D Ultrasound.

It is intended for use by radiologists, clinicians, referring physicians and other qualified individuals to retrieve, process, render, review, store, print, and distribute DICOM 3.0 compliant images, utilizing standard PC hardware.

Additionally, ILUMAVision is a preoperative software application used for the simulation and evaluation of dental implants, orthodontic planning and surgical treatments.

ILUMAVision is not intended for use with mammography.

ILUMAVision is an image management software application used for the display and 3D visualization of medical image files obtained from scanning devices, such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) or three-dimensional (3D) ultrasound.

ILUMAVision uses image filtering, 3D reconstruction and quantitative algorithms to view, measure, and annotate images. ILUMA Vision can be used to make panoramic images and to monitor treatment progress, capture images, bookmark certain items in a treatment, generate and edit reports, and export datasets. The application can also query and import images directly from a Picture Archiving and Communication System (PACS) over a TCP/IP network.

It distributes DICOM 3.0 compliant images, using standard personal computer (PC) hardware. Images can also be saved in JPEG format.

The software is intended to run on a personal computer (PC).

Here's an analysis of the provided text regarding the acceptance criteria and study for the ILUMAVision, v. 2.2 device:

1. A table of acceptance criteria and the reported device performance

Based on the provided document, ILUMAVision v.2.2 is a software application for medical imaging and 3D visualization. The submission is a 510(k) premarket notification asserting substantial equivalence to a predicate device (ILUMAVision v.2.0).
Therefore, the acceptance criteria are primarily focused on demonstrated equivalence to the predicate device and the successful implementation of new features, rather than quantitative performance metrics for diagnostic accuracy.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document does not provide details about a specific test set in the conventional sense (e.g., patient cases) for evaluating clinical performance. The validation discussed refers to the software verification and validation of features, not a clinical study on diagnostic accuracy. Therefore, information on sample size, country of origin, or retrospective/prospective nature of data for clinical testing is not provided.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not provided in the document. As stated above, the validation appears to be software-centric rather than a clinical performance study requiring expert ground truth.

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

This information is not provided.

5. 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

A multi-reader, multi-case (MRMC) comparative effectiveness study was not mentioned or described in the provided document. The device is a "Picture Archiving and Communications System" software with added visualization and planning features; it's not described as an AI-assisted diagnostic tool designed to improve human reader performance in the context of a clinical trial.

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

A standalone performance study focused on algorithmic output without human input was not mentioned or described. The device is a user-controlled visualization and planning tool.

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

Given the nature of the device (image management and visualization software) and the context of a 510(k) for substantial equivalence, the "ground truth" for its functionality would likely involve software testing protocols, functional verification against specifications, and validation that the new features (e.g., temporal bone module, virtual endoscopy, PACS import/export) operate as intended and produce expected visual outputs or measurements accurately. This is not "clinical ground truth" like pathology or expert consensus on a diagnostic finding, but rather proof that the software functions correctly. Specific details on how this functional ground truth was established are not detailed beyond the general statement "The additional software features have been suitably verified and validated."

8. The sample size for the training set

The document does not describe a machine learning algorithm requiring a "training set." Therefore, information on training set sample size is not applicable and not provided.

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

As there is no mention of a machine learning component or a training set, this information is not applicable and not provided.

Ask a specific question about this device

ILUMA VISION is a software application used for the display and 3D visualization of medical image files from scanning devices, such as CT, MRI, or 3D Ultrasound. It is intended for use by radiologist, clinicians, referring physicians and other qualified individuals to retrieve, process, render, review, store, print, assist in diagnosis and distribute DICOM 3.0 compliant images, utilizing standard PC hardware. Additionally, ILUMA Vision is a preoperative software application used for the simulation and evaluation of dental implants, orthodontic planning and surgical treatments. The device is not intended for use with mammography.

Not Found

I am sorry, but the provided text does not contain information regarding objective acceptance criteria or a study proving that the device meets those criteria. The document is a 510(k) premarket notification letter from the FDA, confirming that the device, ILUMA Vision v2.0, is substantially equivalent to a legally marketed predicate device.

The letter mentions the device's intended use and the regulatory classification but does not include:

• A table of acceptance criteria or reported device performance.
• Details about sample sizes, data provenance, or ground truth establishment for any studies.
• Information on expert involvement, adjudication methods, or multi-reader multi-case studies.
• Data on standalone algorithm performance.

Ask a specific question about this device