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The Volume Viewing software is a visualization package for PACS workstations. It is intended to support the medical professional in the reading, analysis and diagnosis of DICOM compliant volumetric medical datasets. The software is intended as a general purpose digital image processing tool, with optional functionality to facilitate visualization and measurement of vessel features.

Other optional functionality is intended for the registration of anatomical (CT) on a second CT dataset or on functional volumetric image data (MR) to facilitate the comparison of various lesions. Volume and distance measurements are intended for evaluation and quantification of tumour measurements, and evaluation of both hard and soft tissues. The software also supports interactive segmentation of a region of interest (ROI), has a dedicated tool set for lung lesion segmentation, quantification and follow-up of lesions selected by the user and provides tools to define and edit paths such as centerlines through structures, which may be used to analyze cross-sections of structures, or to provide flythrough visualizations rendered along such centerline.

Caution: Web-browser access is available for review purposes. Images accessed through a web-browser (via a mobile device or by other means) should not be used to create a diagnosis, treatment plan, or other decision that may affect patient care.

IMPAX Volume Viewing is a general purpose medical image processing tool for the reading and analysis of 3D image datasets. It is also intended for the registration of anatomical (CT) image data onto functional (MR) data to facilitate the comparison of various lesions. Volume and distance measurements facilitate the quantification of lesions and the analysis of both soft and hard tissue.

A variant of the software also provides web-browser access for review purposes. Images accessed through a web-browser (via a mobile device or by other means) should not be used to create a diagnosis, treatment plan, or other decision that may affect patient care.

The new device is similar to the predicate devices. All are PACS system accessories that allow the user to view and manipulate 3D image data sets. This new version adds a dedicated tool set for lesion management and flythrough visualizations rendered along a centerline for endoscopic view of vessels and airways.

Principles of operation and technological characteristics of the new and predicate devices are the same.

The provided text describes the acceptance criteria and the study that proves the device meets those criteria for the IMPAX Volume Viewing 4.0 system.

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size: The document states that "representative clinical datasets" were selected and loaded by the radiologists. The exact number of cases/datasets in the test set is not specified.
• Data Provenance: The radiologists were invited to Agfa's facilities, implying the clinical datasets were likely from retrospective cases, although this is not explicitly stated. The country of origin of the data is not specified but given the location of the testing (Belgium), it is plausible the data also originated from Belgium or nearby European countries.

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

• Number of Experts: 3 radiologists
• Qualifications of Experts: The document states "3 radiologists from several Belgian hospitals." Specific years of experience or subspecialty certification (e.g., neuroradiologist, interventional radiologist) are not provided.

4. Adjudication method for the test set:

• The document states that the radiologists "executed typical workflows and scored the features under investigation. A scoring scale was implemented and acceptance criteria established." This implies a consensus-based scoring or independent scoring followed by a determination of whether acceptance criteria were met. An explicit adjudication method like "2+1" or "3+1" is not mentioned.

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:

• No, an MRMC comparative effectiveness study was not done. The study involved radiologists evaluating the functionality of the device, rather than comparing human reader performance with and without AI assistance on specific diagnostic tasks. The focus was on demonstrating the functionality and subjective adequacy of the new features and equivalence to predicate devices, not on improving human reader performance. This device is primarily a visualization and processing tool, not an AI-powered diagnostic aid that would typically be evaluated in an MRMC study for reader improvement.

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

• Yes, in part. The "Verification" section describes tests on the algorithms themselves:
  • Regression testing on measurement algorithms to ensure they provide the same output as the previous version.
  • Crosshair position checks to verify viewport linking.
  • The "accuracy of the lung nodule segmentation" was scored, suggesting a standalone performance aspect of this algorithm was evaluated against some reference.
• The "Validation" section involved human readers evaluating the new functionality, but the underlying measurements and segmentations are performed by the algorithms. So, the algorithms' standalone performance was assessed for accuracy and functionality, and then confirmed by human interaction during validation.

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

• For Measurement Accuracy: "Reference values" were used, which implies a pre-established, highly accurate measurement for the specific datasets (e.g., from a precisely measured phantom or a highly accurate prior measurement). The exact nature of these reference values is not explicitly stated.
• For Crosshair Position Checks: The ground truth was based on expected precise pixel/voxel alignment ("half a voxel").
• For New Functionality (Endoscopic Viewing, Lung Nodule Segmentation, Lesion Management Module): The ground truth was expert evaluation/consensus by the 3 radiologists regarding the adequacy and equivalence of the functionality.

8. The sample size for the training set:

• The document does not provide any information about a training set. This device is presented more as an advanced image processing and visualization tool rather than a machine learning/AI diagnostic algorithm that typically requires a large training set. While some algorithms (like segmentation) may inherently involve learned parameters, no details on their training are given.

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

• As no information on a training set is provided, the method for establishing its ground truth is also not specified.

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