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syngo.via RT Image Suite is a 3D and 4D image visualization, multi-modality manipulation and contouring tool that helps the preparation of treatments such as, but not limited to those performed with radiation (for example, Brachytherapy, Particle Therapy, External Beam Radiation Therapy).

It provides tools to view existing contours, create, edit, modify, copy contours of regions of the body, such as but not limited to, skin outline, targets and organs-at-risk. It also provides functionalities to create simple geometric treatment plans. Contours, images and treatment plans can subsequently be exported to a Treatment Planning System.

The software combines the following digital image processing and visualization tools:

• . Multi-modality viewing and contouring of anatomical, and multi-parametric images such as but not limited to CT, PET, PET/CT, MRI, Linac CBCT images
• Multiplanar reconstruction (MPR) thin/thick, minimum intensity projection (MIP), volume ● rendering technique (VRT)
• . Freehand and semi-automatic contouring of regions-of-interest on any orientation including oblique
• Automated Contouring on CT images
• . Creation of contours on images supported by the application without prior assignment of a planning CT
• Manual and semi-automatic registration using rigid and deformable registration
• Supports the user in comparing, contouring, and adapting contours based on datasets acquired with different imaging modalities and at different time points
• . Supports multi-modality image fusion
• . Visualization and contouring of moving tumors and organs
• Management of points of interest including but not limited to the isocenter ●
• Creation of simple geometric treatment plans ●
• Generation of a synthetic CT based on multiple pre-define MR acquisitions ●

The subject device with the current software version SOMARIS/8 VB60 is an image analysis software for viewing, manipulation, 3D and 4D visualization, comparison of medical images from multiple imaging modalities and for the segmentation of tumors and organs-at-risk, prior to dosimetric planning in radiation therapy. syngo.via RT Image Suite combines routine and advanced digital image processing and visualization tools for manual and software assisted contouring of volumes of interest, identification of points of interest, sending isocenter points to an external laser system, registering images and exporting final results. syngo.via RT Image Suite supports the medical professional with tools to use during different steps in radiation therapy case preparation.

For the current software version SOMARIS/8 VB60 the following already cleared features have been modified:

• . Reference Point Management
• Patient Marking ●
• Contouring / Routine Contouring
• Structure Set Management ●
• Synthetic CT
• Basic Feature of syngo,via RT Image Suite

The provided documentation relates to the Siemens syngo.via RT Image Suite, specifically describing its 510(k) premarket notification for a new software version (SOMARIS/8 VB60) that includes an AI-based algorithm for synthetic CT generation.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Reported Device Performance

The document describes performance criteria for the AI-based algorithm for generating synthetic CT images from MR images. While not presented in a formal table with specific thresholds, the text outlines the key metrics evaluated and the results.

The document states that the geometric deviation of < 1 mm is "below the voxel resolution and therefore not clinically relevant," implying this meets the clinical relevance standard.

Study Details

The document refers to "performance tests (Non-clinical test reports)" and a "Summary of the Performance Evaluation of the Algorithm" to demonstrate meeting acceptance criteria.

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

• Sample Size: The document does not specify the exact sample size used for the test set for the AI algorithm. It only mentions "independent data."
• Data Provenance: The document does not specify the country of origin or whether the data was retrospective or prospective. It only states the AI algorithm was "tested on independent data."

2. Number of Experts Used to Establish Ground Truth and Qualifications:

• Number of Experts: Not specified.
• Qualifications: Not specified.
  • The ground truth methodology is not described in terms of expert consensus for the test set.

3. Adjudication Method for the Test Set:

• Not specified. The document states "automated bench tests" were used for geometric and HU accuracy, implying an objective, quantitative comparison rather than reader adjudication.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

• No MRMC study appears to have been performed or reported in this document for the AI-based synthetic CT algorithm.
• The evaluation focused on quantitative metrics (geometric and HU accuracy) of the algorithm's output, not on human reader performance with or without AI assistance.

5. Standalone (Algorithm Only) Performance:

• Yes, a standalone performance evaluation was conducted. The "Summary of the Performance Evaluation of the Algorithm" specifically details the AI-based algorithm's performance on "geometric fidelity and HU accuracy using automated bench tests." This implies an algorithm-only evaluation without human intervention in the performance measurement.

6. Type of Ground Truth Used:

• The ground truth for the synthetic CT evaluation appears to have been measured/reference CT images against which the synthetic CTs were compared for geometric and Hounsfield Unit (HU) accuracy. This falls under reference standard/objective measurement data, implying accuracy was determined by comparing the AI's output to a known, accurate CT.

7. Sample Size for the Training Set:

• Not specified. The document does not provide details about the training set size for the deep-learning algorithm.

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

• Not specified. The document indicates that the "algorithm for brain and pelvis synthetic CTs has been changed from Atlas based to a deep-learning algorithm." However, it does not describe how the ground truth for training this deep-learning algorithm was established (e.g., source of data, annotation methods, expert review, etc.).

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