Search Results

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 VB70 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 VB70 the following already cleared features have been modified:

• Patient Marking
• Contouring
• 4D Features ●
• Basic Features of the subject device ●

The provided text describes the acceptance criteria and a study demonstrating that the lobe-based lung ventilation algorithm within the syngo.via RT Image Suite meets these criteria.

Here's the breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

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

• AI-based Lung Lobe Segmentation:

  • Test Set Sample Size: 18 radiotherapy patients (6 female, 12 male).
  • Data Provenance: Acquired from external clinical collaborations with radiotherapy departments in Europe and the Americas. The clinics used standard radiotherapy equipment and protocols to acquire the CT images.

• Lobe-based Lung Ventilation Algorithm (overall validation):

  • Test Set Sample Size: 108 CT datasets from 74 individual lung radiotherapy patients (25 female, 49 male; median age: 66 yrs, range 42-87 yrs).
  • Data Provenance: Not explicitly stated beyond being "clinical validation," but implies patient data.

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

The document does not explicitly state the number or qualifications of experts used to establish the ground truth for the test set. It mentions "annotated ground truth" for the AI segmentation component, but doesn't specify how many experts performed the annotation or their specific qualifications (e.g., "radiologist with 10 years of experience").

4. Adjudication Method for the Test Set

The document does not describe a specific adjudication method (e.g., 2+1, 3+1). It simply refers to "annotated ground truth" for the AI segmentation.

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 MRMC comparative effectiveness study was described where human readers improved with AI assistance versus without AI assistance. The study focuses on evaluating the standalone performance of the AI component and the overall algorithm against established ground truth or standard methods.

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

Yes, a standalone evaluation was done for the AI-based component and the overall lobe-based lung ventilation algorithm.

• The AI-based component for lung lobe segmentation was tested on an independent cohort, with its performance (DICE score) being measured directly against annotated ground truth.
• The entire lobe-based lung ventilation algorithm's output was compared to literature ground truth (first subgroup) and spirometry (second subgroup), indicating a standalone assessment of the algorithm's output.

7. The Type of Ground Truth Used

• AI-based Lung Lobe Segmentation: Expert (or human) annotated ground truth for CT scans.
• Lobe-based Lung Ventilation (4D-CT Normal Breathing): Expected values obtained from scientific literature.
• Lobe-based Lung Ventilation (Breathhold CT): Vital capacity measurements from spirometry (a clinical standard for pulmonary function assessment, acting as outcomes data).

8. The Sample Size for the Training Set

• AI-based Lung Lobe Segmentation:
  • Training Set Sample Size: 8721 thoracic CT scans.
  • Validation Set Sample Size (for AI training): 969 thoracic CT scans.

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

The document states that the segmentation model was trained and validated on "annotated thoracic CT scans." This implies that human experts (likely radiologists or other medical professionals) manually segmented the lung lobes to create the ground truth for both the training and internal validation datasets. The specific process or number of experts for this annotation is not detailed.

Ask a specific question about this device