
| Company Name | Siemens Healthineers |
| Founded | 1847 |
| Head office | Erlangen, Germany |
| Website | https://www.siemens-healthineers.com/ |
| Contact | bas.idzenga@siemens-healthineers.com |
No description available
Coronary artery calcification, Heart, Lung lesions, Lung lobes, Segmentation and volume quantification of lungs, Thoracic aorta, Thoracic vertebrae; density quantification of lungs and vertebrae.
The AI-Rad Companion Chest CT solution offers a multiorgan approach with pulmonary, cardiovascular and musculoskeletal functionalities. Major functionalities: lung lobe segmentation, lung lesion detection (solid and sub-solid) and measurements (incl. longitudinal comparison), pneumonia analysis, heart segmentation and calcium detection, aorta segmentation and diameter measurements, as well as vertebra body segmentation and measurement.
Brain volume quantification, Longitudinal comparison, Normative comparison, Report generation, Segmentation, White matter hyperintensities
AI-Rad Companion Brain MR performs an automatic segmentation of the different brain areas, including an individual volumetric analysis. It compares the different volumes to a normative database and automatically generates a highlighted deviation map, so volumetric changes can be monitored. Additionally the brain White Matter Hyperintensities (WMH) feature provides WMH counts, volumes and locations. The product offers a longitudinal comparison of different timepoints.
Editable AI results (size, location, score), Global PI-RADS scoring, Lesion detection, quantification, and classification, Prostate gland segmentation and volumetric assessment, RTSS objects for TRUS-guided biopsy
AI-Rad Companion Prostate MR is an AI-powered post-processing software that supports prostate MRI interpretation and reporting. It enables automatic segmentation of the prostate gland and suspected lesions, quantitative analysis, and lesion classification from multiparametric MR images.
Exporting results in rtss format, Lesion candidate localization and classification, Pre-populated pi-rads report, Prostate gland segmentation and volumetric assessment, Suspicion map highlighting the lesion candidates
Currently only available with Siemens Healthineers' syngo.via Siemens Healthineers Prostate MR on syngo.via supports prostate MRI reading and reporting. By deploying artificial intelligence, Prostate MR assists in the interpretation of multiparametric Prostate MRI. The deep neural networks of Prostate MR are designed to aid the detection and classification of prostate lesions and generate a pre-populated report. The results can be exported as a RTSS object which is then used in TRUS-guided biopsy by the urologist. The product is not commercially available in some countries, e.g. the US. Due to regulatory reasons the future availability cannot be guaranteed. Please contact your local Siemens Healthineers organization for further details.
Atelectasis, Consolidation, Identifying and highlighting findings, Measuring distance tracheal tube and carina, Notification with an alert, Pleural effusion, Pneumothorax, Pulmonary lesions
The AI-Rad Companion Chest X-ray automatically characterizes radiographic findings for a valid chest scan in the lung and pleura. It works like a second or third reader to support radiologists in differential diagnosis and clinical decision-making. It is capable of detecting pneumothorax, pulmonary lesions (nodules, masses, and granuloma), actelectasis, consolidation and pleural effusion. The DL algorithms automatically detect the tip of the endotracheal tube and carina. The distance will be computed and notifies when the distance between an endotracheal tube tip and the carina is above or below the configured threshold.
Autosegmentation of OAR
AI-Rad Companion Organs RT automatically contours several organs at risk. This input can be used for further processing in a treatment planning system. This enables physicians to free up resources for other time-consuming tasks and drives standardization across different individuals and institutions.
Autosegmentation of OAR
DirectORGANS (Optimized Reconstruction based Generative Adversarial Networks) employs an optimized reconstruction that is used as a standardized input to the Deep Learning based autocontouring algorithm. Both processes, image optimization and automatic contouring, are embedded into the CT simulator enabling results as part of the image acquisition.
Autosegmentation of OAR
Deep-learning based autocontouring with machine learning-trained algorithms for CT.