5 min read
Fovia Ai to Showcase F.A.S.T.® aiCockpit® at Radiology Reimagined AI Exhibit, RSNA 2024
Fovia Ai, Inc., a subsidiary of Fovia, Inc., a world leader in advanced visualization for over two decades and a...
STRIKE
Structured therapy planning with AI decision support for liver interventions.
Hepatocellular carcinoma (HCC) is the second most common cause of cancer-related deaths worldwide. The choice of the optimal therapy and specialized therapy planning are of crucial
importance for the success of treatment and the survival of the patient. However, due to different treatment options, various contraindications and rapidly growing literature on individual therapies, this represents a major challenge in clinical practice.
The aim of STRIKE is to develop a demonstrator that uses artificial intelligence (AI) methods to support therapy selection of HCC. The demonstrator uses decision trees to structure and connect clinical data of patients and quantitative parameters from image data.
KI-BETA
AI-based evaluation of clinical imaging in prostate cancer.
Automated prostate cancer diagnosis: Smart Reporting receives € 1.2 million funding for AI research.
The grant has been awarded € by the German Federal Ministry of Education and Research for developing an artificial intelligence tool that assists physicians in the examination of prostate cancer. The KI-BETA project aims to improve patient management and outcomes by providing a solution for automated prostate cancer diagnosis in radiology and pathology.
A strong emphasis during development will be placed on optimizing physicians’ work flows and KI-BETA will hence be pursued in close cooperation with medical experts. The industry-in-clinic platform M3i will participate in the project to facilitate this process. M3i provides straight-forward access to clinical experts and serves to promote efficient and legally compliant development.
ImageREPORT
Novel system for automated medical image analysis and structured reporting for radiologists.
The ImageREPORT project will revolutionize radiology reporting of common pathologies on chest X-rays and chest CT-scans. The system will combine automated, deep learning-based Computer-Aided Detection (CAD) of lung pathologies with structured reporting, to improve accuracy and efficiency of reporting.
Partners: Thirona, Radboud UMC
Funding Agencies: European Union and German Ministry of Education and Research
RAPIDS
Radiomics Platform for Cloud-Based Image Diagnostics and Structured Reporting.
RAPIDS aims to combine Smart Reporting’s intelligent reporting software Smart Radiology with the performance management solution Siemens teamplay and state-of-the-art machine learning algorithms by TUM in order to develop a workflow optimized system for automated diagnostics in radiology.
Partners: Siemens Healthineers, Technical University of Munich Funding Agency: Bavarian Ministry of Economics, Media, Energy, and Technology
