Disclaimer:
NOVU project is funded by EXIST, the European Union (EU), the European Social Fonds (ESF) as well as the federal ministry of Economic Affairs and Energy (BMWi)
Vision:
NOVU is a group of young researchers committed to challenge the status quo of medical imaging. It has set out to create an eco-system using cutting-edge technologies to facilitate radiologists’ challenges in modern diagnostics.
Project goals:
NOVU employs artificial intelligence (AI) and deep learning techniques to enhance medical services to participate successfully in fighting cancer. Modern AI technologies can help detect cancer such as breast or lung lesions in medical images (e. g. Mammography). If detected early, the survival rate of breast cancer – one of the primary causes of death, can be expanded up to 90%. Consequently, implementing and enhancing large-scale screening programs by AI technologies, we strive to support healthcare providers and governments to excel in the provision of health.
Challenges:
The two main challenges. First, the gap between the number of radiologists and the demand for radiology scans is widening. In Germany, the shortage of radiologists amounts to 30%1) of the required working force. They have to face a 20 million images annual work load, which puts radiologists under pressure to reach the goal of reporting diagnostic results to patients within a week.
Second, medical-imaging diagnostics must reach high precision goals. Only 30 out of 1000 mammograms entail further investigations, 6 of which are diagnosed positively as breast cancer cases. The risk of undetected cases must be kept as low as possible. A false finding does not only lead to additional cost for healthcare providers, but in worse case scenarios can cost lives. AI-technologies can, therefore help preselect potential cases of cancer and thus help radiologists increase the rate of discovery.
Approach:
The NOVU group aim is to develop a cutting-edge technology using artificial intelligence and deep learning to automate the work process in medical imaging: detect, classify and track lesions. First and foremost, we are taking one step ahead to personalize cancer diagnostics. Additionally, we want to ensure a smooth integration of our technology into radiologists’ daily work routine.
Our team includes PhD students, Post-docs as well as radiologists and medical advisors. We are working together to create a more efficient, simplified "all-in-one" solution to relieve doctors of automatable, repetitive tasks so they can use their time to provide better patient care.
1)„Radiology Staff in Focus”. Report from Philips
Supervisors:
1. Univ.-Prof. Dr. Thomas Grebel
2. Prof. Dr.-Ing. Patrick Mäder
Partner with us:
Dr. Sherief Emam
sherief.emam@tu-ilmenau.de
+49 3677 69 4034
