Fraunhofer Institute for Computer Graphics Research IGDTrade fair / April 21, 2026 - April 23, 2026
DMEA 2026
Connecting Digital Health.
We cordially invite you to visit us at the joint Fraunhofer stand:
DMEA
Event details
Program
Congress Session: Next Level Care: AI, Telecare & Tools that Truly Change Everyday Practice
📃 Title: HealthView – Contactless Vital Data Acquisition at the Bedside
🕐 Program Slot: K010 – Wed, April 22, Stage 6.2, 12:55 – 13:55
🎤 Speaker: Dr.-Ing. Gerald Bieber
Location
Messe Berlin
Berlin Exhibition Grounds
Fraunhofer Joint Booth
Date
April 21, 2026 - April 23, 2026
Organization
Fraunhofer Institute for Computer Graphics Research IGD
Fraunhofer Institute for Computer Graphics Research – Rostock branch of the institute IGD
Tabbed contents
Exhibits
MeDiTwin – Interactive and Secure Access to Medical Data
Accessing one’s own medical data still involves many barriers for patients. Although the electronic patient record establishes the fundamental possibility of digital access, the user interfaces are often complex and cumbersome.
Fraunhofer IGD and SIT are developing secure and interactive technologies at the ATHENE research center to enable patients to easily gain an overview of their own data, visually and interactively explore medical information, and selectively and transparently share relevant data with medical institutions.
The exhibit demonstrates these new interactive possibilities and explains the underlying data security concept.
EHR Analysis in Nephrology
AI-supported Visual Analytics for Data-Driven Decisions
Electronic Health Records (EHR) contain extensive data on disease progression, therapies, and treatment outcomes in nephrology. This exhibit shows how artificial intelligence and visual analytics methods can make this data understandable, comparable, and usable.
Interactive visualizations and AI-based analysis methods make it possible to identify patterns in large EHR datasets, compare patient cohorts, and highlight relevant factors influencing disease progression. Complex relationships become transparent and support medical experts in research and clinical decision-making.
The exhibit illustrates the potential of intelligent data analysis for more precise, personalized, and evidence-based kidney care, while ensuring data protection and data sovereignty.
CareCam – Contactless Vital Sign Monitoring at the Hospital or Care Bed
In hospitals and inpatient care, increasing documentation requirements and the continuous monitoring of high-needs patients place significant strain on medical and nursing staff.
This is where Fraunhofer IGD’s CareCam technology comes in. Modern sensor technology and data analysis support clinical and nursing care while simultaneously improving patient safety. At its core is a contactless, non-invasive measurement of vital signs and patient well-being, directly at the bedside—without electrodes, cuffs, or wearable devices.
By intelligently analyzing multispectral imaging data, vital parameters such as heart rate, respiratory rate, skin temperature, movement patterns, and positional changes can be captured in real time. This enables continuous monitoring without disturbing patients or interrupting care processes.
In addition to analyzing key vital parameters, the system can detect relevant events such as restless sleep, atypical breathing patterns, or attempts to get out of bed. These capabilities help caregivers and medical staff identify critical changes at an early stage, automate routine tasks, and improve care for patients requiring close monitoring.
At the same time, the technology increases comfort for patients because no body-attached sensors are required.
CareCam clearly demonstrates how contactless sensor technology can become a key component of digitally supported care—secure, precise, and practical for everyday use.
AI-Based Surgical Planning and Risk Analysis for Kidney Tumors
In the BMFTR-funded project RENALCARE, Fraunhofer IGD is developing AI-based software solutions together with clinical and industrial partners to support patient-specific decision-making in the surgical treatment of kidney tumors. The focus lies on the automated analysis of medical imaging data to support the preoperative planning of partial nephrectomies.
Using AI-based segmentation and analysis methods, relevant anatomical structures such as the tumor, renal parenchyma, vascular system, and collecting system are extracted from CT data. Based on this information, established nephrometry scores such as PADUA and C-index are calculated automatically to objectively assess peri- and postoperative risks and evaluate the suitability of different treatment options.
The results are provided in a structured format and prepared for integration into existing clinical workflows. The exhibit demonstrates how medical image processing and artificial intelligence can help automate complex evaluation processes, improve decision quality, and facilitate the use of standardized scoring systems in everyday clinical practice.