At the MIE 2024 in Athens: Our highlights

The MISIT team on site (from left to right: Celine Brandelik, Gülsüm Pehlivan, Dr. Dominik Müller, Steffen Netzband, Prof. Dr. Frank Kramer & Dr. Florian Auer) *? 威尼斯赌博游戏_威尼斯赌博app-【官网】 of Augsburg*

Entrance area MIE *? 威尼斯赌博游戏_威尼斯赌博app-【官网】 of Augsburg*

From September 25 to 29, we were able to present our latest research findings at the 34th 威尼斯赌博游戏_威尼斯赌博app-【官网】ical Informatics Europe Conference (MIE) of the EFMI - European Federation for 威尼斯赌博游戏_威尼斯赌博app-【官网】ical Informatics in Athens. It was a great opportunity to network with experts in medical informatics and to learn about current developments and challenges. der in Athen vorstellen. Es war eine gro?artige Gelegenheit, uns mit Expertinnen und Experten der medizinischen Informatik zu vernetzen und über aktuelle Entwicklungen und Herausforderungen zu informieren.

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Dr. Florian Auer presented his workt "A Web-Based Questionnaire Builder to Facilitate form Management for the Electronic Data Capture with REDCap", which describes a new web application for the easy creation of questionnaires for electronic data capture with REDCap. This browser-based application provides a user-friendly interface and enables the seamless import and export of questionnaires in REDCap CSV format. This solution improves efficiency and user-friendliness for both beginners and experienced users.

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There were also exciting insights into AI-supported image analysis in healthcare. In his presentation "Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer", Dr. Dominik Müller spoke about the use of deep learning for automated Gleason grading in prostate cancer. His research compares existing and new deep learning architectures and their potential for efficient digital pathology pipelines.

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His bachelor student Gülsüm Pehlivan also presented her paper "Deep Learning Based Automatic Fibroglandular Tissue Segmentation in Breast Magnetic Resonance Imaging Screening" on the automatic segmentation of fibroglandular tissue in breast MRI screenings using deep learning. This method helps to automatically determine tissue density.

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Our last lecture was given by Steffen Netzband: "Towards a FHIR-Based Framework for Analyzing Nursing-Related Data in Smaller Sized Care Facilities" presented a FHIR-based prototype framework that enables integrated data analysis from multiple sources in smaller care facilities.

MIE 2024 provided us with valuable insights and new perspectives for our research. We are already looking forward to MIE 2025 in Glasgow, Schottland!