Frequently Asked Questions
What is EMERALD?
EMERALD is a funded research project focused on developing explainable AI technologies for diagnosing coronary artery disease (CAD) and non-small cell lung cancer (NSCLC). The project leverages advanced methods such as Machine Learning, Deep Learning, and Fuzzy Cognitive Maps (FCMs). These methods are integrated into a Medical Decision Support System (MDSS) to enhance diagnostic accuracy, provide personalized treatment options, and support decision-making in healthcare.
How is the project funded?
The EMERALD Project is funded by the Hellenic Foundation for Research and Innovation (H.F.R.I.) through the “2nd Call for H.F.R.I. Research Projects to support Faculty Members & Researchers” (Project Number: 3656) under ELIDEK. The authors are solely responsible for the content of this document.
Who coordinates the project?
The project is coordinated by Professor Elpiniki Papageorgiou, the Principal Investigator.
What partners are involved?
The partners involved in the EMERALD project include the University of Thessaly, where key contributors are Professor Elpiniki Papageorgiou, Associate Professor Nikolaos Papandrianos, Postdoctoral Researchers Ioannis Apostolopoulos, Serafeim Moustakidis, and Konstantinos Kokkinos, as well as PhD students Anna Feleki and Agorastos-Dimitrios Samaras. The EMERALD project also includes contributors from University of Patras, with Professor of Nuclear Medicine Dimitrios Apostolopoulos and Assistant Professor of Nuclear Medicine Nikolaos Papathanasiou. Additionally, external collaborators include Jose Maria Alonso Moral from the Research Center on Intelligent Technologies (CITIUS) of the University of Santiago de Compostela (USC), and Javier Andreu-Perez from the University of Essex.
How long does the project run for?
The project has a duration of 36 months.
What are the key results?
The main outcome of the EMERALD project is the development of an advanced Medical Decision Support System (MDSS) incorporating explainable AI methods for automated diagnostics in CAD and NSCLC.
How will the EMERALD solutions be tested and validated?
The developed algorithms are validated using external testing datasets, ensuring their accuracy and robustness. Additionally, a questionnaire is distributed to nuclear doctors to collect valuable feedback and assess the system’s effectiveness in real-world clinical settings.
