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Keynote Lectures

Available Soon
Eleni Kaldoudi, Democritus University of Thrace

A Spectroscopic Journey from Beer-Lambert to CAR-T Cell Production
Bruno Wacogne, FEMTO-ST, UMR CNRS 6174, France

From Computer to Cotside, Experiences in Developing Automated Neonatal EEG Analysis Tools Using Signal Processing and Machine Learning
William Marnane, University College Cork, Ireland

Available Soon

Eleni Kaldoudi
Democritus University of Thrace
https://www.linkedin.com/in/eleni-kaldoudi-3728472

Short Bio
Eleni Kaldoudi received a PhD in Bioengineering from UCL, London, UK (1994). Currently, she is a Professor in Medical Physics - Medical Informatics at the School of Medicine, Democritus University of Thrace, Greece and an Adjunct Researcher in ATHENA Research Center for Information and Communication Technologies, Greece. Her research interests are within the areas of medical informatics and telematics, in which she has published one book and over 100 papers in various journals and conference proceedings. She is currently coordinating the Horizon Europe 9.5MEuro project ThrombUS+ towards a novel approach and device for wearable continuous point-of-care monitoring, risk estimation and prevention for deep vein thrombosis. Eleni is also acting as the President of the European Alliance for Medical and Biological Engineering and Science (EAMBES).

Abstract
Available Soon

A Spectroscopic Journey from Beer-Lambert to CAR-T Cell Production

Bruno Wacogne
FEMTO-ST, UMR CNRS 6174, France
http://www.femto-st.fr/

Short Bio
Dr Bruno Wacogne is a CNRS Research Director at the FEMTO-ST Institute (one of the biggest Science and Technologies laboratory in France) where he was the head of the "Photonics for medical instrumentation" team before to join the BioMicroDevices group. He works now in the BIND group. He created and has led the Biom'@x transversal axis "Science et technology for personalized medicine" within this Institute for 15 years. In 2010, at the request of Besançon University Hospital, he applied and has been awarded a Translational Research Fellow position from the National AVIESAN Alliance. This is a supplementary position that allows him to be at the interface between the health activities at the FEMTO-ST Institute and the Clinical Investigation Center in Technological Innovation at Besançon University Hospital. At the hospital, he is the vice-coordinator of the Technological Innovation unit. His research interests concern translational research, science and technology for health and more precisely immuno-combined medical devices, biological qualification devices and biomedical optics. He now focuses on automated CAR-T cells culture systems. He is the author or co-author of over 250 communications among which about 40 invited conferences and keynote lectures and 10 patents. He is regularly chairman in international conferences. He has been awarded several times: Gold Micron at the International MICRONORA Workshop in 2006, Best Poster Award at the 2nd International Conference on Bio-sensing Technology in 2011, and Best Paper Award at the 13th International Conference on Biomedical Electronics and Devices in 2020.

Abstract
CAR-T cell therapies represent a breakthrough in cancer treatment, yet their widespread adoption is hindered by high production costs. Currently, essential quality control methods, performed throughout the production process, rely on bioreactor sampling—a process that paradoxically introduces additional contamination risks. To overcome these challenges, quality control must be re-imagined: real-time, label-free and closed-system monitoring that eliminates sampling and enables immediate intervention at the first sign of culture degradation. White light spectroscopy emerges as a transformative solution. Leveraging recent advancements in ultra-miniaturized spectrometers and embedded signal processing, this approach can deliver precise, label-free and sampling-less monitoring without the need for complex infrastructure. In this keynote, we will share recent results of real-time T-cell concentration and viability assessment, as well as contamination and cell death detection. Most significantly, and to our knowledge, we will introduce the first proof of concept of a fully automated culture system capable of real-time, sampling-less, and label-free monitoring—a milestone in the quest for safer, more cost-effective CAR-T cell production.

From Computer to Cotside, Experiences in Developing Automated Neonatal EEG Analysis Tools Using Signal Processing and Machine Learning

William Marnane
University College Cork, Ireland

Short Bio
William Marnane was Professor in Electrical and Electronic Engineering in University College Cork (UCC). He received the B.E. degree in electrical engineering from UCC in 1984, and the D.Phil. degree from the University of Oxford, U.K., in 1989. He was appointed lecturer in VLSI Design at the School of Electronic Engineering Science, University of Wales, Bangor, in 1989. In 1992 he was awarded a Post Doctoral Marie Curie Fellowship from the European at IRISA University of Rennes. In 1993 he was appointed as Lecturer in Digital Signal Processing in the Department of Electrical and Electronic Engineering at UCC, as Senior Lecturer in 1999 and Professor in 2014. He was Dean of Graduate Studies for UCC from 2013 to 2016. He was head of the School of Engineering, UCC, from 2016 to 2019. He was a Co-PI in the Science Foundation Ireland (SFI) funded Claude Shannon Institute for Discrete Mathematics, Coding and Cryptography (2006-2012). He was Director of the SFI funded Strategic Research Cluster on Efficient Embedded Digital Signal Processing for Mobile Digital Health (2007-2011). He was a founding PI and Deputy Director of the INFANT research centre- Irish Centre for Fetal and Neonatal Translational Research. His main research interests are in biomedical signal processing and machine learning. This research has focused on Neonatel EEG signal analysis and processing.

Abstract
Seizures are more common in the neonatal period than at any other time of life. Treatment of seizures is important, as evidence indicates that seizures damage the brain in addition to that caused by the underlying etiology. The use of electroencephalogram (EEG) for detecting seizures is increasing. Despite the availability of EEG, accurate diagnosis of neonatal seizures is challenging in clinical practice. Monitoring EEG requires specialist expertise and is resource intensive. This has led to a growing interest in developing automated systems to assist clinicians. This talk will present the development of a Seizure Detection algorithm, including algorithm development, classifier training, software implementation, clinical trial and deployment in the NICU. More recent developments in the use of deep learning algorithms, where rather than using handcrafted features, the classifier can be fed with raw EEG will be presented. The application of deep learning to both seizure detection and the assessment of Hypoxic-Ischemic Encephalopathy (HIE) severity will be discussed. Neonatal care is becoming increasingly complex with large amounts of rich, routinely recorded physiological, diagnostic and outcome data. Artificial intelligence (AI) has the potential to harness this vast quantity and range of information and become a powerful tool to support clinical decision making.