TY - JOUR
T1 - Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol
AU - Haraldsen, Ira H.
AU - Hatlestad-Hall, Christoffer
AU - Marra, Camillo
AU - Renvall, Hanna
AU - Maestu, Fernando
AU - Acosta-Hernandez, Jorge
AU - Alfonsin, Soraya
AU - Andersson, Vebjorn
AU - Anand, Abhilash
AU - Ayllon, Victor
AU - Babic, Aleksandar
AU - Belhadi, Asma
AU - Birck, Cindy
AU - Bruna, Ricardo
AU - Caraglia, Naike
AU - Carrarini, Claudia
AU - Christensen, Erik
AU - Cicchetti, Americo
AU - Daugbjerg, Signe
AU - Di Bidino, Rossella
AU - Diaz-Ponce, Ana
AU - Drews, Ainar
AU - Giuffre, Guido Maria
AU - Georges, Jean
AU - Gil-Gregorio, Pedro
AU - Gove, Dianne
AU - Govers, Tim M.
AU - Hallock, Harry
AU - Hietanen, Marja
AU - Holmen, Lone
AU - Hotta, Jaakko
AU - Kaski, Samuel
AU - Khadka, Rabindra
AU - Kinnunen, Antti S.
AU - Koivisto, Anne M.
AU - Kulashekhar, Shrikanth
AU - Larsen, Denis
AU - Liljeström, Mia
AU - Lind, Pedro G.
AU - Marcos Dolado, Alberto
AU - Marshall, Serena
AU - Merz, Susanne
AU - Miraglia, Francesca
AU - Montonen, Juha
AU - Mäntynen, Ville
AU - Oksengard, Anne Rita
AU - Olazaran, Javier
AU - Paajanen, Teemu
AU - Pena, Jose M.
AU - Pena, Luis
AU - Peniche, Daniel lrabien
AU - Perez, Ana S.
AU - Radwan, Mohamed
AU - Ramirez-Torano, Federico
AU - Rodriguez-Pedrero, Andrea
AU - Saarinen, Timo
AU - Salas-Carrillo, Mario
AU - Salmelin, Riitta
AU - Sousa, Sonia
AU - Suyuthi, Abdillah
AU - Toft, Mathias
AU - Toharia, Pablo
AU - Tveitstol, Thomas
AU - Tveter, Mats
AU - Upreti, Ramesh
AU - Vermeulen, Robin J.
AU - Vecchio, Fabrizio
AU - Yazidi, Anis
AU - Rossini, Paolo Maria
N1 - | openaire: EC/H2020/964220/EU//AI-Mind
PY - 2024/1/5
Y1 - 2024/1/5
N2 - More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.
AB - More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.
KW - 113 Computer and information sciences
KW - 3112 Neurosciences
KW - AI-Mind
KW - Artificial intelligence
KW - Clinical study protocol
KW - Dementia
KW - Machine learning
KW - Mild cognitive impairment
KW - electroencephalography (EEG)
KW - magnetoencephalography (MEG)
UR - http://www.scopus.com/inward/record.url?scp=85186556494&partnerID=8YFLogxK
U2 - 10.3389/fnbot.2023.1289406
DO - 10.3389/fnbot.2023.1289406
M3 - Article
SN - 1662-5218
VL - 17
SP - 1
EP - 15
JO - Frontiers in Neurorobotics
JF - Frontiers in Neurorobotics
M1 - 1289406
ER -