期刊
IEEE SIGNAL PROCESSING MAGAZINE
卷 39, 期 2, 页码 99-116出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MSP.2021.3126573
关键词
Neuroimaging; Deep learning; Magnetic resonance imaging; Aging; Predictive models
资金
- Fondazione Cariverona (Bando Ricerca Scientifica di Eccellenza 2018, EDIPO project) [2018.0855.2019]
- INVITE program - European Union within the Horizon 2020 Program
- Regione del Veneto
This article provides a comprehensive review of recent advances in using machine learning and deep learning techniques for predicting brain age, and explores the application of explainable artificial intelligence methods in biomedicine.
Marked changes occur in the brain during people's lives, and individual rates of aging have revealed pronounced differences, giving rise to subject-specific brainprints that are the signature of the brain. These are shaped by a great variety of factors, both endogenous and exogenous. Accurate predictions of brain age (BA) can be derived from neuroimaging endophenotypes by using machine and deep learning (DL) techniques. Predictive models leading to accurate estimates while revealing which features contribute the most to final predictions are key to unveiling the mechanisms underlying the evolution of brain aging patterns. Explainable artificial intelligence (XAI) methods are emerging as enabling technology in different fields, and biomedicine is no exception. Within this framework, this article examines BA and presents a comprehensive review of recent advances in the exploitation of explainable machine learning (ML)/DL methods, highlighting the main open issues and providing hints for future directions.
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