TY - JOUR
T1 - Multimodal Machine Learning for Stroke Prognosis and Diagnosis
T2 - A Systematic Review
AU - Shurrab, Saeed
AU - Guerra-Manzanares, Alejandro
AU - Magid, Amani
AU - Piechowski-Jozwiak, Bartlomiej
AU - Atashzar, S. Farokh
AU - Shamout, Farah E.
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2024
Y1 - 2024
N2 - Stroke is a life-threatening medical condition that could lead to mortality or significant sensorimotor deficits. Various machine learning techniques have been successfully used to detect and predict stroke-related outcomes. Considering the diversity in the type of clinical modalities involved during management of patients with stroke, such as medical images, bio-signals, and clinical data, multimodal machine learning has become increasingly popular. Thus, we conducted a systematic literature review to understand the current status of state-of-the-art multimodal machine learning methods for stroke prognosis and diagnosis. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during literature search and selection, our results show that the most dominant techniques are related to the fusion paradigm, specifically early, joint and late fusion. We discuss opportunities to leverage other multimodal learning paradigms, such as multimodal translation and alignment, which are generally less explored. We also discuss the scale of datasets and types of modalities used to develop existing models, highlighting opportunities for the creation of more diverse multimodal datasets. Finally, we present ongoing challenges and provide a set of recommendations to drive the next generation of multimodal learning methods for improved prognosis and diagnosis of patients with stroke.
AB - Stroke is a life-threatening medical condition that could lead to mortality or significant sensorimotor deficits. Various machine learning techniques have been successfully used to detect and predict stroke-related outcomes. Considering the diversity in the type of clinical modalities involved during management of patients with stroke, such as medical images, bio-signals, and clinical data, multimodal machine learning has become increasingly popular. Thus, we conducted a systematic literature review to understand the current status of state-of-the-art multimodal machine learning methods for stroke prognosis and diagnosis. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during literature search and selection, our results show that the most dominant techniques are related to the fusion paradigm, specifically early, joint and late fusion. We discuss opportunities to leverage other multimodal learning paradigms, such as multimodal translation and alignment, which are generally less explored. We also discuss the scale of datasets and types of modalities used to develop existing models, highlighting opportunities for the creation of more diverse multimodal datasets. Finally, we present ongoing challenges and provide a set of recommendations to drive the next generation of multimodal learning methods for improved prognosis and diagnosis of patients with stroke.
KW - deep learning
KW - machine learning
KW - multimodal clinical data
KW - Stroke
UR - http://www.scopus.com/inward/record.url?scp=85201777488&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85201777488&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2024.3448238
DO - 10.1109/JBHI.2024.3448238
M3 - Review article
C2 - 39172620
AN - SCOPUS:85201777488
SN - 2168-2194
VL - 28
SP - 6958
EP - 6973
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 11
ER -