Disease Progression Score Estimation From Multimodal Imaging and MicroRNA Data Using Supervised Variational Autoencoders

• Published in: IEEE journal of biomedical and health informatics Vol. 26; no. 12; pp. 6024 - 6035
• Main Authors: Kmetzsch, Virgilio, Becker, Emmanuelle, Saracino, Dario, Rinaldi, Daisy, Camuzat, Agnes, Le Ber, Isabelle, Colliot, Olivier
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Amyotrophic lateral sclerosis; Artificial Intelligence; Bioengineering; Biological system modeling; Biomarkers; Clinical trials; Computer Science; Computer Vision and Pattern Recognition; Cross-Sectional Studies; Datasets; Deep learning; Degenerative diseases; Dementia disorders; Disease; Disease Progression; Disease progression score; Engineering Sciences; Frontotemporal dementia; Humans; Image Processing; Imaging; Life Sciences; Medical Imaging; microRNA; MicroRNAs; MicroRNAs - genetics; miRNA; multimodal data; Multimodal Imaging; Mutation; neurodegenerative disease; Neurodegenerative diseases; Neuroimaging; Ribonucleic acid; RNA; Signal and Image processing; Transcriptomics; variational autoencoder; Deep learning; Neuroimaging; MicroRNA; Disease progression score; Variational autoencoder; Multimodal data; Neurodegenerative disease

Frontotemporal dementia and amyotrophic lateral sclerosis are rare neurodegenerative diseases with no effective treatment. The development of biomarkers allowing an accurate assessment of disease progression is crucial for evaluating new therapies. Concretely, neuroimaging and transcriptomic (microR...