ConvNets for automatic detection of polyglutamine SCAs from brain MRIs: state of the art applications

• Published in: Medical & biological engineering & computing Vol. 61; no. 1; pp. 1 - 24
• Main Authors: Cabeza-Ruiz, Robin, Velázquez-Pérez, Luis, Pérez-Rodríguez, Roberto, Reetz, Kathrin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 2023; Springer Nature B.V
• Subjects: Artificial neural networks; Ataxia; Atrophy; Automation; Bioengineering; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Brain; Brain - diagnostic imaging; Cerebellum; Cognitive ability; Computer Applications; Dementia; Disease; Dysarthria; Dysphagia; Dystonia; Epilepsy; Genetic analysis; Genetic screening; Heart Arrest; Human Physiology; Humans; Image processing; Imaging; Magnetic resonance imaging; Medical imaging; Motor ability; Neural networks; Neurodegenerative diseases; Neuroimaging; Peptides; Polyglutamine; Psychosis; Radiology; Review Article; Spasticity; Spinocerebellar ataxia; Spinocerebellar Ataxias - genetics; Trinucleotide repeat diseases; Deep learning; Medical imaging; Neural network; Magnetic resonance imaging; Spinocerebellar ataxia
• ISSN: 0140-0118; 1741-0444
• DOI: 10.1007/s11517-022-02714-w

Polyglutamine spinocerebellar ataxias (polyQ SCAs) are a group of neurodegenerative diseases, clinically and genetically heterogeneous, characterized by loss of balance and motor coordination due to dysfunction of the cerebellum and its connections. The diagnosis of each type of polyQ SCA, alongside with genetic tests, includes medical images analysis, and its automation may help specialists to distinguish between each type. Convolutional neural networks (ConvNets or CNNs) have been recently used for medical image processing, with outstanding results. In this work, we present the main clinical and imaging features of polyglutamine SCAs, and the basics of CNNs. Finally, we review studies that have used this approach to automatically process brain medical images and may be applied to SCAs detection. We conclude by discussing the possible limitations and opportunities of using ConvNets for SCAs diagnose in the future. Graphical Abstract