AIMC Topic: Frontotemporal Dementia

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Analysis of convolutional neural networks for fronto-temporal dementia biomarker discovery.

International journal of computer assisted radiology and surgery
PURPOSE: Frontotemporal lobe dementia (FTD) results from the degeneration of the frontal and temporal lobes. It can manifest in several different ways, leading to the definition of variants characterised by their distinctive symptomatologies. As thes...

Detecting Alzheimer's Disease Stages and Frontotemporal Dementia in Time Courses of Resting-State fMRI Data Using a Machine Learning Approach.

Journal of imaging informatics in medicine
Early, accurate diagnosis of neurodegenerative dementia subtypes such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) is crucial for the effectiveness of their treatments. However, distinguishing these conditions becomes challenging whe...

Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples.

EBioMedicine
BACKGROUND: Dementia's diagnostic protocols are mostly based on standardised neuroimaging data collected in the Global North from homogeneous samples. In other non-stereotypical samples (participants with diverse admixture, genetics, demographics, MR...

Application of Artificial Intelligence to Quantitative Assessment of Fundus Tessellated Density in Young Adults with Different Refractions.

Ophthalmic research
INTRODUCTION: The aim of this study was to quantitatively assess fundus tessellated density (FTD) and associated factors by artificial intelligence (AI) in young adults.

Plasma neurofilament light chain protein is not increased in treatment-resistant schizophrenia and first-degree relatives.

The Australian and New Zealand journal of psychiatry
OBJECTIVE: Schizophrenia, a complex psychiatric disorder, is often associated with cognitive, neurological and neuroimaging abnormalities. The processes underlying these abnormalities, and whether a subset of people with schizophrenia have a neuropro...

Characterizing the Clinical Features and Atrophy Patterns of -Related Frontotemporal Dementia With Disease Progression Modeling.

Neurology
BACKGROUND AND OBJECTIVE: Mutations in the gene cause frontotemporal dementia (FTD). Most previous studies investigating the neuroanatomical signature of mutations have grouped all different mutations together and shown an association with focal at...

Using diffusion tensor imaging to detect cortical changes in fronto-temporal dementia subtypes.

Scientific reports
Fronto-temporal dementia (FTD) is a common type of presenile dementia, characterized by a heterogeneous clinical presentation that includes three main subtypes: behavioural-variant FTD, non-fluent/agrammatic variant primary progressive aphasia and se...

Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach.

NeuroImage
Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be complemented by computational decision-support methods to objectively analyze multidimensional meas...

Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease.

NeuroImage. Clinical
BACKGROUND: In a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, the subtlety of cortical atrophy in some frontotemporal dementia (FTD) patients and overlapping patter...

Diagnostic accuracy of frontotemporal dementia. An artificial intelligence-powered study of symptoms, imaging and clinical judgement.

Advances in medical sciences
PURPOSE: Frontotemporal dementia (FTD) is a neurodegenerative disorder associated with a poor prognosis and a substantial reduction in quality of life. The rate of misdiagnosis of FTD is very high, with patients often waiting for years without a firm...