AIMC Topic: Atrophy

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Uncovering Image-Driven Subtypes with Distinct Pathology and Clinical Course in Autopsy-Confirmed Four Repeat Tauopathies.

Annals of neurology
OBJECTIVES: The four-repeat (4R) tauopathies are a group of neurodegenerative diseases, including progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and globular glial tauopathy (GGT). This study aimed to characterize spatiotempor...

Redefining diagnostic lesional status in temporal lobe epilepsy with artificial intelligence.

Brain : a journal of neurology
Despite decades of advancements in diagnostic MRI, 30%-50% of temporal lobe epilepsy (TLE) patients remain categorized as 'non-lesional' (i.e. MRI negative) based on visual assessment by human experts. MRI-negative patients face diagnostic uncertaint...

Segmentation with artificial intelligence and automated calculation of the corpus callosum index in multiple sclerosis.

Saudi medical journal
OBJECTIVES: To determine the corpus callosum index (CCI) differences between chronic phase multiple sclerosis (MS) patients and healthy individuals and to evaluate the corpus callosum segmentation in MS patients using artificial intelligence technolo...

Generalizable Deep Learning for the Detection of Incomplete and Complete Retinal Pigment Epithelium and Outer Retinal Atrophy: A MACUSTAR Report.

Translational vision science & technology
PURPOSE: The purpose of this study was to develop a deep learning algorithm for detecting and quantifying incomplete retinal pigment epithelium and outer retinal atrophy (iRORA) and complete retinal pigment epithelium and outer retinal atrophy (cRORA...

Artificial intelligence classifies primary progressive aphasia from connected speech.

Brain : a journal of neurology
Neurodegenerative dementia syndromes, such as primary progressive aphasias (PPA), have traditionally been diagnosed based, in part, on verbal and non-verbal cognitive profiles. Debate continues about whether PPA is best divided into three variants an...

Application of artificial intelligence-based magnetic resonance imaging in diagnosis of cerebral small vessel disease.

CNS neuroscience & therapeutics
Cerebral small vessel disease (CSVD) is an important cause of stroke, cognitive impairment, and other diseases, and its early quantitative evaluation can significantly improve patient prognosis. Magnetic resonance imaging (MRI) is an important method...

Sequence of Morphological Changes Preceding Atrophy in Intermediate AMD Using Deep Learning.

Investigative ophthalmology & visual science
PURPOSE: Investigating the sequence of morphological changes preceding outer plexiform layer (OPL) subsidence, a marker preceding geographic atrophy, in intermediate AMD (iAMD) using high-precision artificial intelligence (AI) quantifications on opti...

Quantitative Assessment of Fundus Tessellated Density in Highly Myopic Glaucoma Using Deep Learning.

Translational vision science & technology
PURPOSE: To characterize the fundus tessellated density (FTD) in highly myopic glaucoma (HMG) and high myopia (HM) for discovering early signs and diagnostic markers.

Integration of graph network with kernel SVM and logistic regression for identification of biomarkers in SCA12 and its diagnosis.

Cerebral cortex (New York, N.Y. : 1991)
Spinocerebellar ataxia type 12 is a hereditary and neurodegenerative illness commonly found in India. However, there is no established noninvasive automatic diagnostic system for its diagnosis and identification of imaging biomarkers. This work propo...

Unsupervised Learning Based on Meibography Enables Subtyping of Dry Eye Disease and Reveals Ocular Surface Features.

Investigative ophthalmology & visual science
PURPOSE: This study aimed to establish an image-based classification that can reveal the clinical characteristics of patients with dry eye using unsupervised learning methods.