AIMC Topic: Neuropathology

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Neuropathology of focal epilepsy: the promise of artificial intelligence and digital Neuropathology 3.0.

Pathology Dec 19, 2024
Focal lesions of the human neocortex often cause drug-resistant epilepsy, yet ​surgical resection of the epileptogenic region has been proven as a successful strategy to control seizures in a carefully selected patient cohort. Continuous efforts to s...
Brain Epilepsies, Partial Neuropathology Humans Artificial Intelligence
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The emerging role of artificial intelligence in neuropathology: Where are we and where do we want to go?

Pathology, research and practice Oct 23, 2024
The field of neuropathology, a subspecialty of pathology which studies the diseases affecting the nervous system, is experiencing significant changes due to advancements in artificial intelligence (AI). Traditionally reliant on histological methods a...
Artificial Intelligence Machine Learning Deep Learning Humans Neuropathology
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Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology.

eLife Jun 19, 2024
We present open-source tools for three-dimensional (3D) analysis of photographs of dissected slices of human brains, which are routinely acquired in brain banks but seldom used for quantitative analysis. Our tools can: (1) 3D reconstruct a volume fro...
Neuropathology Magnetic Resonance Imaging Machine Learning Dissection Brain Imaging, Three-Dimensional Neuroimaging Humans Photography Alzheimer Disease
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Ranking and filtering of neuropathology features in the machine learning evaluation of dementia studies.

Brain pathology (Zurich, Switzerland) Feb 19, 2024
Early diagnosis of dementia diseases, such as Alzheimer's disease, is difficult because of the time and resources needed to perform neuropsychological and pathological assessments. Given the increasing use of machine learning methods to evaluate neur...
Aged Aged, 80 and over Male Brain Female Cohort Studies Alzheimer Disease Neuroimaging Aging Dementia Humans Machine Learning Neuropathology
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Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.

Nature communications Oct 11, 2023
Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value f...
Deep Learning Glioma Neuropathology Humans Adult Brain Neoplasms
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Contribution of whole slide imaging-based deep learning in the assessment of intraoperative and postoperative sections in neuropathology.

Brain pathology (Zurich, Switzerland) Apr 25, 2023
The pathological diagnosis of intracranial germinoma (IG), oligodendroglioma, and low-grade astrocytoma on intraoperative frozen section (IFS) and hematoxylin-eosin (HE)-staining section directly determines patients' treatment options, but it is a di...
Oligodendroglioma Neuropathology Humans Brain Neoplasms Ki-67 Antigen Deep Learning Astrocytoma
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Artificial intelligence techniques for neuropathological diagnostics and research.

Neuropathology : official journal of the Japanese Society of Neuropathology Nov 28, 2022
Artificial intelligence (AI) research began in theoretical neurophysiology, and the resulting classical paper on the McCulloch-Pitts mathematical neuron was written in a psychiatry department almost 80 years ago. However, the application of AI in dig...
Neuropathology Artificial Intelligence Machine Learning Brain Diseases Humans
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Deep learning from multiple experts improves identification of amyloid neuropathologies.

Acta neuropathologica communications Apr 28, 2022
Pathologists can label pathologies differently, making it challenging to yield consistent assessments in the absence of one ground truth. To address this problem, we present a deep learning (DL) approach that draws on a cohort of experts, weighs each...
Humans Prospective Studies Amyloid beta-Peptides Neuropathology Deep Learning Cerebral Amyloid Angiopathy
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Same same but different: A Web-based deep learning application revealed classifying features for the histopathologic distinction of cortical malformations.

Epilepsia Feb 20, 2020
OBJECTIVE: The microscopic review of hematoxylin-eosin-stained images of focal cortical dysplasia type IIb and cortical tuber of tuberous sclerosis complex remains challenging. Both entities are distinct subtypes of human malformations of cortical de...
Malformations of Cortical Development, Group I Neuropathology Tuberous Sclerosis Reproducibility of Results Cerebral Cortex Diagnosis, Computer-Assisted Proof of Concept Study Deep Learning Neural Networks, Computer Algorithms Humans Epilepsy ROC Curve Internet Area Under Curve Neurons
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Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy.

Laboratory investigation; a journal of technical methods and pathology Feb 15, 2019
Accumulation of abnormal tau in neurofibrillary tangles (NFT) occurs in Alzheimer disease (AD) and a spectrum of tauopathies. These tauopathies have diverse and overlapping morphological phenotypes that obscure classification and quantitative assessm...
Male Deep Learning Tauopathies Female Humans Neuropathology Aged Brain Aged, 80 and over
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