AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Neuropathology

Showing 1 to 10 of 14 articles

Clear Filters

Advances in Deep Neuropathological Phenotyping of Alzheimer Disease: Past, Present, and Future.

Journal of neuropathology and experimental neurology
Alzheimer disease (AD) is a neurodegenerative disorder characterized pathologically by the presence of neurofibrillary tangles and amyloid beta (Aβ) plaques in the brain. The disease was first described in 1906 by Alois Alzheimer, and since then, the...

Deep learning from multiple experts improves identification of amyloid neuropathologies.

Acta neuropathologica communications
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...

Artificial intelligence techniques for neuropathological diagnostics and research.

Neuropathology : official journal of the Japanese Society of Neuropathology
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...

Contribution of whole slide imaging-based deep learning in the assessment of intraoperative and postoperative sections in neuropathology.

Brain pathology (Zurich, Switzerland)
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...

Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.

Nature communications
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...

Classifying Alzheimer's Disease Neuropathology Using Clinical and MRI Measurements.

Journal of Alzheimer's disease : JAD
BACKGROUND: Computer-aided machine learning models are being actively developed with clinically available biomarkers to diagnose Alzheimer's disease (AD) in living persons. Despite considerable work with cross-sectional in vivo data, many models lack...

Ranking and filtering of neuropathology features in the machine learning evaluation of dementia studies.

Brain pathology (Zurich, Switzerland)
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...

Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology.

eLife
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...

The emerging role of artificial intelligence in neuropathology: Where are we and where do we want to go?

Pathology, research and practice
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...

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

Pathology
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...