Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Alzheimer Disease l: Introduction01:29

Alzheimer Disease l: Introduction

20
Alzheimer disease is a chronic, progressive, and irreversible neurodegenerative disorder and the most common cause of dementia in older adults. It leads to gradual neuronal loss, causing cognitive decline, behavioral changes, and loss of functional independence.Risk Factors and EtiologyThe disease is multifactorial. Age is the strongest risk factor, with prevalence doubling every 5 years after age 65. Genetic factors include mutations in genes such as APP, PSEN1, and PSEN2, which are associated...
20
Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

1.7K
Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
1.7K
Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

1.3K
Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
1.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Transitional Life Events in Friedreich Ataxia: Differential Age at Onset Perspectives.

Cerebellum (London, England)·2026
Same author

Cerebellar cognitive-affective syndrome in Friedreich Ataxia.

Journal of neurology·2026
Same author

Redefining the norms: an interdisciplinary perspective on language testing in multilinguals with acquired and progressive neurogenic disorders.

Alzheimer's & dementia (Amsterdam, Netherlands)·2026
Same author

Safety of Endovascular Thrombectomy in Isolated Cervical Internal Carotid Artery Occlusion While on Oral Anticoagulation.

Journal of stroke·2026
Same author

Connected-speech digital biomarkers for monitoring transcranial pulse stimulation in Alzheimer's disease: A pilot study.

Journal of Alzheimer's disease : JAD·2026
Same author

The FEES Dysphagia Index: a bias-resilient continuous score that captures expert clinical judgment in 2,943 neurological inpatients.

Journal of neurology·2026

Related Experiment Video

Updated: Apr 28, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.0K

Detecting CSF-validated Alzheimer's disease from spontaneous speech in German: an interpretable end-to-end

Daniel Wiechmann1, Elma Kerz2, Milena Albrecht3

  • 1Institute for Logic, Language and Computation (ILLC), University of Amsterdam, Amsterdam, Netherlands.

Frontiers in Neurology
|April 27, 2026
PubMed
Summary

This study introduces an AI framework to detect Alzheimer's disease (AD) using German speech, identifying key linguistic markers for early diagnosis. The AI achieved high accuracy, offering a non-invasive tool to complement existing AD diagnostics.

Keywords:
Alzheimer’s diseaseCSF biomarkersdigital biomarkersexplainable AImachine learningnatural language processing

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.0K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.8K

Related Experiment Videos

Last Updated: Apr 28, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.0K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.0K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.8K

Area of Science:

  • Computational linguistics
  • Artificial intelligence in healthcare
  • Neurodegenerative disease diagnostics

Background:

  • Speech and language impairments are early Alzheimer's disease (AD) symptoms.
  • Advances in NLP and AI enable precise quantification of these impairments.
  • Few studies link spontaneous speech to biologically verified AD, especially outside English.

Purpose of the Study:

  • To develop an end-to-end machine-learning framework for automatic AD detection from German speech.
  • To utilize clinical-biological criteria validated by cerebrospinal fluid (CSF) biomarkers.
  • To identify linguistically interpretable speech biomarkers for AD.

Main Methods:

  • Included 22 biomarker-defined AD cases and 22 cognitively healthy controls (CHC).
  • Elicited connected speech using the 'Cookie Theft' task and transcribed via automatic speech recognition (ASR).
  • Computed 32 linguistic biomarkers (information-theoretic, lexical, syntactic) and applied five supervised models with recursive feature elimination and SHAP for interpretability.

Main Results:

  • Seven key linguistic biomarkers were identified through recursive feature elimination.
  • Classifiers achieved ~91% accuracy, F1 ≈ 0.90, and sensitivity ≈ 0.90 in distinguishing AD from CHC.
  • SHAP analysis revealed information-theoretic and structural markers (e.g., compressibility, lexical density, clause length) as primary drivers of classification.

Conclusions:

  • Robust, clinically meaningful linguistic biomarkers can be extracted from spontaneous speech.
  • Information-theoretic and structural speech properties effectively capture Alzheimer's-related impairments.
  • AI-enabled speech analysis offers a scalable, non-invasive complement to biological AD biomarkers.