Predicting amyloid status in Primary Progressive Aphasia using explainable artificial intelligence
Cole Robertson1, Daisy Hochberg2, Megan Quimby2
1Department of Psychology, Emory University, Atlanta, GA USA.
NPJ Dementia
|June 4, 2026
Summary
We developed an AI model using connected speech to accurately predict amyloid beta (Aβ) positivity, a key marker for Alzheimer's disease (AD). This accessible method can help identify patients needing further diagnostic tests.
Area of Science:
- Computational linguistics
- Artificial intelligence in medicine
- Neurodegenerative disease diagnostics
Background:
- Alzheimer's disease (AD) therapeutics targeting amyloid beta (Aβ) show promise, but Aβ positivity testing is costly.
- Accessible methods are needed to identify patients for timely diagnosis and treatment.
Purpose of the Study:
- To develop and validate a machine learning model for predicting Aβ positivity using connected speech.
- To utilize explainable AI (XAI) to identify linguistic features indicative of Aβ positivity for clinical application.
Main Methods:
- A pre-trained language model (Distil-RoBERTa) was trained on speech samples from 71 patients with Primary Progressive Aphasia (PPA).
- Amyloid positivity was confirmed via cerebrospinal fluid, amyloid PET, or autopsy.
- The LIME algorithm was used for XAI to interpret model predictions and identify linguistic features.
Main Results:
- The Distil-RoBERTa model achieved a mean accuracy of 92% in predicting Aβ positivity.
- XAI methods achieved 97% accuracy and identified novel speech patterns associated with Aβ positivity.
- This represents a 10% improvement over previous research.
Conclusions:
- Connected speech analysis is a valuable, accessible tool for predicting Aβ positivity in patients with PPA.
- XAI successfully revealed novel linguistic biomarkers for potential clinical use.
- Computational linguistic analysis of speech shows significant potential for diagnosing AD and related disorders.
Related Concept Videos
01:29Alzheimer Disease l: Introduction
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...
01:26Alzheimer's Disease: Overview
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 and tau...
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ and tau...
01:23Alzheimer Disease ll: Pathophysiology
Alzheimer disease involves structural changes in the brain that begin long before symptoms appear. The most distinctive features are extracellular neuritic plaques and intracellular neurofibrillary tangles.Neuritic plaques form in the cerebral cortex and around blood vessels. These plaques contain a dense core of beta-amyloid (Aβ)—a toxic protein fragment that clumps outside neurons. The core is surrounded by damaged neuronal extensions, as well as reactive astrocytes and microglia. Abnormal...