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New voice-based approach to enable early detection of Alzheimer’s

Chinese researchers have introduced a groundbreaking voice-based method to address language challenges and enable early detection of Alzheimer’s disease.

The team, led by Prof. Li Hai at the Hefei Institutes of Physical Science of the Chinese Academy of Sciences, noted that with the ageing global population, Alzheimer’s is becoming increasingly prevalent, making early detection critical for improving patient outcomes.

“Language decline is often one of the earliest indicators of cognitive decline,” the experts noted in a paper published in the IEEE Journal of Biomedical and Health Informatics.

 

Currently, available automated speech analysis offers a non-invasive and cost-effective approach to detecting Alzheimer’s. However, these methods face significant challenges, including complexity, poor interpretability, and limited integration of diverse data types, which hinder accuracy and clinical applicability.

To address these limitations, Hai’s team developed the DEMENTIA framework.

“This innovative approach integrates speech, text, and expert knowledge using a hybrid attention mechanism, significantly enhancing both the accuracy and clinical interpretability of Alzheimer’s disease detection,” the researchers said.

The framework leverages advanced large language model technologies. It captures intricate intra- and inter-modal interactions, improving detection accuracy and enabling the prediction of cognitive function scores.

Additionally, the model scores well in comprehensive interpretability analyses, demonstrating its robust clinical decision-support capabilities and adaptability across diverse datasets.

Alzheimer’s is a progressive disease that destroys memory and other critical mental functions. It is the most common form of dementia, constituting around 75 per cent of all dementia cases.

 

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