Speech and language processing with deep learning for dementia diagnosis: A systematic review

Area of Science:

• Neuroscience
• Artificial Intelligence
• Computational Linguistics

Background:

• Dementia diagnosis is complex, resource-intensive, and faces accessibility issues, particularly in low and middle-income countries.
• Early dementia diagnosis enables interventions to slow progression and reduce patient and societal burdens.
• Speech and language data offer accessible resources for developing novel diagnostic tools.

Approach:

• Systematic review of 72 studies published between 2012-2022.
• Utilized PRISMA 2020 protocol for study identification and data extraction.
• Analyzed Deep Learning (DL) technologies applied to speech and language for dementia diagnosis.

Key Points:

• Deep Learning models effectively differentiate between healthy individuals and those with dementia based on speech and language patterns.
• Identified various DL technologies and their applications in dementia detection.
• Highlighted the potential of AI-driven speech analysis in supporting dementia diagnosis.

Conclusions:

• Deep Learning-based speech and language techniques show significant potential to aid in the dementia diagnostic process.
• Advancements in AI offer new possibilities for accessible and timely dementia detection.
• Further research is needed to address existing challenges and optimize DL applications for dementia diagnosis.