In the technique, a machine learns characteristics of sounds of elderly people who answered easy questions from avatars on a computer.
By combining the features of dementia, such as delay in response to questions from avatars depending on the content of questions, intonation, articulation rate of the voice, and the percentage of nouns and verbs in utterance, the computer could distinguish the condition with 90 per cent accuracy, the researcher from Osaka University and Nara Institute of Science and Technology said.
"If this technology is further developed, it will become possible to know whether or not an elderly individual is in the early stages of dementia through conversation with computer avatars at home on a daily basis," said Takashi Kudo from Osaka.
"It will encourage them to seek medical help, leading to early diagnosis,"Kudo added.
The study was published in the IEEE Journal of Translational Engineering in Health and Medicine.
The researchers proposed machine learning algorithms for detecting signs of dementia in its early stages, developing a dementia detection system using interactive computer avatars.
They created a model for machine learning based on features of speech, language, and faces from recorded dialogues with elderly participants.
Through machine learning, a computer was able to distinguish individuals with dementia from healthy controls at a rate of 90 per cent in six questions (two-three minutes per question), the findings showed.
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