Utility of artificial intelligence “one-minute free conversational voice” analysis for detecting cognitive decline in individuals

Abstract Recent developments in artificial intelligence (AI) have provided new technologies that can aid in detecting cognitive decline. This study developed a voice AI model that screens for cognitive decline solely based on a short conversational voice sample. This study involved collecting voice data, AI machine learning (ML), and confirming accuracy using test data. AI extracts multiple voice features from the collected voice data to detect potential signs of cognitive impairment. Data labeling for ML was based on Mini-Mental State Examination scores; scores of 23 or lower were labeled as “cognitively declined (CD),” while scores above 24 were labeled as “cognitively normal (CN).” A fully coupled neural network architecture was employed for deep learning using voice data from 263 patients. Twenty voice samples, comprising “one-minute conversations,” were used for accuracy evaluation. The developed AI model achieved an accuracy of 0.950 in discriminating between CD and CN individuals, with a sensitivity of 0.875, specificity of 1.000, and average area under the curve of 0.990. This voice AI model serves as a promising cognitive screening tool accessible via mobile devices, requiring no specialized environments or equipment.