| Summary: | Abstract Introduction We present a methodology to automatically evaluate the performance of patients during picture description tasks. Methods Transcriptions and audio recordings of the Cookie Theft picture description task were used. With 25 healthy elderly control (HC) samples and an information coverage measure, we automatically generated a population‐specific referent. We then assessed 517 transcriptions (257 Alzheimer's disease [AD], 217 HC, and 43 mild cognitively impaired samples) according to their informativeness and pertinence against this referent. We extracted linguistic and phonetic metrics which previous literature correlated to early‐stage AD. We trained two learners to distinguish HCs from cognitively impaired individuals. Results Our measures significantly (P < .001) correlated with the severity of the cognitive impairment and the Mini–Mental State Examination score. The classification sensitivity was 81% (area under the curve of receiver operating characteristics = 0.79) and 85% (area under the curve of receiver operating characteristics = 0.76) between HCs and AD and between HCs and AD and mild cognitively impaired, respectively. Discussion An automated assessment of a picture description task could assist clinicians in the detection of early signs of cognitive impairment and AD.
|
|---|
