Computer-based evaluation of Alzheimer's disease and mild cognitive impairment patients during a picture description task.

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.