2022 IEEE International Conference on Digital Health (ICDH)
Early diagnosis of dementia, particularly in the prodromal stage (i.e., mild
cognitive impairment, or MCI), has become a research and clinical priority but
remains challenging. Automated analysis of the drawing process has been studied
as a promising means for screening prodromal and clinical dementia, providing
multifaceted information encompassing features, such as drawing speed, pen
posture, writing pressure, and pauses. We examined the feasibility of using
these features not only for detecting prodromal and clinical dementia but also
for predicting the severity of cognitive impairments assessed using Mini-Mental
State Examination (MMSE) as well as the severity of neuropathological changes
assessed by medial temporal lobe (MTL) atrophy. We collected drawing data with
a digitizing tablet and pen from 145 older adults of cognitively normal (CN),
MCI, and dementia. The nested cross-validation results indicate that the
combination of drawing features could be used to classify CN, MCI, and dementia
with an AUC of 0.909 and 75.1% accuracy (CN vs. MCI: 82.4% accuracy; CN vs.
dementia: 92.2% accuracy; MCI vs. dementia: 80.3% accuracy) and predict MMSE
scores with an $R^2$ of 0.491 and severity of MTL atrophy with an $R^2$ of
0.293. Our findings suggest that automated analysis of the drawing process can
provide information about cognitive impairments and neuropathological changes
due to dementia, which can help identify prodromal and clinical dementia as a
digital biomarker.
Yasunori Yamada, Masatomo Kobayashi, Kaoru Shinkawa, Miyuki Nemoto, Miho Ota, Kiyotaka Nemoto, Tetsuaki Arai
2022-11-16
