Automated Analysis of Drawing Process for Detecting Prodromal and Clinical Dementia

• URL: http://arxiv.org/abs/2211.08685v1
• Date: Wed, 16 Nov 2022 05:38:52 GMT
• Title: Automated Analysis of Drawing Process for Detecting Prodromal and Clinical Dementia
• Authors: Yasunori Yamada, Masatomo Kobayashi, Kaoru Shinkawa, Miyuki Nemoto, Miho Ota, Kiyotaka Nemoto, Tetsuaki Arai
• Abstract summary: Automated analysis of the drawing process has been studied as a promising means for screening prodromal and clinical dementia. 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. Our findings suggest that automated analysis of the drawing process can provide information about cognitive impairments and neuropathological changes due to dementia.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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