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| 標題: | Automated Video Analysis of Audio-Visual Approaches to Predict and Detect Mild Cognitive Impairment and Dementia in Older Adults |
| 作者: | Chu, Che-Sheng
Wang, Di-Yuan
Liang, Chih-Kuang
Chou, Ming-Yueh
Hsu, Ying-Hsin
Wang, Yu-Chun
Liao, Mei-Chen
Chu, Wei-Ta
Lin, Yu-Te |
| 貢獻者: | Kaohsiung Vet Gen Hosp, Dept Psychiat
Kaohsiung Vet Gen Hosp, Ctr Geriatr & Gerontol
Soc Psychophysiol, Noninvas Neuromodulat Consortium Mental Disorders
Kaohsiung Med Univ, Coll Med, Grad Inst Med
Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn
Natl Yang Ming Chiao Tung Univ, Ctr Hlth Longev & Aging Sci
Natl Yang Ming Chiao Tung Univ, Sch Med, Dept Geriatr Med
Kaohsiung Vet Gen Hosp, Dept Internal Med, Div Neurol
Chia Nan Univ
Natl Sun Yat Sen Univ, Coll Med, Sch Med |
| 關鍵字: | Artificial intelligence
dementia
machine learning
mild cognitive impairment
video analysis |
| 日期: | 2023 |
| 上傳時間: | 2024-12-25 11:05:27 (UTC+8) |
| 出版者: | IOS PRESS |
| 摘要: | Background: Early identification of different stages of cognitive impairment is important to provide available intervention and timely care for the elderly. Objective: This study aimed to examine the ability of the artificial intelligence (AI) technology to distinguish participants with mild cognitive impairment (MCI) from those with mild to moderate dementia based on automated video analysis. Methods: A total of 95 participants were recruited (MCI, 41; mild to moderate dementia, 54). The videos were captured during the Short Portable Mental Status Questionnaire process; the visual and aural features were extracted using these videos. Deep learning models were subsequently constructed for the binary differentiation of MCI and mild to moderate dementia. Correlation analysis of the predicted Mini-Mental State Examination, Cognitive Abilities Screening Instrument scores, and ground truth was also performed. Results: Deep learning models combining both the visual and aural features discriminated MCI from mild to moderate dementia with an area under the curve (AUC) of 77.0% and accuracy of 76.0%. The AUC and accuracy increased to 93.0% and 88.0%, respectively, when depression and anxiety were excluded. Significant moderate correlations were observed between the predicted cognitive function and ground truth, and the correlation was strong excluding depression and anxiety. Interestingly, female, but not male, exhibited a correlation. Conclusion: The study showed that video-based deep learning models can differentiate participants with MCI from those with mild to moderate dementia and can predict cognitive function. This approach may offer a cost-effective and easily applicable method for early detection of cognitive impairment. |
| 關聯: | Journal of Alzheimers Disease, v.92, n.3, pp.875-886 |
| 顯示於類別: | [藥學系(所)] 期刊論文
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