An Artificial Intelligence-Assisted Method for Dementia Detection Using Images from the Clock Drawing Test

Article type: Research Article

Authors: Amini, Samad^a | Zhang, Lifu^a | Hao, Boran^a | Gupta, Aman^a | Song, Mengting^a | Karjadi, Cody^c | Lin, Honghuang^b | Kolachalama, Vijaya B.^{b; d; e} | Au, Rhoda^{f; c} | Paschalidis, Ioannis Ch.^{a; d; *}

Affiliations: [a] Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, MA, USA | [b] Department of Medicine, Boston University School of Medicine, Boston, MA, USA | [c] Framingham Heart Study, Boston University, Boston, MA, USA | [d] Faculty of Computing & Data Sciences, Boston University, Boston, MA, USA | [e] Department of Computer Science, Boston University, Boston, MA, USA | [f] Departments of Anatomy & Neurobiology, Neurology, and Epidemiology, Boston University School of Medicine and School of Public Health, Boston, MA, USA

Correspondence: [*] Correspondence to: Ioannis Ch. Paschalidis, Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, 8 St. Mary’s Street, Boston, MA 02215, USA. E-mail: yannisp@bu.edu.

Abstract: Background:Widespread dementia detection could increase clinical trial candidates and enable appropriate interventions. Since the Clock Drawing Test (CDT) can be potentially used for diagnosing dementia-related disorders, it can be leveraged to develop a computer-aided screening tool. Objective:To evaluate if a machine learning model that uses images from the CDT can predict mild cognitive impairment or dementia. Methods:Images of an analog clock drawn by 3,263 cognitively intact and 160 impaired subjects were collected during in-person dementia evaluations by the Framingham Heart Study. We processed the CDT images, participant’s age, and education level using a deep learning algorithm to predict dementia status. Results:When only the CDT images were used, the deep learning model predicted dementia status with an area under the receiver operating characteristic curve (AUC) of 81.3% ± 4.3%. A composite logistic regression model using age, level of education, and the predictions from the CDT-only model, yielded an average AUC and average F1 score of 91.9% ±1.1% and 94.6% ±0.4%, respectively. Conclusion:Our modeling framework establishes a proof-of-principle that deep learning can be applied on images derived from the CDT to predict dementia status. When fully validated, this approach can offer a cost-effective and easily deployable mechanism for detecting cognitive impairment.

Keywords: Alzheimer’s disease, artificial intelligence, clock test, deep learning, dementia

DOI: 10.3233/JAD-210299

Journal: Journal of Alzheimer's Disease, vol. 83, no. 2, pp. 581-589, 2021