https://orcid.org/0000-0002-9044-9009
Abstract
Alzheimer’s disease and related dementias significantly impact older adults' quality of life. The clock-drawing test (CDT) is a widely used dementia screening tool due to its ease of administration and effectiveness. However, manual CDT-coding in large-scale studies can be time-intensive and prone to coding errors and is typically limited to ordinal responses. In this study, we developed a continuous CDT score using a deep learning neural network (DLNN) and evaluated its ability to classify participants as having dementia or not.
Using a nationally representative sample of older adults from the National Health and Aging Trends Study (NHATS), we trained deep learning models on CDT images to generate both ordinal and continuous scores. Using a modified NHATS dementia classification algorithm as a benchmark, we computed the Area Under the Receiver Operating Characteristic Curve for each scoring approach. Thresholds were determined by balancing sensitivity and specificity, and demographic-specific thresholds were compared to a uniform threshold for classification accuracy.
Continuous CDT scores provided more granular thresholds than ordinal scores for dementia classification, which vary by demographic characteristics. Lower thresholds were identified for Black individuals, those with lower education, and those ages 90 or older. Compared to ordinal scores, continuous scores also allowed for a more balanced sensitivity and specificity.
This study demonstrates the potential of continuous CDT generated by DLNN to enhance dementia classification. By identifying demographic-specific thresholds, it offers a more inclusive and adaptive approach, which could lead to improved guidelines for using CDT in dementia screening.
Accepted manuscripts
