Improving Dementia Classification with a New Clock Drawing Test Method
Author Information
Author(s): Hu Mengyao, Murphey Yi, Qin Tian, Zahodne Laura, Gonzalez Richard, Freedman Vicki
Primary Institution: UTHealth at Houston, University of Michigan-Dearborn, University of Michigan
Hypothesis
Can a deep learning neural network improve the accuracy of dementia classification using the clock-drawing test?
Conclusion
The study found that continuous scoring from a deep learning model provides more precise thresholds for dementia classification than traditional ordinal scores.
Supporting Evidence
- The clock-drawing test is a common tool for screening dementia.
- Manual coding of the clock-drawing test can lead to errors and is time-consuming.
- The deep learning model was trained on clock-drawing images to generate scores.
- Continuous scores provided better thresholds for dementia classification than ordinal scores.
- Demographic-specific thresholds were identified for different groups.
Takeaway
Researchers created a computer program to help doctors better understand if someone has dementia by looking at their clock drawings, and it works better for different types of people.
Methodology
Developed a deep learning neural network to automate clock-drawing test coding and compared its effectiveness to traditional scoring methods.
Participant Demographics
The study used a nationally representative sample of older adults, with specific thresholds identified for Black individuals, those with lower education, and those aged 90 or older.
Digital Object Identifier (DOI)
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