Digital Drawing Analysis for Cognitive Dysfunction Screening
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Solution Overview
Problem
Current screening methods for cognitive dysfunctions like dementia are often subjective, require specialized equipment, and are not suitable for illiterate or poorly educated individuals, leading to potential misdiagnosis and limited early detection.
Innovation Solution
A method using a pre-trained Naïve Bayes model to analyze drawing data from digital devices, converting motion and geometric features to predict the presence, progression, or treatment effects of dementia, which includes obtaining drawing data, reconstructing images, and determining probability based on personal and drawing features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional clinical screening tests are conducted in face-to-face interviews requiring participants to visit healthcare professionals, then the screening can be performed with professional guidance, but the accessibility and convenience for participants are reduced
Solution Approach 1:
The patent uses digital copies of drawing tests that can be administered remotely through electronic devices. The drawing test is digitized and can be completed by participants at home, with their drawings scanned or photographed and submitted for professional evaluation, thus maintaining screening accuracy while improving accessibility
Solution Approach 2:
The patent introduces digital technology as an intermediary between participants and healthcare professionals. Digital devices and communication platforms serve as mediators that enable remote administration and submission of drawing tests, allowing participants to complete screening without visiting clinics while still receiving professional evaluation
2Measurement precision
If drawing tests are administered digitally with precise tracking of drawing behavior, then the measurement precision and objectivity are improved, but the device complexity and cost increase
Solution Approach 1:
The patent utilizes common digital devices such as smartphones, tablets, or computers that participants already possess. These multi-functional devices can display the drawing test, track drawing behavior through standard touch or mouse inputs, and transmit results without requiring specialized equipment, thus improving measurement precision while avoiding increased device complexity
Solution Approach 2:
The digital drawing test system automatically tracks drawing behavior parameters such as stroke order, pressure, speed, and pauses without requiring additional sensors or complex instrumentation. The device's existing capabilities are leveraged to collect precise data, eliminating the need for specialized tracking equipment
3Measurement precision
If the drawing test requires participants to copy complex figures like clocks, then the cognitive assessment capability is improved, but the ease of operation for illiterate or poorly educated individuals is reduced
Solution Approach 1:
The patent offers multiple versions of the drawing test with varying levels of complexity. For participants with lower education levels or literacy, simpler geometric shapes or patterns are provided instead of complex clocks, while still maintaining the ability to assess cognitive functions such as spatial reasoning and attention through the drawing behavior analysis
Data Source
AI summary
Methods for screening, diagnosing, or predicting presence, progression, or treatment effects of a cognitive dysfunction such as dementia based on an analysis of drawing behavior changes by a pre-trained Naïve Bayes method are provided. The methods include steps of obtaining drawing data of at least one image created by a test subject on a digital device and obtaining personal data of the test subject; reconstructing the at least one image based on the drawing data obtained; converting the drawing data to drawing features comprising a plurality of motion features and a plurality of geometric features; and determining probability that the test subject has a cognitive dysfunction based on the drawing features and the personal data by a pre-trained Naïve Bayes method with a greedy variable selection.