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Automatic, Qualitative Scoring of the Clock Drawing Test (CDT) Based on U-Net, CNN and Mobile Sensor Data
- Source :
- Sensors (Basel, Switzerland), Sensors, Vol 21, Iss 5239, p 5239 (2021), Sensors, Volume 21, Issue 15
- Publication Year :
- 2021
- Publisher :
- MDPI, 2021.
-
Abstract
- The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular screening tool for cognitive functions. In spite of its qualitative capabilities in diagnosis of neurological diseases, the assessment of the CDT has depended on quantitative methods as well as manual paper based methods. Furthermore, due to the impact of the advancement of mobile smart devices imbedding several sensors and deep learning algorithms, the necessity of a standardized, qualitative, and automatic scoring system for CDT has been increased. This study presents a mobile phone application, mCDT, for the CDT and suggests a novel, automatic and qualitative scoring method using mobile sensor data and deep learning algorithms: CNN, a convolutional network, U-Net, a convolutional network for biomedical image segmentation, and the MNIST (Modified National Institute of Standards and Technology) database. To obtain DeepC, a trained model for segmenting a contour image from a hand drawn clock image, U-Net was trained with 159 CDT hand-drawn images at 128 × 128 resolution, obtained via mCDT. To construct DeepH, a trained model for segmenting the hands in a clock image, U-Net was trained with the same 159 CDT 128 × 128 resolution images. For obtaining DeepN, a trained model for classifying the digit images from a hand drawn clock image, CNN was trained with the MNIST database. Using DeepC, DeepH and DeepN with the sensor data, parameters of contour (0–3 points), numbers (0–4 points), hands (0–5 points), and the center (0–1 points) were scored for a total of 13 points. From 219 subjects, performance testing was completed with images and sensor data obtained via mCDT. For an objective performance analysis, all the images were scored and crosschecked by two clinical experts in CDT scaling. Performance test analysis derived a sensitivity, specificity, accuracy and precision for the contour parameter of 89.33, 92.68, 89.95 and 98.15%, for the hands parameter of 80.21, 95.93, 89.04 and 93.90%, for the numbers parameter of 83.87, 95.31, 87.21 and 97.74%, and for the center parameter of 98.42, 86.21, 96.80 and 97.91%, respectively. From these results, the mCDT application and its scoring system provide utility in differentiating dementia disease subtypes, being valuable in clinical practice and for studies in the field.
- Subjects :
- automatic scoring
Accuracy and precision
Computer science
02 engineering and technology
TP1-1185
wearable sensor
Neuropsychological Tests
Biochemistry
Field (computer science)
Article
clock drawing test
Analytical Chemistry
MNIST
03 medical and health sciences
0302 clinical medicine
Cognition
0202 electrical engineering, electronic engineering, information engineering
Humans
Mass Screening
Segmentation
Sensitivity (control systems)
Electrical and Electronic Engineering
Instrumentation
business.industry
Deep learning
Chemical technology
deep learning
Pattern recognition
Construct (python library)
U-Net
Atomic and Molecular Physics, and Optics
Mobile phone
Research Design
020201 artificial intelligence & image processing
Artificial intelligence
business
030217 neurology & neurosurgery
MNIST database
Algorithms
CNN
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 21
- Issue :
- 15
- Database :
- OpenAIRE
- Journal :
- Sensors (Basel, Switzerland)
- Accession number :
- edsair.doi.dedup.....fe5964fd2cd9bb0bc3caee00bb8bb694