ArticleSensors (Basel, Switzerland)2020
Automatic, Qualitative Scoring of the Interlocking Pentagon Drawing Test (PDT) based on U-Net and Mobile Sensor Data.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 18 citations in OpenAlex.
- The Potential of Automated Assessment of Cognitive Function Using Non-Neuroimaging Data: A Systematic Review.Journal of clinical medicine · 2024Review
- Digital quantification of the MMSE interlocking pentagon areas: a three-stage algorithm.Scientific reports · 2024Article
- Automating Rey Complex Figure Test scoring using a deep learning-based approach: a potential large-scale screening tool for cognitive decline.Alzheimer's research & therapy · 2023Article
- Explainable deep learning approach for extracting cognitive features from hand-drawn images of intersecting pentagons.NPJ digital medicine · 2023Article
- The current state of artificial intelligence-augmented digitized neurocognitive screening test.Frontiers in human neuroscience · 2023Review
- Deep convolutional neural networks for automated scoring of pentagon copying test results.Scientific reports · 2022Article
- Automatic, Qualitative Scoring of the Clock Drawing Test (CDT) Based on U-Net, CNN and Mobile Sensor Data.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
We implemented a mobile phone application of the pentagon drawing test (PDT), called mPDT, with a novel, automatic, and qualitative scoring method for the application based on U-Net (a convolutional network for biomedical image segmentation) coupled with mobile sensor data obtained with the mPDT. For the scoring protocol, the U-Net was trained with 199 PDT hand-drawn images of 512ⅹ512 resolution obtained via the mPDT in order to generate a trained model, Deep5, for segmenting a drawn right or left pentagon. The U-Net was also trained with 199 images of 512ⅹ512 resolution to attain the trained model, DeepLock, for segmenting an interlocking figure. Here, the epochs were iterated until the accuracy was greater than 98% and saturated. The mobile senor data primarily consisted of x and y coordinates, timestamps, and touch-events of all the samples with a 20 ms sampling period. The velocities were then calculated using the primary sensor data. With Deep5, DeepLock, and the sensor data, four parameters were extracted. These included the number of angles (0-4 points), distance/intersection between the two drawn figures (0-4 points), closure/opening of the drawn figure contours (0-2 points), and tremors detected (0-1 points). The parameters gave a scaling of 11 points in total. The performance evaluation for the mPDT included 230 images from subjects and their associated sensor data. The results of the performance test indicated, respectively, a sensitivity, specificity, accuracy, and precision of 97.53%, 92.62%, 94.35%, and 87.78% for the number of angles parameter; 93.10%, 97.90%, 96.09%, and 96.43% for the distance/intersection parameter; 94.03%, 90.63%, 92.61%, and 93.33% for the closure/opening parameter; and 100.00%, 100.00%, 100.00%, and 100.00% for the detected tremor parameter. These results suggest that the mPDT is very robust in differentiating dementia disease subtypes and is able to contribute to clinical practice and field studies.
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