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 512 × 512 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 512 × 512 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.
Highlights
The pentagon drawing test (PDT) is a sub-test of the Mini-Mental State Examination (MMSE), used extensively in clinical and research settings as a measure of cognitive impairment [1]
A total of 230 drawing images were used to test the performance of the scoring method with the mPDT
Our study focused on implementation of the PDT as a mobile phone application, namely mPDT, with a novel, automatic, and qualitative scoring method based on U-Net, a convolutional network for biomedical image segmentation of sensor data
Summary
The pentagon drawing test (PDT) is a sub-test of the Mini-Mental State Examination (MMSE), used extensively in clinical and research settings as a measure of cognitive impairment [1]. The MMSE is a general screening tool for cognitive impairment. It shows low sensitivity for detecting cognitive impairment in Parkinson’s disease [2]. 83% of long-term survivors of Parkinson’s disease showed dementia, with impairment of their visuospatial functions and executive functions [4]. As up to half of Parkinson’s patients show visuospatial dysfunction, the PDT has been used for the distinction of dementia in Parkinson’s disease and Alzheimer’s disease cases [5,6,7,8,9,10].
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