scholarly journals Sub-millimeter precise photon interaction position determination in large monolithic scintillators via convolutional neural network algorithms

2021 ◽  
Author(s):  
Maria Kawula ◽  
Tim Maximilian Binder ◽  
Silvia Liprandi ◽  
Rita Viegas ◽  
Katia Parodi ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Algorithms ◽  
Photon Interaction ◽  
Position Determination ◽  
Interaction Position

Improved Nearest Neighbor Methods for Gamma Photon Interaction Position Determination in Monolithic Scintillator PET Detectors

2011 ◽  
Vol 58 (5) ◽  
pp. 2139-2147 ◽  
Author(s):  
H. T. van Dam ◽  
S. Seifert ◽  
R. Vinke ◽  
P. Dendooven ◽  
H. Lohner ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Gamma Photon ◽  
Photon Interaction ◽  
Position Determination ◽  
Pet Detectors ◽  
Interaction Position

scholarly journals Use of the Clock Drawing Test and the Rey–Osterrieth Complex Figure Test-copy with convolutional neural networks to predict cognitive impairment

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Young Chul Youn ◽  
Jung-Min Pyun ◽  
Nayoung Ryu ◽  
Min Jae Baek ◽  
Jae-Won Jang ◽  
...  
Keyword(s):  
Neural Network ◽  
Cognitive Impairment ◽  
Convolutional Neural Network ◽  
Network Algorithms ◽  
Clock Drawing Test ◽  
Clock Drawing ◽  
Complex Figure ◽  
Drawing Test ◽  
Impaired Cognition ◽  
Figure Test

Abstract Background The Clock Drawing Test (CDT) and Rey–Osterrieth Complex Figure Test (RCFT) are widely used as a part of neuropsychological test batteries to assess cognitive function. Our objective was to confirm the prediction accuracies of the RCFT-copy and CDT for cognitive impairment (CI) using convolutional neural network algorithms as a screening tool. Methods The CDT and RCFT-copy data were obtained from patients aged 60–80 years who had more than 6 years of education. In total, 747 CDT and 980 RCFT-copy figures were utilized. Convolutional neural network algorithms using TensorFlow (ver. 2.3.0) on the Colab cloud platform (www.colab.research.google.com) were used for preprocessing and modeling. We measured the prediction accuracy of each drawing test 10 times using this dataset with the following classes: normal cognition (NC) vs. mildly impaired cognition (MI), NC vs. severely impaired cognition (SI), and NC vs. CI (MI + SI). Results The accuracy of the CDT was better for differentiating MI (CDT, 78.04 ± 2.75; RCFT-copy, not being trained) and SI from NC (CDT, 91.45 ± 0.83; RCFT-copy, 90.27 ± 1.52); however, the RCFT-copy was better at predicting CI (CDT, 77.37 ± 1.77; RCFT, 83.52 ± 1.41). The accuracy for a 3-way classification (NC vs. MI vs. SI) was approximately 71% for both tests; no significant difference was found between them. Conclusions The two drawing tests showed good performance for predicting severe impairment of cognition; however, a drawing test alone is not enough to predict overall CI. There are some limitations to our study: the sample size was small, all the participants did not perform both the CDT and RCFT-copy, and only the copy condition of the RCFT was used. Algorithms involving memory performance and longitudinal changes are worth future exploration. These results may contribute to improved home-based healthcare delivery.

Sign in / Sign up

Export Citation Format

Share Document