scholarly journals Identification of cecum time-location in a colonoscopy video by deep learning analysis of colonoscope movement

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7256
Author(s):  
Minwoo Cho ◽  
Jee Hyun Kim ◽  
Kyoung Sup Hong ◽  
Joo Sung Kim ◽  
Hyoun-Joong Kong ◽  
...  

Background Cecal intubation time is an important component for quality colonoscopy. Cecum is the turning point that determines the insertion and withdrawal phase of the colonoscope. For this reason, obtaining information related with location of the cecum in the endoscopic procedure is very useful. Also, it is necessary to detect the direction of colonoscope’s movement and time-location of the cecum. Methods In order to analysis the direction of scope’s movement, the Horn–Schunck algorithm was used to compute the pixel’s motion change between consecutive frames. Horn–Schunk-algorithm applied images were trained and tested through convolutional neural network deep learning methods, and classified to the insertion, withdrawal and stop movements. Based on the scope’s movement, the graph was drawn with a value of +1 for insertion, −1 for withdrawal, and 0 for stop. We regarded the turning point as a cecum candidate point when the total graph area sum in a certain section recorded the lowest. Results A total of 328,927 frame images were obtained from 112 patients. The overall accuracy, drawn from 5-fold cross-validation, was 95.6%. When the value of “t” was 30 s, accuracy of cecum discovery was 96.7%. In order to increase visibility, the movement of the scope was added to summary report of colonoscopy video. Insertion, withdrawal, and stop movements were mapped to each color and expressed with various scale. As the scale increased, the distinction between the insertion phase and the withdrawal phase became clearer. Conclusion Information obtained in this study can be utilized as metadata for proficiency assessment. Since insertion and withdrawal are technically different movements, data of scope’s movement and phase can be quantified and utilized to express pattern unique to the colonoscopist and to assess proficiency. Also, we hope that the findings of this study can contribute to the informatics field of medical records so that medical charts can be transmitted graphically and effectively in the field of colonoscopy.

2020 ◽  
Author(s):  
Wei Zhang ◽  
Zixing Huang ◽  
Jian Zhao ◽  
Du He ◽  
Mou Li ◽  
...  

2021 ◽  
Author(s):  
Huozhu Wang ◽  
Ziyuan Zhu ◽  
Zhongkai Tong ◽  
Xiang Yin ◽  
Yusi Feng ◽  
...  

2021 ◽  
Author(s):  
Francesca Lizzi ◽  
Francesca Brero ◽  
Raffaella Cabini ◽  
Maria Fantacci ◽  
Stefano Piffer ◽  
...  

2020 ◽  
Vol 35 (21) ◽  
pp. 2050119
Author(s):  
Lev Dudko ◽  
Georgi Vorotnikov ◽  
Petr Volkov ◽  
Maxim Perfilov ◽  
Andrei Chernoded ◽  
...  

Deep learning neural network (DNN) technique is one of the most efficient and general approach of multivariate data analysis of the collider experiments. The important step of the analysis is the optimization of the input space for multivariate technique. In the paper we propose the general recipe how to form the set of low-level observables sensitive to the differences in hard scattering processes at the colliders. It is shown in the paper that without any sophisticated analysis of the kinematic properties one can achieve close to optimal performance of DNN with the proposed general set of low-level observables.


2021 ◽  
Vol 17 (2) ◽  
pp. e1008767
Author(s):  
Zutan Li ◽  
Hangjin Jiang ◽  
Lingpeng Kong ◽  
Yuanyuan Chen ◽  
Kun Lang ◽  
...  

N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA’s biological functions. However, the existing experimental techniques for detecting 6mA sites are cost-ineffective, which implies the great need of developing new computational methods for this problem. In this paper, we developed, without requiring any prior knowledge of 6mA and manually crafted sequence features, a deep learning framework named Deep6mA to identify DNA 6mA sites, and its performance is superior to other DNA 6mA prediction tools. Specifically, the 5-fold cross-validation on a benchmark dataset of rice gives the sensitivity and specificity of Deep6mA as 92.96% and 95.06%, respectively, and the overall prediction accuracy is 94%. Importantly, we find that the sequences with 6mA sites share similar patterns across different species. The model trained with rice data predicts well the 6mA sites of other three species: Arabidopsis thaliana, Fragaria vesca and Rosa chinensis with a prediction accuracy over 90%. In addition, we find that (1) 6mA tends to occur at GAGG motifs, which means the sequence near the 6mA site may be conservative; (2) 6mA is enriched in the TATA box of the promoter, which may be the main source of its regulating downstream gene expression.


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