scholarly journals Utility of an Artificial Intelligence System for Classification of Esophageal Lesions when Simulating its Clinical use

2021 ◽  
Author(s):  
Ayaka Tajiri ◽  
Ryu ISHIHARA ◽  
Yusuke KATO ◽  
Takahiro INOUE ◽  
Katsunori MATSUEDA ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Narrow Band Imaging ◽  
Validation Dataset ◽  
Muscularis Mucosa ◽  
Still Images ◽  
Still Image ◽  
Intelligence System ◽  
System Diagnosis ◽  
Diagnostic Support

Abstract Background:Previous reports have shown favorable performance of artificial intelligence (AI) systems for diagnosing esophageal squamous cell carcinoma (ESCC) compared with endoscopists. However, these findings don’t reflect performance in clinical situations, as endoscopists classify lesions based on both magnified and non-magnified videos, while AI systems often use only a few magnified narrow band imaging (NBI) still images. We evaluated the performance of the AI system in simulated clinical situations.Methods:We used 25,048 images from 1,433 superficial ESCC and 4,746 images from 410 noncancerous esophagi to construct our AI system. For the validation dataset, we took NBI videos of suspected superficial ESCCs. The AI system diagnosis used one magnified still image taken from each video, while 19 endoscopists used whole videos. Results:We used 147 datasets including 83 superficial ESCC and 64 non-ESCC lesions. The accuracy, sensitivity and specificity for the classification of ESCC were, respectively, 80.9%, 85.5%, and 75.0% for the AI system and 69.2%, 67.5%, and 71.5% for the endoscopists. The AI system correctly classified all ESCCs invading the muscularis mucosa or submucosa and 96.8% of lesions ≥ 20 mm, whereas even the experts misdiagnosed some of them.Conclusions:Our AI system showed higher diagnostic ability for classifying ESCC and non-ESCC than endoscopists. It may provide valuable diagnostic support to endoscopists.

Artificial intelligence diagnostic system predicts multiple Lugol-voiding lesions in the esophagus and patients at high risk for esophageal squamous cell carcinoma

Endoscopy ◽  
2021 ◽  
Author(s):  
Yohei Ikenoyama ◽  
Toshiyuki Yoshio ◽  
Junki Tokura ◽  
Sakiko Naito ◽  
Ken Namikawa ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Esophageal Cancer ◽  
High Risk ◽  
Narrow Band Imaging ◽  
Diagnostic System ◽  
High Sensitivity ◽  
Endoscopic Examination ◽  
Validation Dataset ◽  
Iodine Staining ◽  
Experienced Endoscopists

Abstract Background It is known that an esophagus with multiple Lugol-voiding lesions (LVLs) after iodine staining is high risk for esophageal cancer; however, it is preferable to identify high-risk cases without staining because iodine causes discomfort and prolongs examination times. This study assessed the capability of an artificial intelligence (AI) system to predict multiple LVLs from images that had not been stained with iodine as well as patients at high risk for esophageal cancer. Methods We constructed the AI system by preparing a training set of 6634 images from white-light and narrow-band imaging in 595 patients before they underwent endoscopic examination with iodine staining. Diagnostic performance was evaluated on an independent validation dataset (667 images from 72 patients) and compared with that of 10 experienced endoscopists. Results The sensitivity, specificity, and accuracy of the AI system to predict multiple LVLs were 84.4 %, 70.0 %, and 76.4 %, respectively, compared with 46.9 %, 77.5 %, and 63.9 %, respectively, for the endoscopists. The AI system had significantly higher sensitivity than 9/10 experienced endoscopists. We also identified six endoscopic findings that were significantly more frequent in patients with multiple LVLs; however, the AI system had greater sensitivity than these findings for the prediction of multiple LVLs. Moreover, patients with AI-predicted multiple LVLs had significantly more cancers in the esophagus and head and neck than patients without predicted multiple LVLs. Conclusion The AI system could predict multiple LVLs with high sensitivity from images without iodine staining. The system could enable endoscopists to apply iodine staining more judiciously.

Artificial intelligence system based on the layout effect of both sides in volleyball matches

2020 ◽  
pp. 1-10
Author(s):  
Kai Zhao ◽  
Wei Jiang ◽  
Xinlong Jin ◽  
Xuming Xiao
Keyword(s):  
Artificial Intelligence ◽  
Moving Objects ◽  
Support Vector ◽  
Moving Target ◽  
Artificial Intelligence System ◽  
Fast Detection ◽  
Intelligence System ◽  
Detection And Tracking ◽  
Traditional Sports

The traditional sports match analysis mostly adopts the method of manual observation and recording, which is not only time-consuming and laborious but also has the defects of subjectivity and inaccuracy in the judgment results, resulting in the deviation of the match data analysis and statistical results. The purpose of this paper is to study an artificial intelligence system that can automatically analyze and evaluate the effect of both sides in volleyball matches. In this paper, the system is divided into two steps: detection and tracking of moving objects, recognition, and classification of players’ behaviors and movements. About moving target detection and tracking, this paper proposes a moving target fast detection framework based on a mixture of mainstream technologies and a MeanShift target tracking method based on Kalman filtering and adaptive target region size. For behavior and action recognition and classification, this paper proposes a classifier combining BP neural network and support vector machine. Experimental results show that the proposed algorithm and classifier are effective. By analyzing the performance of the proposed classifier, the classification accuracy is 98%.

scholarly journals Artificial intelligence system for the analysis of theoretical and experimental current-voltage characteristics

2021 ◽  
Vol 2131 (2) ◽  
pp. 022089
Author(s):  
I V Gudza ◽  
A V Kovalenko ◽  
M Kh Urtenov ◽  
A V Pismenskiy
Keyword(s):  
Artificial Intelligence ◽  
Membrane Separation ◽  
Fundamental Problem ◽  
Intelligence System ◽  
Current Voltage ◽  
Reverse Osmosis Plant ◽  
Seawater Reverse Osmosis ◽  
Current Voltage Characteristics ◽  
Artificial Intelligence Systems

Abstract Digitalization is one of the development priorities in the global scientific community. Digitalization at this point in time means the introduction of artificial intelligence systems in production, science, economics, management, etc. Artificial intelligence systems have shown their effectiveness in various fields of science and technology, including electrochemistry for such tasks as modeling membrane separation, controlling the variable operation of a simple seawater reverse osmosis plant, etc., however, the study and prediction of theoretical and experimental current-voltage characteristics of electromembrane systems by machine learning and artificial intelligence methods is still practically not carried out. The problem of finding connections between the regularities of changes in the current-voltage characteristics (CVC) and the regularities of the process of salt ion transfer in the electrodialysis apparatus (EDA) desalination channel, the classification of theoretical and experimental CVC depending on the values of the input parameters has not yet been set and investigated, and therefore is a new fundamental problem and an actual practical problem.

scholarly journals Classifying AI Systems

2021 ◽  
Author(s):  
Catherine Aiken ◽  
Keyword(s):  
Artificial Intelligence ◽  
Risk Assessment ◽  
System Management ◽  
Artificial Intelligence System ◽  
Intelligence System

This brief explores the development and testing of artificial intelligence system classification frameworks intended to distill AI systems into concise, comparable and policy-relevant dimensions. Comparing more than 1,800 system classifications, it points to several factors that increase the utility of a framework for human classification of AI systems and enable AI system management, risk assessment and governance.

scholarly journals Concept to recognize crisis of organizational and institutional separation by Artificial Intelligence System

2021 ◽  
Vol 10 (43) ◽  
pp. 59-71
Author(s):  
Oleg N. Dmitriev ◽  
Veronika A. Zolotova
Keyword(s):  
Artificial Intelligence ◽  
Crisis Management ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Critical Management ◽  
High Level ◽  
Post Industrial ◽  
Typological Classification ◽  
Crisis Situations

The sphere of anti-crisis management is highlighted in relation to the open variety of organizational and institutional separations that are typical for the higher forms of industrial and post-industrial economies. This article shows the typicity and relevance of critical management situations associated with the emergence of crises. Furthermore, it justifies the objective orientation to a dense (not sparse) stream of crisis situations requiring identification, ranking, and classification. A strict management interpretation of the separation crisis is given through an assessment of the nature of the dynamics of the separation state indexes. Also, the document presents a generalized typological classification of crises. This article shows the necessity of using a high-level Artificial Intelligence System for this purpose, an indispensable component of which is the classification component.

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