scholarly journals Artificial intelligence-assisted analysis of endoscopic retrograde cholangiopancreatography image for identifying ampulla and difficulty of selective cannulation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Taesung Kim ◽  
Jinhee Kim ◽  
Hyuk Soon Choi ◽  
Eun Sun Kim ◽  
Bora Keum ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Clinical Significance ◽  
Endoscopic Retrograde Cholangiopancreatography ◽  
Endoscopic Retrograde ◽  
Centroid Distance ◽  
Selective Cannulation ◽  
Assisted Analysis ◽  
Difficult Cases

AbstractThe advancement of artificial intelligence (AI) has facilitated its application in medical fields. However, there has been little research for AI-assisted endoscopy, despite the clinical significance of the efficiency and safety of cannulation in the endoscopic retrograde cholangiopancreatography (ERCP). In this study, we aim to assist endoscopists performing ERCP through automatic detection of the ampulla and the identification of cannulation difficulty. We developed a novel AI-assisted system based on convolutional neural networks that predict the location of the ampulla and the difficulty of cannulation to the ampulla. ERCP data of 531 and 451 patients were utilized in the evaluation of our model for each task. Our model detected the ampulla with mean intersection-over-union 64.1%, precision 76.2%, recall 78.4%, and centroid distance 0.021. In classifying the cannulation difficulty, it achieved the recall of 71.9% for the class of easy cases and that of 61.1% for that of difficult cases. Remarkably, our model accurately detected AOV with varying morphological shape, size, and texture on par with the level of a human expert and showed promising results for recognizing cannulation difficulty. It demonstrated its potential to improve the quality of ERCP by assisting endoscopists.

scholarly journals Demand Modelling in Telecommunications

10.14311/1121 ◽  
2009 ◽  
Vol 49 (2) ◽  
Author(s):  
M. Chvalina
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Network Models ◽  
Short Term ◽  
Neural Network Models ◽  
Term Forecast ◽  
Artificial Intelligence Methods ◽  
Demand Modelling

This article analyses the existing possibilities for using Standard Statistical Methods and Artificial Intelligence Methods for a short-term forecast and simulation of demand in the field of telecommunications. The most widespread methods are based on Time Series Analysis. Nowadays, approaches based on Artificial Intelligence Methods, including Neural Networks, are booming. Separate approaches will be used in the study of Demand Modelling in Telecommunications, and the results of these models will be compared with actual guaranteed values. Then we will examine the quality of Neural Network models. 

scholarly journals Archetypal Use of Artificial Intelligence for Bridge Structural Monitoring

2020 ◽  
Vol 10 (20) ◽  
pp. 7157
Author(s):  
Bernardino Chiaia ◽  
Valerio De Biagi
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Damage Detection ◽  
Working Life ◽  
Research Topic ◽  
Structural Monitoring ◽  
The Sixties ◽  
Relevant Role ◽  
Measurement Devices

Structural monitoring is a research topic that is receiving more and more attention, especially in light of the fact that a large part our infrastructural heritage was built in the Sixties and is aging and approaching the end of its design working life. The detection of damage is usually performed through artificial intelligence techniques. In contrast, tools for the localization and the estimation of the extent of the damage are limited, mainly due to the complete datasets of damages needed for training the system. The proposed approach consists in numerically generating datasets of damaged structures on the basis of random variables representing the actions and the possible damages. Neural networks were trained to perform the main structural monitoring tasks: damage detection, localization, and estimation. The artificial intelligence tool interpreted the measurements on a real structure. To simulate real measurements more accurately, noise was added to the synthetic dataset. The results indicate that the accuracy of the measurement devices plays a relevant role in the quality of the monitoring.

Empirical Inference of Numerical Information into Causal Strategy Models by Means of Artificial Intelligence

2011 ◽  
pp. 290-313
Author(s):  
Christian Hillbrand
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Time Series ◽  
Forecasting Model ◽  
Empirical Validation ◽  
Functional Dependencies ◽  
Cause And Effect ◽  
Decision Supporting ◽  
Approximate Simulation

The motivation for this chapter is the observation that many companies build their strategy upon poorly validated hypotheses about cause and effect of certain business variables. However, the soundness of these cause-and-effect-relations as well as the knowledge of the approximate shape of the functional dependencies underlying these associations turns out to be the biggest issue for the quality of the results of decision supporting procedures. Since it is sufficiently clear that mere correlation of time series is not suitable to prove the causality of two business concepts, there seems to be a rather dogmatic perception of the inadmissibility of empirical validation mechanisms for causal models within the field of strategic management as well as management science. However, one can find proven causality techniques in other sciences like econometrics, mechanics, neuroscience, or philosophy. Therefore this chapter presents an approach which applies a combination of well-established statistical causal proofing methods to strategy models in order to validate them. These validated causal strategy models are then used as the basis for approximating the functional form of causal dependencies by the means of Artificial Neural Networks. This in turn can be employed to build an approximate simulation or forecasting model of the strategic system.

Research of the Reliability of the Electrical Supply System of Airports and Aerodromes Using Neural Networks

2020 ◽  
pp. 279-305
Author(s):  
Serhii Mykolaiovych Boiko ◽  
Yurii Shmelev ◽  
Viktoriia Chorna ◽  
Marina Nozhnova
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
High Precision ◽  
Supply System ◽  
Electricity Supply ◽  
Diagnostic Data ◽  
Number Of Factors ◽  
Standard Situation ◽  
Electrical Supply

The system of supplying airports and airfields is subject to high requirements for the degree of reliability. This is due to the existence of a large number of factors that affect the work of airports and airfields. In this regard, the control systems for these complexes must, as soon as possible, adopt the most optimal criteria for the reliability and quality of the solution. This complicates the structure of the electricity supply complex quite a lot and necessitates the use of modern, reliable, and high-precision technologies for the management of these complexes. One of them is artificial intelligence, which allows you to make decisions in a non-standard situation, to give recommendations to the operator to perform actions based on analysis of diagnostic data.

scholarly journals Utility of the Anterior Oblique-Viewing Endoscope and the Double-Balloon Enteroscope for Endoscopic Retrograde Cholangiopancreatography in Patients with Billroth II Gastrectomy

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Manabu Sen-yo ◽  
Seiji Kaino ◽  
Shigeyuki Suenaga ◽  
Toshiyuki Uekitani ◽  
Kanako Yoshida ◽  
...  
Keyword(s):  
Success Rate ◽  
Endoscopic Retrograde Cholangiopancreatography ◽  
Control Group ◽  
Endoscopic Retrograde ◽  
Billroth Ii ◽  
Double Balloon ◽  
Billroth Ii Gastrectomy ◽  
Selective Cannulation ◽  
Historical Controls

Background/Purpose. The difficulties of endoscopic retrograde cholangiopancreatography in patients with Billroth II gastrectomy have been reported. We evaluated the usefulness of an anterior oblique-viewing endoscope and a double-balloon enteroscope for endoscopic retrograde cholangiopancreatography in such patients.Methods. From January 2003 to December 2011, 65 patients with Billroth II gastrectomy were enrolled in this study. An anterior oblique-viewing endoscope was used for all patients. From February 2007, a double-balloon enteroscope was used for the failed cases. The success rate of procedures was compared with those in 20 patients with Billroth II gastrectomy using forward-viewing endoscope or side-viewing endoscope from March 1996 to July 2002 as historical controls.Results. In all patients in whom the papilla was reached (60/65), selective cannulation was achieved. The success rate of selective cannulation and accomplishment of planned procedures in the anterior oblique-viewing endoscope group were both significantly higher than that in the control group (100% versus 70.1%, 100 versus 58.8%, resp.). A double-balloon enteroscope was used in 2 patients, and the papilla could be reached and the planned procedures completed.Conclusions. An anterior oblique-viewing endoscope and double-balloon enteroscope appear to be useful in performing endoscopic retrograde cholangiopancreatography in patients with Billroth II gastrectomy.

Su1355 Peri-Diverticular Papilla: Clinical Significance in Endoscopic Retrograde Cholangiopancreatography Cannulation

2013 ◽  
Vol 77 (5) ◽  
pp. AB295
Author(s):  
Marco Alburquerque ◽  
Montserrat Figa ◽  
Lluis Vidal ◽  
Marcela Perez Contreras ◽  
J. Oriol Miquel Cusachs ◽  
...  
Keyword(s):  
Clinical Significance ◽  
Endoscopic Retrograde Cholangiopancreatography ◽  
Endoscopic Retrograde
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