EVALUATION OF A REAL-TIME ARTIFICIAL INTELLIGENCE SYSTEM USING A DEEP NEURAL NETWORK FOR POLYP DETECTION AND LOCALIZATION IN THE LOWER GASTROINTESTINAL TRACT

This study investigated whether parameters derived from hand motions of expert and novice surgeons accurately and objectively reflect laparoscopic surgical skill levels using an artificial intelligence system consisting of a three-layer chaos neural network. Sixty-seven surgeons (23 experts and 44 novices) performed a laparoscopic skill assessment task while their hand motions were recorded using a magnetic tracking sensor. Eight parameters evaluated as measures of skill in a previous study were used as inputs to the neural network. Optimization of the neural network was achieved after seven trials with a training dataset of 38 surgeons, with a correct judgment ratio of 0.99. The neural network that prospectively worked with the remaining 29 surgeons had a correct judgment rate of 79% for distinguishing between expert and novice surgeons. In conclusion, our artificial intelligence system distinguished between expert and novice surgeons among surgeons with unknown skill levels.

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Features of Signature Verification By a Person as a Primary Criteria for Developing an Artificial Intelligence System

Siberian Law Review ◽

10.19073/2658-7602-2020-17-4-514-522 ◽

2020 ◽

Vol 17 (4) ◽

pp. 514-522

Author(s):

D. V. Bakhteev

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Artificial Neural Network ◽

Artificial Intelligence System ◽

Experimental Conditions ◽

Intelligence System ◽

Initial Stage ◽

Correct Determination ◽

Artificial Neural ◽

Applied Artificial Intelligence

The modern capabilities of computers have returned interest in artificial intelligence technologies. A particular area of application of these technologies is pattern recognition, which can be applied to the traditional forensic task – identification of signs of forgery (imitation) of a signature. The results of forgery are differentiated into three types: auto-forgery, simple and skilled forgeries. Only skilled forgeries are considered in this study. The online and offline approaches to the study of signatures and other handwriting material are described. The developed artificial intelligence system based on an artificial neural network refers to the offline type of signature recognition – that is, it is focused on working exclusively with the consequences of the signature – its graphic image. The content and principles of the formation of a hypothesis for the development of an artificial intelligence system are described with a combination of humanitarian (legal) knowledge and natural-technical knowledge. At the initial stage of the study, in order to develop an experimental-applied artificial intelligence system based on an artificial neural network focused on identifying forged signatures, 127 people were questioned in order to identify a person's ability to detect fake signatures. It was found that under experimental conditions the probability of a correct determination of the originality or forgery of the presented signature for the respondent is on average 69.29 %. Accordingly, this value can be used as a threshold for determining the effectiveness of the developed artificial intelligence system. In the process of preparing the dataset (an array for training and verification of its results) of the system in terms of fraudulent signatures, some forensically significant features were revealed, associated with the psychological and anatomical features of the person performing the forgery, both known to criminalistics and new ones. It is emphasized that the joint development of artificial intelligence systems by the methods of computer science and criminalistics can generate additional results that may be useful outside the scope of the research tasks.

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New artificial intelligence system: first validation study versus experienced endoscopists for colorectal polyp detection

Gut ◽

10.1136/gutjnl-2019-319914 ◽

2019 ◽

Vol 69 (5) ◽

pp. 799-800 ◽

Cited By ~ 23

Author(s):

Cesare Hassan ◽

Michael B Wallace ◽

Prateek Sharma ◽

Roberta Maselli ◽

Vincenzo Craviotto ◽

...

Keyword(s):

Artificial Intelligence ◽

Validation Study ◽

Colorectal Polyp ◽

Polyp Detection ◽

Artificial Intelligence System ◽

Intelligence System ◽

Experienced Endoscopists

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An Artificial Intelligence System to Help the Player of Real-Time Strategy Games

2010 Brazilian Symposium on Games and Digital Entertainment ◽

10.1109/sbgames.2010.23 ◽

2010 ◽

Cited By ~ 3

Author(s):

Renato Luiz de Freitas Cunha ◽

Luiz Chaimowicz

Keyword(s):

Artificial Intelligence ◽

Real Time ◽

Artificial Intelligence System ◽

Intelligence System ◽

Strategy Games

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Using Artificial Intelligence System for Pattern Identification Based on Features Extracted from Gel Electrophoresis Images

International Journal of Research in Pharmaceutical Sciences ◽

10.26452/ijrps.v11i2.2115 ◽

2020 ◽

Vol 11 (2) ◽

pp. 1944-1952

Author(s):

Sarmad M. Hadi ◽

Al-Faiz M Z ◽

Ali A. Ibrahim

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Gel Electrophoresis ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Artificial Intelligence System ◽

Average Standard Deviation ◽

Learning Time ◽

Intelligence System ◽

Error Back Propagation

Artificial intelligence has many branches of image processing-based applications in terms of classification and identification, error back-propagation neural network is a great match for such applications as long as linear vector quantization (LVQ) and pattern recognition is another great match for recognition of digital images based on their features. The dataset used in this paper are gel electrophoresis images where 6 features had been extracted from the images and used as input to a neural network for learning and then checked for recognition purposed and the system managed to recognize all the 6 images. Six features had been used: average, standard deviation, smoothness, skewness, uniformity, and entropy. A tiny error rate where allowed in the recognition program to cover the variation of the dataset and the test data (gel-electrophoresis images). The proposed system had successfully managed to identify all of the learned data in both LVQ and error-back-propagation. Error-back-propagation proved itself as a great tool in terms of learning time compared with LVQ, which was very slow in terms of learning time and recognition.

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