scholarly journals Identification of a small, depressed type of colorectal invasive cancer by an artificial intelligence-assisted detection system

Endoscopy ◽  
2021 ◽  
Author(s):  
Shin-ei Kudo ◽  
Masashi Misawa ◽  
Yuichi Mori ◽  
Yurie Kawabata ◽  
Yasuharu Maeda ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Detection System ◽  
Invasive Cancer ◽  
Depressed Type

scholarly journals Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study

2020 ◽  
Author(s):  
Yuchen Luo ◽  
Yi Zhang ◽  
Ming Liu ◽  
Yihong Lai ◽  
Panpan Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
High Performance ◽  
Detection System ◽  
Random Order ◽  
Colon Polyps ◽  
Clinical Environment ◽  
Polyp Detection ◽  
Link Type ◽  
Polyp Detection Rate

Abstract Background and aims Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. Methods The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov. (NCT047126265). Results In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions. Conclusions A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. Trial Registration clinicaltrials.gov Identifier: NCT047126265

scholarly journals Automated Detection of Laryngeal Carcinoma in Laryngoscopic Images from a Multicenter Database using a Convolutional Neural Network

2021 ◽  
Author(s):  
Peikai Yan ◽  
Shaohua Li ◽  
Zhou Zhou ◽  
Qian Liu ◽  
Jiahui Wu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Laryngeal Carcinoma ◽  
Detection System ◽  
Tertiary Care ◽  
Diagnostic System ◽  
Intelligence System ◽  
Testing Dataset ◽  
Malignant Group ◽  
Automatic Detection System ◽  
Tertiary Care Centers

OBJECTIVE Little is known about the efficacy of using artificial intelligence to identify laryngeal carcinoma from images of vocal lesions taken in different hospitals with multiple laryngoscope systems. This multicenter study was aimed to establish an artificial intelligence system and provide a reliable auxiliary tool to screen for laryngeal carcinoma. Study Design: Multicentre case-control study Setting: Six tertiary care centers Participants: The laryngoscopy images were collected from 2179 patients with vocal lesions. Outcome Measures: An automatic detection system of laryngeal carcinoma was established based on Faster R-CNN, which was used to distinguish vocal malignant and benign lesions in 2179 laryngoscopy images acquired from 6 hospitals with 5 types of laryngoscopy systems. Pathology was the gold standard to identify malignant and benign vocal lesions. Results: Among 89 cases of the malignant group, the classifier was able to evaluate the laryngeal carcinoma in 66 patients (74.16%, sensitivity), while the classifier was able to assess the benign laryngeal lesion in 503 cases among 640 cases of the benign group (78.59%, specificity). Furthermore, the CNN-based classifier achieved an overall accuracy of 78.05% with a 95.63% negative prediction for the testing dataset. Conclusion: This automatic diagnostic system has the potential to assist clinical laryngeal carcinoma diagnosis, which may improve and standardize the diagnostic capacity of endoscopists using different laryngoscopes.

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

scholarly journals AUTOMATED DETECTION SYSTEM FOR LUNG MASSES AS A STAGE OF ARTIFICIAL INTELLIGENCE DEVELOPMENT IN THE DIAGNOSTICS OF LUNG CANCER

2018 ◽  
pp. 62-68 ◽  
Author(s):  
B. M. Moiseenko ◽  
A. A. Meldo ◽  
L. V. Utkin ◽  
I. Yu. Prokhorov ◽  
M. A. Ryabinin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Industrial Revolution ◽  
Detection System ◽  
Lung Nodule ◽  
Cancer Diagnostics ◽  
Digital Information ◽  
Intellectual Activity ◽  
Domestic Literature ◽  
Modern Computer

In the century of the fourth industrial revolution, there is a rapid progress of technological developments in medicine. Possibilities of collecting large amounts of digital information and the modern computer capacity growth are reasons for the increased attention to artificial intelligence (AI) and its role in the diagnostics and the prediction of diseases. In the diagnostics, AI aims to model the human intellectual activity, providing assistance to a practicing doctor in the processing of big data. Development of AI can be considered as a way for implementation and ensuring of national political and economic interests in the health care improvement. Lung cancer is on the first position of cancer incidences. This implies that the development and implementation of computed-aided systems for lung cancer diagnostic is very urgent and important. The article presents the results concerning the development of a computed-aided system for the lung nodule detection, which is based on the processing of computed tomography data. Perspectives of the AI application to the lung cancer diagnostics are discussed. There is a few information about a role of Russian developments in this area in foreign and domestic literature.

A New Automated Energy Meter Fraud Detection System Based on Artificial Intelligence

2021 ◽  
Author(s):  
Joao Pedro Klock ◽  
Jhonatan Correa ◽  
Miguel Bessa ◽  
Janier Arias-Garcia ◽  
Felipe Barboza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Detection System ◽  
Fraud Detection ◽  
Energy Meter

Tu1990 ARTIFICIAL INTELLIGENCE-ASSISTED POLYP DETECTION SYSTEM FOR COLONOSCOPY, BASED ON THE LARGEST AVAILABLE COLLECTION OF CLINICAL VIDEO DATA FOR MACHINE LEARNING

2019 ◽  
Vol 89 (6) ◽  
pp. AB646-AB647 ◽  
Author(s):  
Masashi Misawa ◽  
Shinei Kudo ◽  
Yuichi Mori ◽  
Tomonari Cho ◽  
Shinichi Kataoka ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Detection System ◽  
Video Data ◽  
Polyp Detection

scholarly journals A user-centered approach for detecting emotions with low-cost sensors

2020 ◽  
Vol 79 (47-48) ◽  
pp. 35885-35907
Author(s):  
Rita Francese ◽  
Michele Risi ◽  
Genoveffa Tortora
Keyword(s):  
Artificial Intelligence ◽  
Detection System ◽  
Low Cost ◽  
Affective State ◽  
Classification Performance ◽  
Emotion Detection ◽  
Emotion Classification ◽  
User Perceptions ◽  
Machine Learning Classifiers ◽  
User Centered

AbstractDetecting emotions is very useful in many fields, from health-care to human-computer interaction. In this paper, we propose an iterative user-centered methodology for supporting the development of an emotion detection system based on low-cost sensors. Artificial Intelligence techniques have been adopted for emotion classification. Different kind of Machine Learning classifiers have been experimentally trained on the users’ biometrics data, such as hearth rate, movement and audio. The system has been developed in two iterations and, at the end of each of them, the performance of classifiers (MLP, CNN, LSTM, Bidirectional-LSTM and Decision Tree) has been compared. After the experiment, the SAM questionnaire is proposed to evaluate the user’s affective state when using the system. In the first experiment we gathered data from 47 participants, in the second one an improved version of the system has been trained and validated by 107 people. The emotional analysis conducted at the end of each iteration suggests that reducing the device invasiveness may affect the user perceptions and also improve the classification performance.

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