The impact of deep convolutional neural network‐based artificial intelligence on colonoscopy outcomes: A systematic review with meta‐analysis

2020 ◽  
Vol 35 (10) ◽  
pp. 1676-1683 ◽  
Author(s):  
Muhammad Aziz ◽  
Rawish Fatima ◽  
Charles Dong ◽  
Wade Lee‐Smith ◽  
Ali Nawras
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Systematic Review ◽  
Convolutional Neural Network ◽  
Meta Analysis ◽  
Deep Convolutional Neural Network ◽  
The Impact

scholarly journals Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis

2021 ◽  
Vol 09 (04) ◽  
pp. E513-E521
Author(s):  
Munish Ashat ◽  
Jagpal Singh Klair ◽  
Dhruv Singh ◽  
Arvind Rangarajan Murali ◽  
Rajesh Krishnamoorthi
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Meta Analysis ◽  
Screening Colonoscopy ◽  
Average Risk ◽  
Polyp Detection ◽  
Withdrawal Time ◽  
Detection Rates ◽  
Adenoma Detection ◽  
The Impact

Abstract Background and study aims With the advent of deep neural networks (DNN) learning, the field of artificial intelligence (AI) is rapidly evolving. Recent randomized controlled trials (RCT) have investigated the influence of integrating AI in colonoscopy and its impact on adenoma detection rates (ADRs) and polyp detection rates (PDRs). We performed a systematic review and meta-analysis to reliably assess if the impact is statistically significant enough to warrant the adoption of AI -assisted colonoscopy (AIAC) in clinical practice. Methods We conducted a comprehensive search of multiple electronic databases and conference proceedings to identify RCTs that compared outcomes between AIAC and conventional colonoscopy (CC). The primary outcome was ADR. The secondary outcomes were PDR and total withdrawal time (WT). Results Six RCTs (comparing AIAC vs CC) with 5058 individuals undergoing average-risk screening colonoscopy were included in the meta-analysis. ADR was significantly higher with AIAC compared to CC (33.7 % versus 22.9 %; odds ratio (OR) 1.76, 95 % confidence interval (CI) 1.55–2.00; I2 = 28 %). Similarly, PDR was significantly higher with AIAC (45.6 % versus 30.6 %; OR 1.90, 95 %CI, 1.68–2.15, I2 = 0 %). The overall WT was higher for AIAC compared to CC (mean difference [MD] 0.46 (0.00–0.92) minutes, I2 = 94 %). Conclusions There is an increase in adenoma and polyp detection with the utilization of AIAC.

scholarly journals Towards robotics and artificial intelligence for the prevention of Covid 19 pandemic

2021 ◽  
Vol 229 ◽  
pp. 01035
Author(s):  
Saloua Senhaji ◽  
Sanaa Faquir ◽  
Mohammed Ouazzani Jamil
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Train Station ◽  
Facial Mask ◽  
Corona Virus ◽  
Temperature Detection ◽  
Medical Crisis

In times of medical crisis, robotics and artificial intelligence helps humans manage emergencies and ensure a fast and efficient decontamination process. In this paper, we propose a robot with temperature detection, Corona virus checker using new biosensors, and artificial intelligence facial mask detection based on the deep convolutional neural network. Our robot can sterilize and patrol any type of area. In particular, airports, the train station and transport facilities which are the routes of transmission of the virus from one country to another.

scholarly journals Classifying colorectal cancer or colorectal polyps in endoscopic setting using convolutional neural network: protocol for a systematic review and meta-analysis

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1086
Author(s):  
Kamyab Keshtkar ◽  
Abbas Keshtkar ◽  
Alireza Safarpour
Keyword(s):  
Neural Network ◽  
Colorectal Cancer ◽  
Systematic Review ◽  
Convolutional Neural Network ◽  
Prediction Models ◽  
Meta Analysis ◽  
Geographical Area ◽  
Colorectal Polyps ◽  
Primary Data ◽  
Data Synthesis

Background: Colorectal cancer (CRC) is the third most common cancer worldwide. Although colonoscopy screening has been proven as an effective strategy for preventing CRC unfortunately, even conventional colonoscopy by expert gastroenterologists can miss adenomas or pre-cancerous lesions in up to 25% of cases. This systematic review aimed to classify colorectal polyps (CRP) or CRC in endoscopic clinic settings using a new machine learning method, convolutional neural network (CNN).   Methods: We will search PubMed/MEDLINE, Scopus, Web of Science, IEEE, Inspec, ProQuest, Google Scholar, Microsoft Academic Search, ScienceOpen, arXiv, and bioRxiv from 1st January 2010 to the 31th of July 2020.  Our search will not be restricted based on language or geographical area. The primary studies will be selected that have observational design (cross-sectional, case control or cohort); the study subjects will be adult patients (>= 18 years old) referred to colonoscopy clinics; and the results of their colonoscopy evaluation will be available in the form of images or videos. The extracted data will be combined using meta-analysis of prediction models. The primary data synthesis will be performed based on area under curve-receiver operating characteristic curve and/or accuracy measures. We will use Stata version 14.2 (Statacorp; College Station, TX) for primary and secondary data synthesis. Conclusion: The inferences of our secondary research will provide evidence to evaluate the prognostic role of CNN in discriminating CRP or CRC in colonoscopy settings.

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