scholarly journals A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology

2019 ◽  
Vol 07 (12) ◽  
pp. E1616-E1623 ◽  
Author(s):  
Alanna Ebigbo ◽  
Christoph Palm ◽  
Andreas Probst ◽  
Robert Mendel ◽  
Johannes Manzeneder ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Gastrointestinal Tract ◽  
Gastrointestinal Endoscopy ◽  
Colorectal Polyps ◽  
Comprehensive Overview ◽  
Technical Aspects ◽  
Neoplastic Disorders ◽  
Number Of Publications ◽  
Basic Insight

Abstract Background and aim The growing number of publications on the application of artificial intelligence (AI) in medicine underlines the enormous importance and potential of this emerging field of research. In gastrointestinal endoscopy, AI has been applied to all segments of the gastrointestinal tract most importantly in the detection and characterization of colorectal polyps. However, AI research has been published also in the stomach and esophagus for both neoplastic and non-neoplastic disorders. The various technical as well as medical aspects of AI, however, remain confusing especially for non-expert physicians. This physician-engineer co-authored review explains the basic technical aspects of AI and provides a comprehensive overview of recent publications on AI in gastrointestinal endoscopy. Finally, a basic insight is offered into understanding publications on AI in gastrointestinal endoscopy.

The scientific progress and prospects of artificial intelligence in digestive endoscopy: a comprehensive bibliometric analysis (Preprint)

2021 ◽  
Author(s):  
Peiling Gan ◽  
Shu Huang ◽  
Xiao Pan ◽  
Shali Tan ◽  
Chunyu Zhong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Gastrointestinal Endoscopy ◽  
Web Of Science ◽  
Research Output ◽  
Scientific Progress ◽  
Digestive Endoscopy ◽  
Asian Countries ◽  
Microsoft Excel ◽  
Number Of Publications ◽  
Research Frontiers

UNSTRUCTURED Abstract Background: A growing number of studies have reported artificial intelligence (AI) has been developed for diagnosis and outcome prediction in clinical practice. Furthermore, AI in digestive endoscopy has attracted much attention, which has shown promising and stimulating results. Our study aimed to visualize the articles to determine the trends and hotspots of AI in digestive endoscopy. Methods: Publications on AI in digestive endoscopy research were retrieved from the Web of Science Core Collection (WoSCC) on March 14, 2021. Microsoft Excel 2016, VOSviewer 1.6.11.0, and CiteSpace V were used to assess and plot the research output. Results: The analytic research was based on original articles and reviews. A total of 121 records of AI research in digestive endoscopy published from 2017 to 2021 were retrieved. The citation number for these articles ranged from 0 to 142. The number of published articles increased 68-fold just from 2017 to 2020. All publications were distributed among 31 countries and 296 institutions. Asian countries had the most publications in this field (80.17%). Among the 31 countries, China and Japan were consistently the leading driving force and contributed mostly (31.40% and 28.93%, respectively), with a strong academic reputation in this area. Tada Tomohiro distributed the most related articles (13.22%) and was cited the most frequently. Gastrointestinal endoscopy published the largest number of publications (14.88%), and 4 of the top 10 cited references were in this leading journal. “Barrett’s esophagus” was the leading research hotspot. The keywords “classification,” “polyps,” “risk,” “histology,” and “resection” appeared most recently as research frontiers. Conclusions: Our study provides a systematic elaboration for researchers to obtain a good comprehension of AI development in digestive endoscopy.

scholarly journals Artificial intelligence for identification and characterization of colonic polyps

2021 ◽  
Vol 14 ◽  
pp. 263177452110146
Author(s):  
Nasim Parsa ◽  
Michael F. Byrne
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Computer Aided Design ◽  
Colorectal Polyps ◽  
Optical Biopsy ◽  
Machine Learning Techniques ◽  
Withdrawal Time ◽  
Adenoma Detection ◽  
Computer Aided

Colonoscopy remains the gold standard exam for colorectal cancer screening due to its ability to detect and resect pre-cancerous lesions in the colon. However, its performance is greatly operator dependent. Studies have shown that up to one-quarter of colorectal polyps can be missed on a single colonoscopy, leading to high rates of interval colorectal cancer. In addition, the American Society for Gastrointestinal Endoscopy has proposed the “resect-and-discard” and “diagnose-and-leave” strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the performance of optical biopsy has been suboptimal in community practice. With recent improvements in machine-learning techniques, artificial intelligence–assisted computer-aided detection and diagnosis have been increasingly utilized by endoscopists. The application of computer-aided design on real-time colonoscopy has been shown to increase the adenoma detection rate while decreasing the withdrawal time and improve endoscopists’ optical biopsy accuracy, while reducing the time to make the diagnosis. These are promising steps toward standardization and improvement of colonoscopy quality, and implementation of “resect-and-discard” and “diagnose-and-leave” strategies. Yet, issues such as real-world applications and regulatory approval need to be addressed before artificial intelligence models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of artificial intelligence for detection and characterization of colorectal polyps and review the limitation of existing artificial intelligence technologies and future directions for this field.

Su1744 DIAGNOSTIC ACCURACY AND LEARNING CURVE IN THE CHARACTERIZATION OF COLORECTAL POLYPS FOR FIRST YEAR GASTROINTESTINAL ENDOSCOPY TRAINEES

2019 ◽  
Vol 89 (6) ◽  
pp. AB404
Author(s):  
César Tróchez Mejía ◽  
Martha C. Galindo Orozco ◽  
Katia Picazo Ferrera ◽  
Cesar Jaurrieta Rico ◽  
Miguel Ángel Herrera ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Learning Curve ◽  
Gastrointestinal Endoscopy ◽  
Colorectal Polyps ◽  
First Year

scholarly journals Definition of competence standards for optical diagnosis of diminutive colorectal polyps: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement

Endoscopy ◽  
2021 ◽  
Author(s):  
Britt B. S. L. Houwen ◽  
Cesare Hassan ◽  
Veerle M. H. Coupé ◽  
Marjolein J. E. Greuter ◽  
Yark Hazewinkel ◽  
...  
Keyword(s):  
Gold Standard ◽  
Literature Search ◽  
Gastrointestinal Endoscopy ◽  
Colorectal Neoplasia ◽  
Position Statement ◽  
Colorectal Polyps ◽  
Optical Diagnosis ◽  
High Confidence ◽  
Level Of Agreement

Abstract Background The European Society of Gastrointestinal Endoscopy (ESGE) has developed a core curriculum for high quality optical diagnosis training for practice across Europe. The development of easy-to-measure competence standards for optical diagnosis can optimize clinical decision-making in endoscopy. This manuscript represents an official Position Statement of the ESGE aiming to define simple, safe, and easy-to-measure competence standards for endoscopists and artificial intelligence systems performing optical diagnosis of diminutive colorectal polyps (1 – 5 mm). Methods A panel of European experts in optical diagnosis participated in a modified Delphi process to reach consensus on Simple Optical Diagnosis Accuracy (SODA) competence standards for implementation of the optical diagnosis strategy for diminutive colorectal polyps. In order to assess the clinical benefits and harms of implementing optical diagnosis with different competence standards, a systematic literature search was performed. This was complemented with the results from a recently performed simulation study that provides guidance for setting alternative competence standards for optical diagnosis. Proposed competence standards were based on literature search and simulation study results. Competence standards were accepted if at least 80 % agreement was reached after a maximum of three voting rounds. Recommendation 1 In order to implement the leave-in-situ strategy for diminutive colorectal lesions (1–5 mm), it is clinically acceptable if, during real-time colonoscopy, at least 90 % sensitivity and 80 % specificity is achieved for high confidence endoscopic characterization of colorectal neoplasia of 1–5 mm in the rectosigmoid. Histopathology is used as the gold standard.Level of agreement 95 %. Recommendation 2 In order to implement the resect-and-discard strategy for diminutive colorectal lesions (1–5 mm), it is clinically acceptable if, during real-time colonoscopy, at least 80 % sensitivity and 80 % specificity is achieved for high confidence endoscopic characterization of colorectal neoplasia of 1–5 mm. Histopathology is used as the gold standard.Level of agreement 100 %. Conclusion The developed SODA competence standards define diagnostic performance thresholds in relation to clinical consequences, for training and for use when auditing the optical diagnosis of diminutive colorectal polyps.

scholarly journals Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps identification using text mining

2021 ◽  
pp. 1-13
Author(s):  
Lamiae Benhayoun ◽  
Daniel Lang
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Academic Training ◽  
Market Requirements ◽  
Job Advertisements ◽  
The Individual

BACKGROUND: The renewed advent of Artificial Intelligence (AI) is inducing profound changes in the classic categories of technology professions and is creating the need for new specific skills. OBJECTIVE: Identify the gaps in terms of skills between academic training on AI in French engineering and Business Schools, and the requirements of the labour market. METHOD: Extraction of AI training contents from the schools’ websites and scraping of a job advertisements’ website. Then, analysis based on a text mining approach with a Python code for Natural Language Processing. RESULTS: Categorization of occupations related to AI. Characterization of three classes of skills for the AI market: Technical, Soft and Interdisciplinary. Skills’ gaps concern some professional certifications and the mastery of specific tools, research abilities, and awareness of ethical and regulatory dimensions of AI. CONCLUSIONS: A deep analysis using algorithms for Natural Language Processing. Results that provide a better understanding of the AI capability components at the individual and the organizational levels. A study that can help shape educational programs to respond to the AI market requirements.

scholarly journals Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 1022
Author(s):  
Hoang T. Nguyen ◽  
Kate T. Q. Nguyen ◽  
Tu C. Le ◽  
Guomin Zhang
Keyword(s):  
Artificial Intelligence ◽  
Flame Retardancy ◽  
Material Science ◽  
Construction Materials ◽  
Engineering Problem ◽  
Future Research ◽  
Fire Performance ◽  
Structural Components ◽  
Comprehensive Overview ◽  
Fire Engineering

The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.

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