Artificial intelligence for endoscopy in inflammatory bowel disease

2022 ◽  
Author(s):  
Kento Takenaka ◽  
Ami Kawamoto ◽  
Ryuichi Okamoto ◽  
Mamoru Watanabe ◽  
Kazuo Ohtsuka
Keyword(s):  
Artificial Intelligence ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Inflammatory Bowel

Clinical applications of artificial intelligence and machine learning‐based methods in inflammatory bowel disease

2021 ◽  
Vol 36 (2) ◽  
pp. 279-285
Author(s):  
Shirley Cohen‐Mekelburg ◽  
Sameer Berry ◽  
Ryan W Stidham ◽  
Ji Zhu ◽  
Akbar K Waljee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Clinical Applications ◽  
Inflammatory Bowel

scholarly journals Emerging use of artificial intelligence in inflammatory bowel disease

2020 ◽  
Vol 26 (44) ◽  
pp. 6923-6928
Author(s):  
Arushi Kohli ◽  
Erik A Holzwanger ◽  
Alexander N Levy
Keyword(s):  
Artificial Intelligence ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Inflammatory Bowel

scholarly journals Artificial intelligence in gastrointestinal endoscopy for inflammatory bowel disease: a systematic review and new horizons

2021 ◽  
Vol 14 ◽  
pp. 175628482110177
Author(s):  
Gian Eugenio Tontini ◽  
Alessandro Rimondi ◽  
Marta Vernero ◽  
Helmut Neumann ◽  
Maurizio Vecchi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Gastrointestinal Endoscopy ◽  
Early Stage ◽  
Real Life ◽  
Added Value ◽  
Clinical Scenarios ◽  
Inflammatory Bowel

Introduction: Since the advent of artificial intelligence (AI) in clinical studies, luminal gastrointestinal endoscopy has made great progress, especially in the detection and characterization of neoplastic and preneoplastic lesions. Several studies have recently shown the potential of AI-driven endoscopy for the investigation of inflammatory bowel disease (IBD). This systematic review provides an overview of the current position and future potential of AI in IBD endoscopy. Methods: A systematic search was carried out in PubMed and Scopus up to 2 December 2020 using the following search terms: artificial intelligence, machine learning, computer-aided, inflammatory bowel disease, ulcerative colitis (UC), Crohn’s disease (CD). All studies on human digestive endoscopy were included. A qualitative analysis and a narrative description were performed for each selected record according to the Joanna Briggs Institute methodologies and the PRISMA statement. Results: Of 398 identified records, 18 were ultimately included. Two-thirds of these (12/18) were published in 2020 and most were cross-sectional studies (15/18). No relevant bias at the study level was reported, although the risk of publication bias across studies cannot be ruled out at this early stage. Eleven records dealt with UC, five with CD and two with both. Most of the AI systems involved convolutional neural network, random forest and deep neural network architecture. Most studies focused on capsule endoscopy readings in CD ( n = 5) and on the AI-assisted assessment of mucosal activity in UC ( n = 10) for automated endoscopic scoring or real-time prediction of histological disease. Discussion: AI-assisted endoscopy in IBD is a rapidly evolving research field with promising technical results and additional benefits when tested in an experimental clinical scenario. External validation studies being conducted in large and prospective cohorts in real-life clinical scenarios will help confirm the added value of AI in assessing UC mucosal activity and in CD capsule reading. Plain language summary Artificial intelligence for inflammatory bowel disease endoscopy Artificial intelligence (AI) is a promising technology in many areas of medicine. In recent years, AI-assisted endoscopy has been introduced into several research fields, including inflammatory bowel disease (IBD) endoscopy, with promising applications that have the potential to revolutionize clinical practice and gastrointestinal endoscopy. We have performed the first systematic review of AI and its application in the field of IBD and endoscopy. A formal process of paper selection and analysis resulted in the assessment of 18 records. Most of these (12/18) were published in 2020 and were cross-sectional studies (15/18). No relevant biases were reported. All studies showed positive results concerning the novel technology evaluated, so the risk of publication bias cannot be ruled out at this early stage. Eleven records dealt with UC, five with CD and two with both. Most studies focused on capsule endoscopy reading in CD patients ( n = 5) and on AI-assisted assessment of mucosal activity in UC patients ( n = 10) for automated endoscopic scoring and real-time prediction of histological disease. We found that AI-assisted endoscopy in IBD is a rapidly growing research field. All studies indicated promising technical results. When tested in an experimental clinical scenario, AI-assisted endoscopy showed it could potentially improve the management of patients with IBD. Confirmatory evidence from real-life clinical scenarios should be obtained to verify the added value of AI-assisted IBD endoscopy in assessing UC mucosal activity and in CD capsule reading.

scholarly journals Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications

Genes ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1438
Author(s):  
Biljana Stankovic ◽  
Nikola Kotur ◽  
Gordana Nikcevic ◽  
Vladimir Gasic ◽  
Branka Zukic ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Disease Diagnosis ◽  
Omics Data ◽  
Current Application ◽  
Inflammatory Bowel ◽  
Future Outcomes ◽  
Clinical Records

Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.

scholarly journals Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions

2021 ◽  
Vol 27 (17) ◽  
pp. 1920-1935
Author(s):  
John Gubatan ◽  
Steven Levitte ◽  
Akshar Patel ◽  
Tatiana Balabanis ◽  
Mike T Wei ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Emerging Technologies ◽  
Future Directions ◽  
Inflammatory Bowel

scholarly journals Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease

2021 ◽  
Vol 9 ◽  
Author(s):  
Guihua Chen ◽  
Jun Shen
Keyword(s):  
Artificial Intelligence ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Productivity Loss ◽  
Work Productivity ◽  
Indirect Costs ◽  
Support Vector ◽  
Susceptible Host ◽  
Diagnosis And Prognosis ◽  
Inflammatory Bowel

Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease (CD), is an idiopathic condition related to a dysregulated immune response to commensal intestinal microflora in a genetically susceptible host. As a global disease, the morbidity of IBD reached a rate of 84.3 per 100,000 persons and reflected a continued gradual upward trajectory. The medical cost of IBD is also notably extremely high. For example, in Europe, it has €3,500 in CD and €2,000 in UC per patient per year, respectively. In addition, taking into account the work productivity loss and the reduced quality of life, the indirect costs are incalculable. In modern times, the diagnosis of IBD is still a subjective judgment based on laboratory tests and medical images. Its early diagnosis and intervention is therefore a challenging goal and also the key to control its progression. Artificial intelligence (AI)-assisted diagnosis and prognosis prediction has proven effective in many fields including gastroenterology. In this study, support vector machines were utilized to distinguish the significant features in IBD. As a result, the reliability of IBD diagnosis due to its impressive performance in classifying and addressing region problems was improved. Convolutional neural networks are advanced image processing algorithms that are currently in existence. Digestive endoscopic images can therefore be better understood by automatically detecting and classifying lesions. This study aims to summarize AI application in the area of IBD, objectively evaluate the performance of these methods, and ultimately understand the algorithm–dataset combination in the studies.

Artificial intelligence and inflammatory bowel disease: practicalities and future prospects

2021 ◽  
pp. flgastro-2021-102003
Author(s):  
Johanne Brooks-Warburton ◽  
James Ashton ◽  
Anjan Dhar ◽  
Tony Tham ◽  
Patrick B Allen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Inflammatory Bowel Disease ◽  
Risk Stratification ◽  
Bowel Disease ◽  
Emerging Technology ◽  
Disease Classification ◽  
Future Prospects ◽  
Patient Journey ◽  
Self Monitoring ◽  
Inflammatory Bowel

Artificial intelligence (AI) is an emerging technology predicted to have significant applications in healthcare. This review highlights AI applications that impact the patient journey in inflammatory bowel disease (IBD), from genomics to endoscopic applications in disease classification, stratification and self-monitoring to risk stratification for personalised management. We discuss the practical AI applications currently in use while giving a balanced view of concerns and pitfalls and look to the future with the potential of where AI can provide significant value to the care of the patient with IBD.

scholarly journals Clinician perspectives on the use of artificial intelligence in inflammatory bowel disease

2021 ◽  
Vol 94 (3) ◽  
pp. 662-663 ◽  
Author(s):  
Gaurav B. Nigam ◽  
Rajan N. Patel ◽  
Brian Bressler ◽  
Bu’ Hayee ◽  
Marietta Iacucci ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Clinician Perspectives ◽  
Inflammatory Bowel
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