Artificial Intelligence In Endoscopy Research Articles

The application of the combination between artificial intelligence and endoscopy in gastrointestinal tumors

AbstractGastrointestinal (GI) tumors have always been a major type of malignant tumor and a leading cause of tumor‐related deaths worldwide. The main principles of modern medicine for GI tumors are early prevention, early diagnosis, and early treatment, with early diagnosis being the most effective measure. Endoscopy, due to its ability to visualize lesions, has been one of the primary modalities for screening, diagnosing, and treating GI tumors. However, a qualified endoscopist often requires long training and extensive experience, which to some extent limits the wider use of endoscopy. With advances in data science, artificial intelligence (AI) has brought a new development direction for the endoscopy of GI tumors. AI can quickly process large quantities of data and images and improve diagnostic accuracy with some training, greatly reducing the workload of endoscopists and assisting them in early diagnosis. Therefore, this review focuses on the combined application of endoscopy and AI in GI tumors in recent years, describing the latest research progress on the main types of tumors and their performance in clinical trials, the application of multimodal AI in endoscopy, the development of endoscopy, and the potential applications of AI within it, with the aim of providing a reference for subsequent research.

Diagnostic Accuracy of Artificial Intelligence in Endoscopy: Umbrella Review.

Some research has already reported the diagnostic value of artificial intelligence (AI) in different endoscopy outcomes. However, the evidence is confusing and of varying quality. This review aimed to comprehensively evaluate the credibility of the evidence of AI's diagnostic accuracy in endoscopy. Before the study began, the protocol was registered on PROSPERO (CRD42023483073). First, 2 researchers searched PubMed, Web of Science, Embase, and Cochrane Library using comprehensive search terms. Then, researchers screened the articles and extracted information. We used A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR2) to evaluate the quality of the articles. When there were multiple studies aiming at the same result, we chose the study with higher-quality evaluations for further analysis. To ensure the reliability of the conclusions, we recalculated each outcome. Finally, the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) was used to evaluate the credibility of the outcomes. A total of 21 studies were included for analysis. Through AMSTAR2, it was found that 8 research methodologies were of moderate quality, while other studies were regarded as having low or critically low quality. The sensitivity and specificity of 17 different outcomes were analyzed. There were 4 studies on esophagus, 4 studies on stomach, and 4 studies on colorectal regions. Two studies were associated with capsule endoscopy, two were related to laryngoscopy, and one was related to ultrasonic endoscopy. In terms of sensitivity, gastroesophageal reflux disease had the highest accuracy rate, reaching 97%, while the invasion depth of colon neoplasia, with 71%, had the lowest accuracy rate. On the other hand, the specificity of colorectal cancer was the highest, reaching 98%, while the gastrointestinal stromal tumor, with only 80%, had the lowest specificity. The GRADE evaluation suggested that the reliability of most outcomes was low or very low. AI proved valuabe in endoscopic diagnoses, especially in esophageal and colorectal diseases. These findings provide a theoretical basis for developing and evaluating AI-assisted systems, which are aimed at assisting endoscopists in carrying out examinations, leading to improved patient health outcomes. However, further high-quality research is needed in the future to fully validate AI's effectiveness.

Role of Artificial Intelligence in Endoscopic Intervention: A Clinical Review.

Gastrointestinal diseases are increasing in global prevalence. As a result, the contribution to both mortality and healthcare costs is increasing. While interventions utilizing scoping techniques or ultrasound are crucial to both the timely diagnosis and management of illness, a few limitations are associated with these techniques. Artificial intelligence, using computerized diagnoses, deep learning systems, or neural networks, is increasingly being employed in multiple aspects of medicine to improve the characteristics and outcomes of these tools. Therefore, this review aims to discuss applications of artificial intelligence in endoscopy, colonoscopy, and endoscopic ultrasound.

Exploring human–artificial intelligence interactions in a negative pragmatic trial of computer-aided polyp detection

Background and aimsThe progress of artificial intelligence (AI) in endoscopy is at a crossroads. The positive results of randomized controlled trials (RCT) of computer-aided detection (CADe) have not been replicated in multiple pragmatic CADe trials, including ours. This gap between efficacy and effectiveness remains to be understood. We surveyed and interviewed our trial’s colonoscopists to gain insight into human-AI interactions. MethodsWe used a sequential, mixed-methodology design. Following the trial, we administered Survey 1 focusing on attitudes and beliefs before and after trying CADe. The trial’s null results were disclosed, and we then administered Survey 2 and conducted open-ended interviews, focusing on reactions to the null results. Responses were analyzed overall and by baseline adenoma detection rate (ADR) tertile. We identified key themes utilizing thematic analysis and qualitative software. ResultsNearly all colonoscopists responded (22 and 21 of 24 [92%, 88%] for Surveys 1 and 2). Most (96%) regarded endoscopic ability as critical to their professional identity. Large majorities conveyed trust in and enthusiasm for AI before and after trying CADe (82-87%), and desired to have CADe available (72%). Nearly two-thirds (62%) were surprised by the null results. There were few differences by ADR. No unifying explanation for the null results emerged from surveys or individual interviews. Colonoscopists expressed a range of expectations for AI in endoscopy. ConclusionsLack of enthusiasm or mistrust of AI/CADe do not explain our pragmatic CADe trial’s null results. AI may need to target dimensions beyond optical recognition to realize its promise in endoscopy.

AI-luminating Artificial Intelligence in Inflammatory Bowel Diseases: A Narrative Review on the Role of AI in Endoscopy, Histology, and Imaging for IBD.

Endoscopy, histology, and cross-sectional imaging serve as fundamental pillars in the detection, monitoring, and prognostication of inflammatory bowel disease (IBD). However, interpretation of these studies often relies on subjective human judgment, which can lead to delays, intra- and interobserver variability, and potential diagnostic discrepancies. With the rising incidence of IBD globally coupled with the exponential digitization of these data, there is a growing demand for innovative approaches to streamline diagnosis and elevate clinical decision-making. In this context, artificial intelligence (AI) technologies emerge as a timely solution to address the evolving challenges in IBD. Early studies using deep learning and radiomics approaches for endoscopy, histology, and imaging in IBD have demonstrated promising results for using AI to detect, diagnose, characterize, phenotype, and prognosticate IBD. Nonetheless, the available literature has inherent limitations and knowledge gaps that need to be addressed before AI can transition into a mainstream clinical tool for IBD. To better understand the potential value of integrating AI in IBD, we review the available literature to summarize our current understanding and identify gaps in knowledge to inform future investigations.

The adoption of artificial intelligence assisted endoscopy in the Middle East: challenges and future potential.

The use of artificial intelligence (AI) in endoscopy has shown immense potential to enhance diagnostic accuracy, streamline procedures, and improve patient outcomes. There are potential uses in every field of endoscopy, from improving adenoma detection rate (ADR) in colonoscopy to reducing read time in capsule endoscopy or minimizing blind spots in gastroscopy. Indeed, some of these systems are already licensed and in commercial use across the world. In the Middle East, where healthcare systems are rapidly evolving, there is a growing interest in adopting AI technologies to revolutionise endoscopic practices. This article provides an overview of the advancements, potential opportunities and challenges associated with the implementation of AI in endoscopy within the Middle East region. Our aim is to contribute to the ongoing dialogue surrounding the implementation of AI in endoscopy and consider some of the factors that are particularly relevant in the Middle Eastern context, including the need to train the models for local populations, cost and training, as well as trying to ensure equity of access for patients. It provides valuable insights for healthcare professionals, policymakers, and researchers interested in leveraging AI to enhance endoscopic procedures, improve patient care, and address the unique healthcare needs of the Middle East population.

The Importance of Artificial Intelligence in Upper Gastrointestinal Endoscopy.

Recently, there has been a growing interest in the application of artificial intelligence (AI) in medicine, especially in specialties where visualization methods are applied. AI is defined as a computer's ability to achieve human cognitive performance, which is accomplished through enabling computer "learning". This can be conducted in two ways, as machine learning and deep learning. Deep learning is a complex learning system involving the application of artificial neural networks, whose algorithms imitate the human form of learning. Upper gastrointestinal endoscopy allows examination of the esophagus, stomach and duodenum. In addition to the quality of endoscopic equipment and patient preparation, the performance of upper endoscopy depends on the experience and knowledge of the endoscopist. The application of artificial intelligence in endoscopy refers to computer-aided detection and the more complex computer-aided diagnosis. The application of AI in upper endoscopy is aimed at improving the detection of premalignant and malignant lesions, with special attention on the early detection of dysplasia in Barrett's esophagus, the early detection of esophageal and stomach cancer and the detection of H. pylori infection. Artificial intelligence reduces the workload of endoscopists, is not influenced by human factors and increases the diagnostic accuracy and quality of endoscopic methods.

Assisted documentation as a new focus for artificial intelligence in endoscopy: the precedent of reliable withdrawal time and image reporting.

BACKGROUND : Reliable documentation is essential for maintaining quality standards in endoscopy; however, in clinical practice, report quality varies. We developed an artificial intelligence (AI)-based prototype for the measurement of withdrawal and intervention times, and automatic photodocumentation. METHOD: A multiclass deep learning algorithm distinguishing different endoscopic image content was trained with 10 557 images (1300 examinations, nine centers, four processors). Consecutively, the algorithm was used to calculate withdrawal time (AI prediction) and extract relevant images. Validation was performed on 100 colonoscopy videos (five centers). The reported and AI-predicted withdrawal times were compared with video-based measurement; photodocumentation was compared for documented polypectomies. RESULTS: Video-based measurement in 100 colonoscopies revealed a median absolute difference of 2.0 minutes between the measured and reported withdrawal times, compared with 0.4 minutes for AI predictions. The original photodocumentation represented the cecum in 88 examinations compared with 98/100 examinations for the AI-generated documentation. For 39/104 polypectomies, the examiners' photographs included the instrument, compared with 68 for the AI images. Lastly, we demonstrated real-time capability (10 colonoscopies). CONCLUSION : Our AI system calculates withdrawal time, provides an image report, and is real-time ready. After further validation, the system may improve standardized reporting, while decreasing the workload created by routine documentation.

The Pros and Cons of Artificial Intelligence in Endoscopy.

1Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy 2Humanitas Clinical and Research Center –IRCCS-, Endoscopy Unit, Rozzano, Italy. 3Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway 4Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan 5University of Kansas School of Medicine and VA Medical Center, Kansas City, United States

Research trends in endoscopic applications in early gastric cancer: A bibliometric analysis of studies published from 2012 to 2022.

Endoscopy is the optimal method of diagnosing and treating early gastric cancer (EGC), and it is therefore important to keep up with the rapid development of endoscopic applications in EGC. This study utilized bibliometric analysis to describe the development, current research progress, hotspots, and emerging trends in this field. We retrieved publications about endoscopic applications in EGC from 2012 to 2022 from Web of Science™ (Clarivate™, Philadelphia, PA, USA) Core Collection (WoSCC). We mainly used CiteSpace (version 6.1.R3) and VOSviewer (version 1.6.18) to perform the collaboration network analysis, co-cited analysis, co-occurrence analysis, cluster analysis, and burst detection. A total of 1,333 publications were included. Overall, both the number of publications and the average number of citations per document per year increased annually. Among the 52 countries/regions that were included, Japan contributed the most in terms of publications, citations, and H-index, followed by the Republic of Korea and China. The National Cancer Center, based in both Japan and the Republic of Korea, ranked first among institutions in terms of number of publications, citation impact, and the average number of citations. Yong Chan Lee was the most productive author, and Ichiro Oda had the highest citation impact. In terms of cited authors, Gotoda Takuji had both the highest citation impact and the highest centrality. Among journals, Surgical Endoscopy and Other Interventional Techniques had the most publications, and Gastric Cancer had the highest citation impact and H-index. Among all publications and cited references, a paper by Smyth E C etal., followed by one by Gotoda T etal., had the highest citation impact. Using keywords co-occurrence and cluster analysis, 1,652 author keywords were categorized into 26 clusters, and we then divided the clusters into six groups. The largest and newest clusters were endoscopic submucosal dissection and artificial intelligence (AI), respectively. Over the last decade, research into endoscopic applications in EGC has gradually increased. Japan and the Republic of Korea have contributed the most, but research in this field in China, from an initially low base, is developing at a striking speed. However, a lack of collaboration among countries, institutions, and authors, is common, and this should be addressed in future. The main focus of research in this field (i.e., the largest cluster) is endoscopic submucosal dissection, and the topic at the frontier (i.e., the newest cluster) is AI. Future research should focus on the application of AI in endoscopy, and its implications for the clinical diagnosis and treatment of EGC.

The future of endoscopy – what are the thoughts on artificial intelligence?

ABSTRACT There is an emerging role of artificial intelligence (AI) in endoscopy with studies on early systems showing promising results. However, various limitations inhibit widespread use. The aim of this study was to ascertain the sentiments of endoscopists and understand the benefits and barriers towards adoption of AI systems into healthcare. An anonymous online 18-question survey was disseminated to gastroenterology and surgical departments across UK. A total of 75 endoscopists completed the questionnaire. The majority felt that AI would increase adenoma detection rate (ADR) (72.8%) and aid lesion characterisation (78.6%). However, only a quarter of respondents were either moderately or very familiar with AI, and there was no consensus on necessity of AI in endoscopy. The key barriers identified were cost, accessibility and lack of guidelines. Endoscopists believe AI systems will have a positive impact on endoscopy; however, these systems must provide quality assurance through large clinical trials before adoption.

Colorectal cancer: technological revolution

Colorectal cancer represents 4500 incidental cases in Switzerland per year, with an incidence increasing among the youngest patients. Technological innovation guides the management of colorectal cancer. Artificial intelligence in endoscopy optimizes the detection of small colonic lesions. Submucosal dissection allows treating extensive lesions at an early stage of the disease. The improvement of surgical techniques, notably robotic surgery, allows limiting complications and optimizing organ preservation. Molecular tools are leading to the development of promising targeted therapies for localized or advanced disease. The development of reference centers tends to bring together this expertise.

Artificial intelligence in endoscopy: Overview, applications, and future directions.

Since the emergence of artificial intelligence (AI) in medicine, endoscopy applications in gastroenterology have been at the forefront of innovations. The ever-increasing number of studies necessitates the need to organize and classify applications in a useful way. Separating AI capabilities by computer aided detection (CADe), diagnosis (CADx), and quality assessment (CADq) allows for a systematic evaluation of each application. CADe studies have shown promise in accurate detection of esophageal, gastric and colonic neoplasia as well as identifying sources of bleeding and Crohn's disease in the small bowel. While more advanced CADx applications employ optical biopsies to give further information to characterize neoplasia and grade inflammatory disease, diverse CADq applications ensure quality and increase the efficiency of procedures. Future applications show promise in advanced therapeutic modalities and integrated systems that provide multimodal capabilities. AI is set to revolutionize clinical decision making and performance of endoscopy.

Artificial intelligence in endoscopy: More than what meets the eye in screening colonoscopy and endosonographic evaluation of pancreatic lesions

Artificial intelligence in endoscopy: More than what meets the eye in screening colonoscopy and endosonographic evaluation of pancreatic lesions

The application of artificial intelligence in gastrointestinal endoscopy: a state-of-the-art review

Endoscopy is an important tool for detecting and diagnosing digestive diseases. However, the performance of endoscopists varies, which significantly impacts the health outcomes of patients. In recent years, with the continuous development of science and technology, artificial intelligence (AI) has also set off a new wave. Nowadays, AI has been widely studied in the medical field and has shown great potential, especially in gastrointestinal endoscopy. The application of AI in endoscopy mainly includes the detection of lesions, classification of diseases, selection of best therapy, prognosis judgment, quality control, etc. What's more, the effectiveness and safety of the application of AI in gastrointestinal endoscopy have been confirmed in clinical trials. In this paper, we review the current research status and future development of AI in gastrointestinal endoscopy. Core Tips: Endoscopy is an important tool for detecting and diagnosing digestive diseases. However, the level of endoscopists varies, which significantly impacts the health outcomes of patients. In recent years, with the continuous development of science and technology, artificial intelligence (AI) has also set off a new wave. AI is increasingly widely used in the medical field nowadays and has shown great potential, especially in gastrointestinal endoscopy. The application of AI in endoscopy mainly includes the detection of lesions, classification of diseases, selection of best therapy, prognosis judgment, quality control, etc. What's more, the effectiveness and safety of the application of AI in gastrointestinal endoscopy have been confirmed in clinical trials. In this paper, we review the current research status and future development of AI in gastrointestinal endoscopy.

The application of artificial intelligence in gastrointestinal endoscopy: a state-of-the-art review

Endoscopy is an important tool for detecting and diagnosing digestive diseases. However, the performance of endoscopists varies, which significantly impacts the health outcomes of patients. In recent years, with the continuous development of science and technology, artificial intelligence (AI) has also set off a new wave. Nowadays, AI has been widely studied in the medical field and has shown great potential, especially in gastrointestinal endoscopy. The application of AI in endoscopy mainly includes the detection of lesions, classification of diseases, selection of best therapy, prognosis judgment, quality control, etc. What's more, the effectiveness and safety of the application of AI in gastrointestinal endoscopy have been confirmed in clinical trials. In this paper, we review the current research status and future development of AI in gastrointestinal endoscopy.

Key research questions for implementation of artificial intelligence in capsule endoscopy

Artificial intelligence (AI) is rapidly infiltrating multiple areas in medicine, with gastrointestinal endoscopy paving the way in both research and clinical applications. Multiple challenges associated with the incorporation of AI in endoscopy are being addressed in recent consensus documents. In the current paper, we aimed to map future challenges and areas of research for the incorporation of AI in capsule endoscopy (CE) practice. Modified three-round Delphi consensus online survey. The study design was based on a modified three-round Delphi consensus online survey distributed to a group of CE and AI experts. Round one aimed to map out key research statements and challenges for the implementation of AI in CE. All queries addressing the same questions were merged into a single issue. The second round aimed to rank all generated questions during round one and to identify the top-ranked statements with the highest total score. Finally, the third round aimed to redistribute and rescore the top-ranked statements. Twenty-one (16 gastroenterologists and 5 data scientists) experts participated in the survey. In the first round, 48 statements divided into seven themes were generated. After scoring all statements and rescoring the top 12, the question of AI use for identification and grading of small bowel pathologies was scored the highest (mean score 9.15), correlation of AI and human expert reading-second (9.05), and real-life feasibility-third (9.0). In summary, our current study points out a roadmap for future challenges and research areas on our way to fully incorporating AI in CE reading.

Artificial intelligence in endoscopy: The challenges and future directions

Artificial intelligence based approaches, in particular deep learning, have achieved state-of-the-art performance in medical fields with increasing number of software systems being approved by both Europe and United States. This paper reviews their applications to early detection of oesophageal cancers with a focus on their advantages and pitfalls. The paper concludes with future recommendations towards the development of a real-time, clinical implementable, interpretable and robust diagnosis support systems.

Artificial Intelligence in Endoscopy.

Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular, the advent of convolutional neural network, which is a class of deep learning method, has the potential to revolutionize the field of GI endoscopy, including esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy. A total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified in this review. The main focuses of AI in EGD are cancer detection, identifying the depth of cancer invasion, prediction of pathological diagnosis, and prediction of Helicobacter pylori infection. In the field of CE, automated detection of bleeding sites, ulcers, tumors, and various small bowel diseases is being investigated. AI in colonoscopy has advanced with several patient-based prospective studies being conducted on the automated detection and classification of colon polyps. Furthermore, research on inflammatory bowel disease has also been recently reported. Most studies of AI in the field of GI endoscopy are still in the preclinical stages because of the retrospective design using still images. Video-based prospective studies are needed to advance the field. However, AI will continue to develop and be used in daily clinical practice in the near future. In this review, we have highlighted the published literature along with providing current status and insights into the future of AI in GI endoscopy.

GIE Editorial Board top 10 topics: advances in GI endoscopy in 2020

The American Society for Gastrointestinal Endoscopy's Gastrointestinal Endoscopy Editorial Board reviewed a systematic literature search of original endoscopy-related articles published during 2020 in Gastrointestinal Endoscopy and 10 other high-impact medical and gastroenterology journals. Votes from each individual board member were tallied to identify a consensus list of the 10 most significant topic areas in GI endoscopy over the calendar year of study using 4 criteria: significance, novelty, impact on national health, and impact on global health. The 10 areas identified were as follows: artificial intelligence in endoscopy, coronavirus disease 2019 and GI practice, third-space endoscopy, lumen-apposing metal stents, single-use duodenoscopes and other disposable equipment, endosonographic needle technology and techniques, endoscopic closure devices, advances in GI bleeding management, improvements in polypectomy techniques, and bariatric endoscopy. Each board member contributed a summary of important articles relevant to 1 to 2 topic areas, leading to a collective summary thatis presented in this document of the "top 10" endoscopic advances of2020.