Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett's esophagus: a tandem randomized and video trial.

BACKGROUND: This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett's esophagus (BE). METHODS: 96 standardized endoscopy videos were assessed by 22 endoscopists with varying degrees of BE experience from 12 centers. Assessment was randomized into two video sets: group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett's esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level. RESULTS: AI had a stand-alone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.3%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1%, and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.8% [95%CI 65.2%-74.2%] to 78.0% [95%CI 74.0%-82.0%]; specificity 67.3% [95%CI 62.5%-72.2%] to 72.7% [95%CI 68.2%-77.3%]). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI. CONCLUSION: BE nonexperts benefitted significantly from additional AI. BE experts and nonexperts remained significantly below the stand-alone performance of AI, suggesting that there may be other factors influencing endoscopists' decisions to follow or discard AI advice.

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.

Optimizing adenoma detection in screening-related colonoscopy.

INTRODUCTION: Screening-related colonoscopy is a vital component of screening initiatives to both diagnose and prevent colorectal cancer (CRC), with prevention being reliant upon early and accurate detection of pre-malignant lesions. Several strategies, techniques, and interventions exist to optimize endoscopists' adenoma detection rates (ADR). AREAS COVERED: This narrative review provides an overview of the importance of ADR and other colonoscopy quality indicators. It then summarizes the available evidence regarding the effectiveness of the following domains in terms of improving ADR: endoscopist factors, pre-procedural parameters, peri-procedural parameters, intra-procedural strategies and techniques, antispasmodics, distal attachment devices, enhanced colonoscopy technologies, enhanced optics, and artificial intelligence. These summaries are based on an electronic search of the databases Embase, PubMed, and Cochrane performed on 12 December 2022. EXPERT OPINION: Given the prevalence and associated morbidity and mortality of CRC, the quality of screening-related colonoscopy quality is appropriately prioritized by patients, endoscopists, units, and payers alike. Endoscopists performing colonoscopy should be up to date regarding available strategies, techniques, and interventions to optimize their performance.

Artificial intelligence in inflammatory bowel disease: implications for clinical practice and future directions.

Inflammatory bowel disease encompasses Crohn's disease and ulcerative colitis and is characterized by uncontrolled, relapsing, and remitting course of inflammation in the gastrointestinal tract. Artificial intelligence represents a new era within the field of gastroenterology, and the amount of research surrounding artificial intelligence in patients with inflammatory bowel disease is on the rise. As clinical trial outcomes and treatment targets evolve in inflammatory bowel disease, artificial intelligence may prove as a valuable tool for providing accurate, consistent, and reproducible evaluations of endoscopic appearance and histologic activity, thereby optimizing the diagnosis process and identifying disease severity. Furthermore, as the applications of artificial intelligence for inflammatory bowel disease continue to expand, they may present an ideal opportunity for improving disease management by predicting treatment response to biologic therapies and for refining the standard of care by setting the basis for future treatment personalization and cost reduction. The purpose of this review is to provide an overview of the unmet needs in the management of inflammatory bowel disease in clinical practice and how artificial intelligence tools can address these gaps to transform patient care.

Artificial Intelligence in Inflammatory Bowel Disease Endoscopy: Implications for Clinical Trials.

Artificial intelligence shows promise for clinical research in inflammatory bowel disease endoscopy. Accurate assessment of endoscopic activity is important in clinical practice and inflammatory bowel disease clinical trials. Emerging artificial intelligence technologies can increase efficiency and accuracy of assessing the baseline endoscopic appearance in patients with inflammatory bowel disease and the impact that therapeutic interventions may have on mucosal healing in both of these contexts. In this review, state-of-the-art endoscopic assessment of mucosal disease activity in inflammatory bowel disease clinical trials is described, covering the potential for artificial intelligence to transform the current paradigm, its limitations, and suggested next steps. Site-based artificial intelligence quality evaluation and inclusion of patients in clinical trials without the need for a central reader is proposed; for following patient progress, a second reading using AI alongside a central reader with expedited reading is proposed. Artificial intelligence will support precision endoscopy in inflammatory bowel disease and is on the threshold of advancing inflammatory bowel disease clinical trial recruitment.

Application of Deep Learning Models to Improve Ulcerative Colitis Endoscopic Disease Activity Scoring Under Multiple Scoring Systems.

BACKGROUND AND AIMS: Lack of clinical validation and inter-observer variability are two limitations of endoscopic assessment and scoring of disease severity in patients with ulcerative colitis [UC]. We developed a deep learning [DL] model to improve, accelerate and automate UC detection, and predict the Mayo Endoscopic Subscore [MES] and the Ulcerative Colitis Endoscopic Index of Severity [UCEIS]. METHODS: A total of 134 prospective videos [1550 030 frames] were collected and those with poor quality were excluded. The frames were labelled by experts based on MES and UCEIS scores. The scored frames were used to create a preprocessing pipeline and train multiple convolutional neural networks [CNNs] with proprietary algorithms in order to filter, detect and assess all frames. These frames served as the input for the DL model, with the output being continuous scores for MES and UCEIS [and its components]. A graphical user interface was developed to support both labelling video sections and displaying the predicted disease severity assessment by the artificial intelligence from endoscopic recordings. RESULTS: Mean absolute error [MAE] and mean bias were used to evaluate the distance of the continuous model's predictions from ground truth, and its possible tendency to over/under-predict were excellent for MES and UCEIS. The quadratic weighted kappa used to compare the inter-rater agreement between experts' labels and the model's predictions showed strong agreement [0.87, 0.88 at frame-level, 0.88, 0.90 at section-level and 0.90, 0.78 at video-level, for MES and UCEIS, respectively]. CONCLUSIONS: We present the first fully automated tool that improves the accuracy of the MES and UCEIS, reduces the time between video collection and review, and improves subsequent quality assurance and scoring.

AGA White Paper: Challenges and Gaps in Innovation for the Performance of Colonoscopy for Screening and Surveillance of Colorectal Cancer.

In 2018, the American Gastroenterological Association's Center for GI Innovation and Technology convened a consensus conference, entitled "Colorectal Cancer Screening and Surveillance: Role of Emerging Technology and Innovation to Improve Outcomes." The conference participants, which included more than 60 experts in colorectal cancer, considered recent improvements in colorectal cancer screening rates and polyp detection, persistent barriers to colonoscopy uptake, and opportunities for performance improvement and innovation. This white paper originates from that conference. It aims to summarize current patient- and physician-centered gaps and challenges in colonoscopy, diagnostic and therapeutic challenges affecting colonoscopy uptake, and the potential use of emerging technologies and quality metrics to improve patient outcomes.

Artificial intelligence for identification and characterization of colonic polyps.

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.

Colorectal polyp characterization with standard endoscopy: Will Artificial Intelligence succeed where human eyes failed?

The American Society for Gastrointestinal Endoscopy (ASGE) 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 diagnostic thresholds set by these guidelines are not always met in community practice. To overcome this sub-optimal performance, artificial intelligence (AI) has been applied to the field of endoscopy. The incorporation of deep learning algorithms with AI models resulted in highly accurate systems that match the expert endoscopists' optical biopsy and exceed the ASGE recommended thresholds. Recent studies have demonstrated that the integration of AI in clinical practice results in significant improvement in endoscopists' diagnostic accuracy while reducing the time to make a diagnosis. Yet, several points need to be addressed before AI models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of AI for characterization of colorectal polyps, and review the current limitation and future directions for this field.

Artificial intelligence and deep learning for small bowel capsule endoscopy.

Capsule endoscopy is ideally suited to artificial intelligence-based interpretation given its reliance on pattern recognition in still images. Time saving viewing modes and lesion detection features currently available rely on machine learning algorithms, a form of artificial intelligence. Current software necessitates close human supervision given poor sensitivity relative to an expert reader. However, with the advent of deep learning, artificial intelligence is becoming increasingly reliable and will be increasingly relied upon. We review the major advances in artificial intelligence for capsule endoscopy in recent publications and briefly review artificial intelligence development for historical understanding. Importantly, recent advancements in artificial intelligence have not yet been incorporated into practice and it is immature to judge the potential of this technology based on current platforms. Remaining regulatory and standardization hurdles are being overcome and artificial intelligence-based clinical applications are likely to proliferate rapidly.

Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method.

BACKGROUND : Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. METHODS : An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers, from nine countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. RESULTS : The top 10 ranked questions were categorized into five themes. Theme 1: clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterization, determining the optimal end points for evaluation of AI, and demonstrating impact on interval cancer rates. Theme 2: technological developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false-positive rates, and minimizing latency. Theme 3: clinical adoption/integration (1 question), concerning the effective combination of detection and characterization into one workflow. Theme 4: data access/annotation (1 question), concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: regulatory approval (1 question), related to making regulatory approval processes more efficient. CONCLUSIONS : This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy.

Endoscopic prediction of submucosal invasion in Barrett's cancer with the use of artificial intelligence: a pilot study.

BACKGROUND: The accurate differentiation between T1a and T1b Barrett's-related cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an artificial intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett's cancer on white-light images. METHODS: Endoscopic images from three tertiary care centers in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) were evaluated using the AI system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett's cancer. RESULTS: The sensitivity, specificity, F1 score, and accuracy of the AI system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.74, and 0.71, respectively. There was no statistically significant difference between the performance of the AI system and that of experts, who showed sensitivity, specificity, F1, and accuracy of 0.63, 0.78, 0.67, and 0.70, respectively. CONCLUSION: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett's cancer. AI scored equally to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and real-life settings. Nevertheless, the correct prediction of submucosal invasion in Barrett's cancer remains challenging for both experts and AI.

A primer on artificial intelligence and its application to endoscopy.

Artificial intelligence (AI) has emerged as a powerful and exciting new technology poised to impact many aspects of health care. In endoscopy, AI is now being used to detect and characterize benign and malignant GI lesions and assess malignant lesion depth of invasion. It will undoubtedly also find use in capsule endoscopy and inflammatory bowel disease. Herein, we provide the general endoscopist with a brief overview of AI and its emerging uses in our field. We also touch on the challenges of incorporating AI into clinical practice, such as workflow integration, data storage, and data privacy.