Role of ultra-high definition endoscopy (endomicroscopy and endocytoscopy) and real-time histologic examination in inflammatory bowel disease: Scoping review

Artificial Intelligence in Inflammatory Bowel Disease Endoscopy

Inflammatory bowel diseases (IBDs), comprising Crohn's disease (CD) and ulcerative colitis (UC), are chronic immune-mediated inflammatory diseases of the gastrointestinal (GI) tract with still-elusive etiopathogeneses and an increasing prevalence worldwide. Despite the growing availability of more advanced therapies in the last two decades, there are still a number of unmet needs. For example, the achievement of mucosal healing has been widely demonstrated as a prognostic marker for better outcomes and a reduced risk of dysplasia and cancer; however, the accuracy of endoscopy is crucial for both this aim and the precise and reproducible evaluation of endoscopic activity and the detection of dysplasia. Artificial intelligence (AI) has drastically altered the field of GI studies and is being extensively applied to medical imaging. The utilization of deep learning and pattern recognition can help the operator optimize image classification and lesion segmentation, detect early mucosal abnormalities, and eventually reveal and uncover novel biomarkers with biologic and prognostic value. The role of AI in endoscopy-and potentially also in histology and imaging in the context of IBD-is still at its initial stages but shows promising characteristics that could lead to a better understanding of the complexity and heterogeneity of IBDs, with potential improvements in patient care and outcomes. The initial experience with AI in IBDs has shown its potential value in the differentiation of UC and CD when there is no ileal involvement, reducing the significant amount of time it takes to review videos of capsule endoscopy and improving the inter- and intra-observer variability in endoscopy reports and scoring. In addition, these initial experiences revealed the ability to predict the histologic score index and the presence of dysplasia. Thus, the purpose of this review was to summarize recent advances regarding the application of AI in IBD endoscopy as there is, indeed, increasing evidence suggesting that the integration of AI-based clinical tools will play a crucial role in paving the road to precision medicine in IBDs.

Advanced Endoscopic Imaging for Assessing Mucosal Healing and Histologic Remission in Inflammatory Bowel Diseases

Recent advances in the field of endoscopy have found fertile ground for application in inflammatory bowel diseases (IBD). Mucosal healing is a primary goal of IBD therapy, and current evidence shows that histologic remission (HR) is an additional desirable outcome. However, with the use of standard endoscopy, a considerable number of patients with histologically active disease go unrecognized. This narrative article examines the role, current or potential, of each endoscopic technique, from standard white-light endoscopy to molecular imaging, in the assessment of mucosal healing and HR in IBD.

Advanced Endoscopic Imaging to Predict Clinical Outcomes in Inflammatory Bowel Disease

Advanced endoscopic imaging including high-definition endoscopy, confocal laser endomicroscopy (CLE) and endocytoscopy (EC) serve as an adjunct to predict clinical outcomes in inflammatory bowel disease (IBD). CLE can identify gut barrier dysfunction which correlates with disease behavior and long-term disease outcome. EC allows the assessment of histologic activity. Future controlled studies are warranted to define a definite role of these novel technologies in the management of patients with IBD.

Advanced Endoscopic Imaging for Dysplasia Characterization in Inflammatory Bowel Disease

Recent therapeutic innovations in the management of inflammatory bowel disease (IBD) have significantly improved patient outcomes, leading to increased life expectancy and reducing the necessity for total colectomy. However, this prolonged disease duration increases the cumulative risk for dysplasia and eventually colorectal cancer development. Therefore, timely detection and correct characterization of emerging dysplastic lesions is of great importance in longstanding IBD. This narrative review aims to elucidate the current state of advanced endoscopic imaging for dysplasia characterization in inflammatory bowel disease.

Feasibility of Optical Biopsy During Endoscopic Sinus Surgery With Confocal Laser Endomicroscopy: A Pilot Study

OBJECTIVE

Confocal laser endomicroscopy (CLE) is an optical imaging technique that allows in vivo, real-time, microscope-like assessment of superficial lesions. Although there is substantial data on CLE use in the upper GI tract, there is limited information regarding its application in the nasal cavity and paranasal sinuses. This study aims to assess the feasibility and diagnostic metrics of CLE in the nasal cavity and paranasal sinuses regarding differentiation between healthy/benign and malignant tissue. These structures show, however, a wider variety of frequent and concomitant benign and malignant pathologies, which could pose an increased challenge for optical biopsy by CLE.

METHODS

We performed CLE on a case series of six patients with various findings in the nose (three chronic rhinosinusitis, adenocarcinoma, meningoenzephalozele, esthesionneuroblastoma). Forty-two sequences (3792 images) from various structures in the nasal cavity and/or paranasal sinuses were acquired. Biopsies were taken at corresponding locations and analyzed in hematoxylin and eosin staining as a standard of reference. Three independent examiners blinded to the histopathology assessed the sequences.

RESULTS

Healthy and inflamed mucosa could be distinguished from malignant lesions with an accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 84.1%, 85.4%, 83.1%, 72.5%, and 92.1%, respectively, with a substantial agreement between raters (Fleiss κ = 0.62).

CONCLUSION

This technique shows, despite its limitations, potential as an adjunctive imaging technique during sinus surgery; however, the creation of a scoring system based on reproducible and defined characteristics in a larger more diverse population should be the focus of further research to improve its diagnostic value and clinical utility.

LEVEL OF EVIDENCE

NA Laryngoscope, 134:4217-4224, 2024.

Endoscopic approaches to the management of dysplasia in inflammatory bowel disease: A state-of-the-art narrative review

Patients with inflammatory bowel disease (IBD) are at an increased risk of developing colitis-associated neoplasia (CAN), including colorectal cancer (CRC), through the inflammation-dysplasia-neoplasia pathway. Dysplasia is the most reliable, early and actionable marker for CAN in these patients. While such lesions are frequently encountered, adequate management depends on an accurate assessment, complete resection and close surveillance. With recent advances in endoscopic technologies and research in the field of CAN, the management of dysplastic lesions has significantly improved. The American Gastroenterology Association and Surveillance for Colorectal Endoscopic Neoplasia Detection (SCENIC) provide a guideline framework for approaching dysplastic lesions in patients with IBD. However, there are significant gaps in these recommendations and real-world clinical practice. Accurate lesion assessment remains pivotal for adequate management of CAN. Artificial intelligence-guided modalities are now increasingly being used to aid the detection of these lesions further. As the lesion detection technologies are improving, our armamentarium of resection techniques is also expanding and includes hot or cold polypectomy, endoscopic mucosal resection, endoscopic sub-mucosal dissection and full-thickness resection. With the broadened scope of endoscopic resection, the recommendations regarding surveillance after resection has also changed. Certain patient populations such as those with invisible dysplasia or with prior colectomy and ileal pouch anal anastomosis need special consideration. In the present review, we aim to provide a state-of-the-art summary of the current practice of endoscopic detection, resection and surveillance of dysplasia in patients with IBD and provide some perspective on the future directions based on the latest research.

Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications

Artificial intelligence (AI) has emerged as a transformative tool across several specialties, namely gastroenterology, where it has the potential to optimize both diagnosis and treatment as well as enhance patient care. Coloproctology, due to its highly prevalent pathologies and tremendous potential to cause significant mortality and morbidity, has drawn a lot of attention regarding AI applications. In fact, its application has yielded impressive outcomes in various domains, colonoscopy being one prominent example, where it aids in the detection of polyps and early signs of colorectal cancer with high accuracy and efficiency. With a less explored path but equivalent promise, AI-powered capsule endoscopy ensures accurate and time-efficient video readings, already detecting a wide spectrum of anomalies. High-resolution anoscopy is an area that has been growing in interest in recent years, with efforts being made to integrate AI. There are other areas, such as functional studies, that are currently in the early stages, but evidence is expected to emerge soon. According to the current state of research, AI is anticipated to empower gastroenterologists in the decision-making process, paving the way for a more precise approach to diagnosing and treating patients. This review aims to provide the state-of-the-art use of AI in coloproctology while also reflecting on future directions and perspectives.

Artificial intelligence in endoscopy related to inflammatory bowel disease: A systematic review

BACKGROUND AND OBJECTIVES

In spite of rapid growth of artificial intelligence (AI) in digestive endoscopy in lesion detection and characterization, the role of AI in inflammatory bowel disease (IBD) endoscopy is not clearly defined. We aimed at systematically reviewing the role of AI in IBD endoscopy and identifying future research areas.

METHODS

We searched the PubMed and Embase database using keywords ("artificial intelligence" OR "machine learning" OR "computer-aided" OR "convolutional neural network") AND ("inflammatory bowel disease" OR "ulcerative colitis" OR "Crohn's") AND ("endoscopy" or "colonoscopy" or "capsule endoscopy" or "device assisted enteroscopy") between 1975 and September 2023 and identified 62 original articles for detailed review. Review articles, consensus guidelines, case reports/series, editorials, letter to the editor, non-peer-reviewed pre-prints and conference abstracts were excluded. The quality of the included studies was assessed using the MI-CLAIM checklist.

RESULTS

The accuracy of AI models (25 studies) to assess ulcerative colitis (UC) endoscopic activity ranged between 86.54% and 94.5%. AI-assisted capsule endoscopy reading (12 studies) substantially reduced analyzable images and reading time with excellent accuracy (90.5% to 99.9%). AI-assisted analysis of colonoscopic images can help differentiate IBD from non-IBD, UC from non-UC and UC from Crohn's disease (CD) (three studies) with 72.1%, 98.3% and > 90% accuracy, respectively. AI models based on non-invasive clinical and radiologic parameters could predict endoscopic activity (three studies). AI-assisted virtual chromoendoscopy (four studies) could predict histologic remission and long-term outcomes. Computer-assisted detection (CADe) of dysplasia (two studies) is feasible along with AI-based differentiation of high from low-grade IBD neoplasia (79% accuracy). AI is effective in linking electronic medical record data (two studies) with colonoscopic videos to facilitate widespread machine learning.

CONCLUSION

AI-assisted IBD endoscopy has the potential to impact clinical management by automated detection and characterization of endoscopic lesions. Large, multi-center, prospective studies and commercially available IBD-specific endoscopic AI algorithms are warranted.