From Staining Techniques to Artificial Intelligence: A Review of Colorectal Polyps Characterization.

This review article provides a comprehensive overview of the evolving techniques in image-enhanced endoscopy (IEE) for the characterization of colorectal polyps, and the potential of artificial intelligence (AI) in revolutionizing the diagnostic accuracy of endoscopy. We discuss the historical use of dye-spray and virtual chromoendoscopy for the characterization of colorectal polyps, which are now being replaced with more advanced technologies. Specifically, we focus on the application of AI to create a "virtual biopsy" for the detection and characterization of colorectal polyps, with potential for replacing histopathological diagnosis. The incorporation of AI has the potential to provide an evolutionary learning system that aids in the diagnosis and management of patients with the best possible outcomes. A detailed analysis of the literature supporting AI-assisted diagnostic techniques for the detection and characterization of colorectal polyps, with a particular emphasis on AI's characterization mechanism, is provided. The benefits of AI over traditional IEE techniques, including the reduction in human error in diagnosis, and its potential to provide an accurate diagnosis with similar accuracy to the gold standard are presented. However, the need for large-scale testing of AI in clinical practice and the importance of integrating patient data into the diagnostic process are acknowledged. In conclusion, the constant evolution of IEE technology and the potential for AI to revolutionize the field of endoscopy in the future are presented.

Lack of Effectiveness of Computer Aided Detection for Colorectal Neoplasia: A Systematic Review and Meta-Analysis of Nonrandomized Studies.

BACKGROUND AND AIMS: Benefits of computer-aided detection (CADe) in detecting colorectal neoplasia were shown in many randomized trials in which endoscopists' behavior was strictly controlled. However, the effect of CADe on endoscopists' performance in less-controlled setting is unclear. This systematic review and meta-analyses were aimed at clarifying benefits and harms of using CADe in real-world colonoscopy. METHODS: We searched MEDLINE, EMBASE, Cochrane, and Google Scholar from inception to August 20, 2023. We included nonrandomized studies that compared the effectiveness between CADe-assisted and standard colonoscopy. Two investigators independently extracted study data and quality. Pairwise meta-analysis was performed utilizing risk ratio for dichotomous variables and mean difference (MD) for continuous variables with a 95% confidence interval (CI). RESULTS: Eight studies were included, comprising 9782 patients (4569 with CADe and 5213 without CADe). Regarding benefits, there was a difference in neither adenoma detection rate (44% vs 38%; risk ratio, 1.11; 95% CI, 0.97 to 1.28) nor mean adenomas per colonoscopy (0.93 vs 0.79; MD, 0.14; 95% CI, -0.04 to 0.32) between CADe-assisted and standard colonoscopy, respectively. Regarding harms, there was no difference in the mean non-neoplastic lesions per colonoscopy (8 studies included for analysis; 0.52 vs 0.47; MD, 0.14; 95% CI, -0.07 to 0.34) and withdrawal time (6 studies included for analysis; 14.3 vs 13.4 minutes; MD, 0.8 minutes; 95% CI, -0.18 to 1.90). There was a substantial heterogeneity, and all outcomes were graded with a very low certainty of evidence. CONCLUSION: CADe in colonoscopies neither improves the detection of colorectal neoplasia nor increases burden of colonoscopy in real-world, nonrandomized studies, questioning the generalizability of the results of randomized trials.

SIED-GISCOR recommendations for colonoscopy in screening programs: Part I - Diagnostic.

The implementation of FIT programs reduces incidence and mortality from CRC in the screened subjects. The ultimate efficacy for CRC morbidity and mortality prevention in a FIT program depends on the colonoscopy in FIT+ subjects that has the task of detecting and removing these advanced lesions. Recently, there has been growing evidence on factors that influence the quality of colonoscopy specifically withing organized FIT programs, prompting to dedicated interventions in order to maximize the benefit/harm ratio of post-FIT colonoscopy. This document focuses on the diagnostic phase of colonoscopy, providing indications on how to standardise colonoscopy in FIT+ subjects, regarding timing of examination, management of antithrombotic therapy, bowel preparation, competence and sedation.

Microbiota and IBD: Current knowledge and future perspectives.

Inflammatory Bowel Disease (IBD) is a chronic relapsing-remitting disease with a remarkable increase in incidence worldwide and a substantial disease burden. Although the pathophysiology is not fully elucidated yet an aberrant immune reaction against the intestinal microbiota and the gut microbial dysbiosis have been identified to play a major role. The composition of gut microbiota in IBD patients is distinct from that of healthy individuals, with certain organisms predominating over others. Differences in the microbial dysbiosis have been also observed between Crohn Disease (CD) and Ulcerative Colitis (UC). A disruption of the microbiota's balance can lead to inflammation and intestinal damage. Microbiota composition in IBD can be affected both by endogenous (i.e., interaction with the immune system and intestinal epithelial cells) and exogenous (i.e., medications, surgery, diet) factors. The complex interplay between the gut microbiota and IBD is an area of great interest for understanding disease pathogenesis and developing new treatments. The purpose of this review is to summarize the latest evidence on the role of microbiota in IBD pathogenesis and to explore possible future areas of research.

Exploring a novel composite method using non-contrast EUS enhanced microvascular imaging and cyst fluid analysis to differentiate pancreatic cystic lesions.

BACKGROUND AND AIMS: Differentiating pancreatic cystic lesions (PCLs) remains a diagnostic challenge. The use of high-definition imaging modalities which detect tumor microvasculature have been described in solid lesions. We aim to evaluate the usefulness of cystic microvasculature when used in combination with cyst fluid biochemistry to differentiate PCLs. METHODS: We retrospectively analyzed 110 consecutive patients with PCLs from 2 Italian Hospitals who underwent EUS with H-Flow and EUS fine needle aspiration to obtain cystic fluid. The accuracy of fluid biomarkers was evaluated against morphological features on radiology and EUS. Gold standard for diagnosis was surgical resection. A clinical and radiological follow up was applied in those patients who were not resected because not surgical indication and no signs of malignancy were shown. RESULTS: Of 110 patients, 65 were diagnosed with a mucinous cyst, 41 with a non-mucinous cyst, and 4 with an undetermined cyst. Fluid analysis alone yielded 76.7% sensitivity, 56.7% specificity, 77.8 positive predictive value (PPV), 55.3 negative predictive value (NPV) and 56% accuracy in diagnosing pancreatic cysts alone. Our composite method yielded 97.3% sensitivity, 77.1% specificity, 90.1% PPV, 93.1% NPV, 73.2% accuracy. CONCLUSIONS: This new composite could be applied to the holistic approach of combining cyst morphology, vascularity, and fluid analysis alongside endoscopist expertise.

Real-Time Computer-Aided Detection of Colorectal Neoplasia During Colonoscopy : A Systematic Review and Meta-analysis.

BACKGROUND: Artificial intelligence computer-aided detection (CADe) of colorectal neoplasia during colonoscopy may increase adenoma detection rates (ADRs) and reduce adenoma miss rates, but it may increase overdiagnosis and overtreatment of nonneoplastic polyps. PURPOSE: To quantify the benefits and harms of CADe in randomized trials. DESIGN: Systematic review and meta-analysis. (PROSPERO: CRD42022293181). DATA SOURCES: Medline, Embase, and Scopus databases through February 2023. STUDY SELECTION: Randomized trials comparing CADe-assisted with standard colonoscopy for polyp and cancer detection. DATA EXTRACTION: Adenoma detection rate (proportion of patients with ≥1 adenoma), number of adenomas detected per colonoscopy, advanced adenoma (≥10 mm with high-grade dysplasia and villous histology), number of serrated lesions per colonoscopy, and adenoma miss rate were extracted as benefit outcomes. Number of polypectomies for nonneoplastic lesions and withdrawal time were extracted as harm outcomes. For each outcome, studies were pooled using a random-effects model. Certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework. DATA SYNTHESIS: Twenty-one randomized trials on 18 232 patients were included. The ADR was higher in the CADe group than in the standard colonoscopy group (44.0% vs. 35.9%; relative risk, 1.24 [95% CI, 1.16 to 1.33]; low-certainty evidence), corresponding to a 55% (risk ratio, 0.45 [CI, 0.35 to 0.58]) relative reduction in miss rate (moderate-certainty evidence). More nonneoplastic polyps were removed in the CADe than the standard group (0.52 vs. 0.34 per colonoscopy; mean difference [MD], 0.18 polypectomy [CI, 0.11 to 0.26 polypectomy]; low-certainty evidence). Mean inspection time increased only marginally with CADe (MD, 0.47 minute [CI, 0.23 to 0.72 minute]; moderate-certainty evidence). LIMITATIONS: This review focused on surrogates of patient-important outcomes. Most patients, however, may consider cancer incidence and cancer-related mortality important outcomes. The effect of CADe on such patient-important outcomes remains unclear. CONCLUSION: The use of CADe for polyp detection during colonoscopy results in increased detection of adenomas but not advanced adenomas and in higher rates of unnecessary removal of nonneoplastic polyps. PRIMARY FUNDING SOURCE: European Commission Horizon 2020 Marie Sklodowska-Curie Individual Fellowship.

A Comprehensive Guide to Artificial Intelligence in Endoscopic Ultrasound.

BACKGROUND: Endoscopic Ultrasound (EUS) is widely used for the diagnosis of bilio-pancreatic and gastrointestinal (GI) tract diseases, for the evaluation of subepithelial lesions, and for sampling of lymph nodes and solid masses located next to the GI tract. The role of Artificial Intelligence in healthcare in growing. This review aimed to provide an overview of the current state of AI in EUS from imaging to pathological diagnosis and training. METHODS: AI algorithms can assist in lesion detection and characterization in EUS by analyzing EUS images and identifying suspicious areas that may require further clinical evaluation or biopsy sampling. Deep learning techniques, such as convolutional neural networks (CNNs), have shown great potential for tumor identification and subepithelial lesion (SEL) evaluation by extracting important features from EUS images and using them to classify or segment the images. RESULTS: AI models with new features can increase the accuracy of diagnoses, provide faster diagnoses, identify subtle differences in disease presentation that may be missed by human eyes, and provide more information and insights into disease pathology. CONCLUSIONS: The integration of AI in EUS images and biopsies has the potential to improve the diagnostic accuracy, leading to better patient outcomes and to a reduction in repeated procedures in case of non-diagnostic biopsies.

Current and future implications of artificial intelligence in colonoscopy.

Gastrointestinal endoscopy has proved to be a perfect context for the development of artificial intelligence (AI) systems that can aid endoscopists in many tasks of their daily activities. Lesion detection (computer-aided detection, CADe) and lesion characterization (computer-aided characterization, CADx) during colonoscopy are the clinical applications of AI in gastroenterology for which by far the most evidence has been published. Indeed, they are the only applications for which more than one system has been developed by different companies, is currently available on the market, and may be used in clinical practice. Both CADe and CADx, alongside hopes and hypes, come with potential drawbacks, limitations and dangers that must be known, studied and researched as much as the optimal uses of these machines, aiming to stay one step ahead of the possible misuse of what will always be an aid to the clinician and never a substitute. An AI revolution in colonoscopy is on the way, but the potential uses are infinite and only a fraction of them have currently been studied. Future applications can be designed to ensure all aspects of colonoscopy quality parameters and truly deliver a standardization of practice, regardless of the setting in which the procedure is performed. In this review, we cover the available clinical evidence on AI applications in colonoscopy and offer an overview of future directions.

Diagnostic accuracy and safety of EUS-guided end-cutting fine-needle biopsy needles for tissue sampling of abdominal and mediastinal lymphadenopathies: a prospective multicenter series.

BACKGROUND AND AIMS: The role of the newer EUS fine-needle biopsy needles in lymphadenopathies (LAs) is still under evaluation. We aimed to evaluate the diagnostic accuracy and adverse event rate of EUS-guided fine-needle biopsy sampling (EUS-FNB) in diagnosing LAs. METHODS: From June 2015 to June 2022, all patients referred to 4 institutions for EUS-FNB of mediastinal and abdominal LAs were enrolled. Twenty-two-gauge Franseen tip or 25-gauge fork-tip needles were used. The criterion standard for positive results was surgery or imaging and clinical evolution over a follow-up of at least 1 year. RESULTS: One hundred consecutive patients were enrolled, consisting of those with a new diagnosis of LA (40%), presence of LA with a previous history of neoplasia (51%), or suspected lymphoproliferative disease (9%). EUS-FNB was technically feasible in all LA patients with 2 to 3 passes (mean, 2.62 ± .93). The overall sensitivity, positive predictive value, specificity, negative predictive value, and accuracy for EUS-FNB were 96.20%, 100%, 100%, 87.50%, and 97.00%, respectively. Histologic analysis was feasible in 89% of cases. Cytologic evaluation was performed in 67% of specimens. A statistical difference between the accuracy of the 22-gauge or 25-gauge needle (P = .63) was not found. A subanalysis on lymphoproliferative disease revealed a sensitivity and accuracy of 89.29% and 90.0%, respectively. No adverse events were recorded. CONCLUSIONS: EUS-FNB with new end-cutting needles is a valuable and safe method to diagnose LAs. The high quality of histologic cores and the good amount of tissue allowed a complete immunohistochemical analysis of metastatic LAs and precise subtyping of the lymphomas. (Clinical trial registration number: NCT02855151.).