Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies

The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.

Kyoto classification of gastritis, virtual chromoendoscopy and artificial intelligence: Where are we going? What do we need?

Chronic gastritis (CG) is a widespread and frequent disease, mainly caused by Helicobacter pylori infection, which is associated with an increased risk of gastric cancer. Virtual chromoendoscopy improves the endoscopic diagnostic efficacy, which is essential to establish the most appropriate therapy and to enable cancer prevention. Artificial intelligence provides algorithms for the diagnosis of gastritis and, in particular, early gastric cancer, but it is not yet used in practice. Thus, technological innovation, through image resolution and processing, optimizes the diagnosis and management of CG and gastric cancer. The endoscopic Kyoto classification of gastritis improves the diagnosis and management of this disease, but through the analysis of the most recent literature, new algorithms can be proposed.

Artificial intelligence for characterization of diminutive colorectal polyps: A feasibility study comparing two computer-aided diagnosis systems

BACKGROUND

Artificial intelligence (AI) has potential in the optical diagnosis of colorectal polyps.

AIM

To evaluate the feasibility of the real-time use of the computer-aided diagnosis system (CADx) AI for ColoRectal Polyps (AI4CRP) for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYE^TM (Fujifilm, Tokyo, Japan). CADx influence on the optical diagnosis of an expert endoscopist was also investigated.

METHODS

AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm. Both CADx-systems exploit convolutional neural networks. Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard. AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value (range 0.0-1.0). A predefined cut-off value of 0.6 was set with values < 0.6 indicating benign and values ≥ 0.6 indicating premalignant colorectal polyps. Low confidence characterizations were defined as values 40% around the cut-off value of 0.6 (< 0.36 and > 0.76). Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.

RESULTS

AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps. Self-critical AI4CRP, excluding 14 low confidence characterizations [27.5% (14/51)], had a diagnostic accuracy of 89.2%, sensitivity of 89.7%, and specificity of 87.5%, which was higher compared to AI4CRP. CAD EYE had a 83.7% diagnostic accuracy, 74.2% sensitivity, and 100.0% specificity. Diagnostic performances of the endoscopist alone (before AI) increased non-significantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE (AI-assisted endoscopist). Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems, except for specificity for which CAD EYE performed best.

CONCLUSION

Real-time use of AI4CRP was feasible. Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.

Progress and prospects of artificial intelligence in colonoscopy

Artificial intelligence (AI) is a branch of computer science. As a new technological science, it mainly develops and expands human intelligence through the research of intelligence theory, methods and technology. In the medical field, AI has bright application prospects (for example: imaging, diagnosis and treatment). The exploration of robotic gastroscopy and colonoscopy systems is not only a bold attempt, but also an inevitable trend of AI in the development of digestive endoscopy in the future. Based on the current research findings, this article summarizes the research progress of colonoscopy, and looking forward for the application of AI in colonoscopy.

Delivering a high-quality colonoscopy service fit for the 21st century

Colorectal cancer (CRC) is the third most prevalent cancer globally. There is a concerning increase in its incidence among younger individuals. Colonoscopy remains the gold standard for CRC diagnosis. With the introduction of population-based bowel screening and increased public awareness, there has been a significant rise in referrals for colonoscopy. Healthcare providers worldwide will need to strategically evaluate how to allocate resources to adequately train the next generation of colonoscopists who will need to provide accurate endoscopic assessment and treatment for premalignant polyps and early CRC. This review outlines the current workload challenges faced by colonoscopists whilst exploring emerging technologies such as artificial intelligence for adenoma detection. Additionally, advanced endoscopic surgical techniques like endoscopic submucosal dissection are discussed.

Ultra-minimally invasive endoscopic techniques and colorectal diseases: Current status and its future

Colorectal diseases are increasing due to altered lifestyle, genetic, and environmental factors. Colonoscopy plays an important role in diagnosis. Advances in colonoscope (ultrathin scope, magnetic scope, capsule) and technological gadgets (Balloon assisted scope, third eye retroscope, NaviAid G-EYE, dye-based chromoendoscopy, virtual chromoendoscopy, narrow band imaging, i-SCAN, etc.) have made colonoscopy more comfortable and efficient. Now in-vivo microscopy can be performed using confocal laser endomicroscopy, optical coherence tomography, spectroscopy, etc. Besides developments in diagnostic colonoscopy, therapeutic colonoscopy has improved to manage lower gastrointestinal tract bleeding, obstruction, perforations, resection polyps, and early colorectal cancers. The introduction of combined endo-laparoscopic surgery and robotic endoscopic surgery has made these interventions feasible. The role of artificial intelligence in the diagnosis and management of colorectal diseases is also increasing day by day. Hence, this article is to review cutting-edge developments in endoscopic principles for the management of colorectal diseases.

Artificial intelligence: Applications in critical care gastroenterology

Gastrointestinal (GI) complications frequently necessitate intensive care unit (ICU) admission. Additionally, critically ill patients also develop GI complications requiring further diagnostic and therapeutic interventions. However, these patients form a vulnerable group, who are at risk for developing side effects and complications. Every effort must be made to reduce invasiveness and ensure safety of interventions in ICU patients. Artificial intelligence (AI) is a rapidly evolving technology with several potential applications in healthcare settings. ICUs produce a large amount of data, which may be employed for creation of AI algorithms, and provide a lucrative opportunity for application of AI. However, the current role of AI in these patients remains limited due to lack of large-scale trials comparing the efficacy of AI with the accepted standards of care.

Application of convolutional neural network in detecting and classifying gastric cancer

Gastric cancer (GC) is the fifth most common cancer in the world, and at present, esophagogastroduodenoscopy is recognized as an acceptable method for the screening and monitoring of GC. Convolutional neural networks (CNNs) are a type of deep learning model and have been widely used for image analysis. This paper reviews the application and prospects of CNNs in detecting and classifying GC, aiming to introduce a computer-aided diagnosis system and to provide evidence for subsequent studies.

Artificial intelligence fails to improve colonoscopy quality: A single centre retrospective cohort study

BACKGROUND

Limited data currently exists on the clinical utility of Artificial Intelligence Assisted Colonoscopy (AIAC) outside of clinical trials.

AIM

To evaluate the impact of AIAC on key markers of colonoscopy quality compared to conventional colonoscopy (CC).

METHODS

This single-centre retrospective observational cohort study included all patients undergoing colonoscopy at a secondary centre in Brisbane, Australia. CC outcomes between October 2021 and October 2022 were compared with AIAC outcomes after the introduction of the Olympus Endo-AID module from October 2022 to January 2023. Endoscopists who conducted over 50 procedures before and after AIAC introduction were included. Procedures for surveillance of inflammatory bowel disease were excluded. Patient demographics, proceduralist specialisation, indication for colonoscopy, and colonoscopy quality metrics were collected. Adenoma detection rate (ADR) and sessile serrated lesion detection rate (SSLDR) were calculated for both AIAC and CC.

RESULTS

The study included 746 AIAC procedures and 2162 CC procedures performed by seven endoscopists. Baseline patient demographics were similar, with median age of 60 years with a slight female predominance (52.1%). Procedure indications, bowel preparation quality, and caecal intubation rates were comparable between groups. AIAC had a slightly longer withdrawal time compared to CC, but the difference was not statistically significant. The introduction of AIAC did not significantly change ADR (52.1% for AIAC vs 52.6% for CC, P = 0.91) or SSLDR (17.4% for AIAC vs 18.1% for CC, P = 0.44).

CONCLUSION

The implementation of AIAC failed to improve key markers of colonoscopy quality, including ADR, SSLDR and withdrawal time. Further research is required to assess the utility and cost-efficiency of AIAC for high performing endoscopists.

Endoscopic ultrasound-guided gastroenterostomy: The new standard treatment of gastric outlet obstruction

Endoscopic ultrasound-guided gastroenterostomy (EUS-GE) has emerged as an effective and minimally invasive alternative for treating gastric outlet obstruction. Compared to traditional options, including duodenal stenting and surgical gastrojejunostomy, EUS-GE offers comparable technical and clinical success while providing longer-lasting patency, fewer adverse events, and lower reintervention rates. The technique has expanded beyond malignant obstruction to include benign etiologies and complex conditions such as afferent loop syndrome. EUS-GE enables rapid recovery and early resumption of oral intake, which is crucial for oncologic patients. However, the procedure remains technically demanding, and optimal techniques, device selection, and management of complications are still under investigation. This mini-review summarizes current evidence, compares EUS-GE with alternative therapies, discusses patient selection and procedural aspects, and outlines key areas for future research.

Virtual reality tools for training in gastrointestinal endoscopy: A systematic review

BACKGROUND

Virtual reality (VR) has emerged as an innovative technology in endoscopy training, providing a simulated environment that closely resembles real-life scenarios and offering trainees a valuable platform to acquire and enhance their endoscopic skills. This systematic review will critically evaluate the effectiveness and feasibility of VR-based training compared to traditional methods.

AIM

To evaluate the effectiveness and feasibility of VR-based training compared to traditional methods. By examining the current state of the field, this review seeks to identify gaps, challenges, and opportunities for further research and implemen-tation of VR in endoscopic training.

METHODS

The study is a systematic review, following the guidelines for reporting systematic reviews set out by the PRISMA statement. A comprehensive search command was designed and implemented and run in September 2023 to identify relevant studies available, from electronic databases such as PubMed, Scopus, Cochrane, and Google Scholar. The results were systematically reviewed.

RESULTS

Sixteen articles were included in the final analysis. The total number of participants was 523. Five studies focused on both upper endoscopy and colonoscopy training, two on upper endoscopy training only, eight on colon-oscopy training only, and one on sigmoidoscopy training only. Gastro-intestinal Mentor virtual endoscopy simulator was commonly used. Fifteen reported positive results, indicating that VR-based training was feasible and acceptable for endoscopy learners. VR technology helped the trainees enhance their skills in manipulating the endoscope, reducing the procedure time or increasing the technical accuracy, in VR scenarios and real patients. Some studies show that the patient discomfort level decreased significantly. However, some studies show there were no significant differences in patient discomfort and pain scores between VR group and other groups.

CONCLUSION

VR training is effective for endoscopy training. There are several well-designed randomized controlled trials with large sample sizes, proving the potential of this innovative tool. Thus, VR should be more widely adopted in endoscopy training. Furthermore, combining VR training with conventional methods could be a promising approach that should be implemented in training.

Artificial intelligence in the endoscopic approach of biliary tract diseases: A current review

In recent years there have been major developments in the field of artificial intelligence. The different areas of medicine have taken advantage of this tool to make various diagnostic and therapeutic methods more effective, safe, and user-friendly. In this way, artificial intelligence has been an increasingly present reality in medicine. In the field of Gastroenterology, the main application has been in the detection and characterization of colonic polyps, but an increasing number of studies have been published on the application of deep learning systems in other pathologies of the gastrointestinal tract. Evidence of the application of artificial intelligence in the assessment of biliary tract is still scarce. Some studies support the usefulness of these systems in the investigation and treatment of choledocholithiasis, demonstrating that they have the potential to be integrated into clinical practice and endoscopic procedures, such as endoscopic retrograde cholangiopancreatography. Its application in cholangioscopy for the investigation of undetermined biliary strictures also seems to be promising. Assessing the bile duct through endoscopic ultrasound can be challenging, especially for less experienced operators, thus becoming an area of potential interest for artificial intelligence. In this review, we summarize the state of the art of artificial intelligence in the endoscopic diagnosis and treatment of biliary diseases.