Machine learning model to predict recurrent ulcer bleeding in patients with history of idiopathic gastroduodenal ulcer bleeding

Designing Chinese hospital emergency departments to leverage artificial intelligence-a systematic literature review on the challenges and opportunities

Artificial intelligence (AI) has witnessed rapid advances in the healthcare domain in recent years, especially in the emergency field, where AI is likely to radically reshape medical service delivery. Although AI has substantial potential to enhance diagnostic accuracy and operational efficiency in hospitals, research on its applications in Emergency Department building design remains relatively scarce. Therefore, this study aims to investigate Emergency Department facility design by identifying the challenges and opportunities of using AI. Two systematic literature reviews are combined, one in AI and the other in sensors, to explore their potential application to support decision-making, resource optimisation and patient monitoring. These reviews have then informed a discussion on integrating AI sensors in contemporary Emergency Department designs for use in China to support the evidence base on resuscitation units, emergency operating rooms and Emergency Department Intensive Care Unit (ED-ICU) design. We hope to inform the strategic implementation of AI sensors and how they might transform Emergency Department design to support medical staff and enhance the patient experience.

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.

Progress in research of risk prediction models for upper gastrointestinal bleeding

Upper gastrointestinal bleeding is a common gastrointes-tinal condition with a high incidence rate and great harm to the body. This paper reviews the rebleeding, endos-copic intervention, and death risk prediction models for upper gastrointestinal bleeding, including the study population, research methods, related risk factors, and model performance, in order to provide reference for clinicians to conduct risk assessment as soon as possible, formulate effective prevention and management plans, and improve patient survival outcomes.

Robust Ulcer Classification: Contrast and Illumination Invariant Approach

Gastrointestinal (GI) disease cases are on the rise throughout the world. Ulcers, being the most common type of GI disease, if left untreated, can cause internal bleeding resulting in anemia and bloody vomiting. Early detection and classification of different types of ulcers can reduce the death rate and severity of the disease. Manual detection and classification of ulcers are tedious and error-prone. This calls for automated systems based on computer vision techniques to detect and classify ulcers in images and video data. A major challenge in accurate detection and classification is dealing with the similarity among classes and the poor quality of input images. Improper contrast and illumination reduce the anticipated classification accuracy. In this paper, contrast and illumination invariance was achieved by utilizing log transformation and power law transformation. Optimal values of the parameters for both these techniques were achieved and combined to obtain the fused image dataset. Augmentation was used to handle overfitting and classification was performed using the lightweight and efficient deep learning model MobilNetv2. Experiments were conducted on the KVASIR dataset to assess the efficacy of the proposed approach. An accuracy of 96.71% was achieved, which is a considerable improvement over the state-of-the-art techniques.

Effectiveness of prophylactic clipping in preventing postpolypectomy bleeding in oral anticoagulant users: a propensity-score analysis

BACKGROUND AND AIMS

Evidence of prophylactic clipping is inconsistent except for proximal and large colonic lesions in the general population. Although warfarin and direct oral anticoagulants (DOACs) are significant risk factors of postpolypectomy bleeding (PPB), dedicated studies to examine the benefit of prophylactic clipping in these high-risk patients remain limited.

METHODS

We performed a propensity score-weighted retrospective cohort study from 2012 to 2020. Patients who received an oral anticoagulant and underwent colonoscopic polypectomy were included. Data were collected on baseline demographics, medications (anticoagulant, antiplatelet, and heparin bridging), and endoscopies (polyp number, location, size, morphology, histopathology, resection method and prophylactic clipping). Propensity-score models with inverse probability of treatment weighting were developed between prophylactic clipping and no clipping groups. Unbalanced variables were included in a doubly robust model with multivariate analysis. The primary outcome was clinically significant delayed PPB, defined as a composite endpoint of hemoglobin drop ≥2 g/dL, blood transfusion, or repeat colonoscopy for hemostasis within 30 days.

RESULTS

Five hundred forty-seven patients with 1485 polyps were included. Prophylactic clipping was not associated with a reduced risk of PPB (odds ratio [OR], 1.19; 95% confidence interval [CI], .73-1.95; P = .497). The hot resection method was associated with a significantly higher risk of PPB (OR, 9.76; 95% CI, 3.94-32.60; P < .001) compared with cold biopsy or snare polypectomy. In a subgroup analysis, prophylactic clipping was associated with a lower PPB risk in patients on DOACs (OR, .36; 95% CI, .16-.82; P = .015).

CONCLUSIONS

Prophylactic clipping was not associated with an overall reduced risk of PPB in patients on oral anticoagulants. The use of cold snare polypectomy should be maximized in anticoagulated patients.

Utility of a deep learning model and a clinical model for predicting bleeding after endoscopic submucosal dissection in patients with early gastric cancer

BACKGROUND

Bleeding is one of the major complications after endoscopic submucosal dissection (ESD) in early gastric cancer (EGC) patients. There are limited studies on estimating the bleeding risk after ESD using an artificial intelligence system.

AIM

To derivate and verify the performance of the deep learning model and the clinical model for predicting bleeding risk after ESD in EGC patients.

METHODS

Patients with EGC who underwent ESD between January 2010 and June 2020 at the Samsung Medical Center were enrolled, and post-ESD bleeding (PEB) was investigated retrospectively. We split the entire cohort into a development set (80%) and a validation set (20%). The deep learning and clinical model were built on the development set and tested in the validation set. The performance of the deep learning model and the clinical model were compared using the area under the curve and the stratification of bleeding risk after ESD.

RESULTS

A total of 5629 patients were included, and PEB occurred in 325 patients. The area under the curve for predicting PEB was 0.71 (95% confidence interval: 0.63-0.78) in the deep learning model and 0.70 (95% confidence interval: 0.62-0.77) in the clinical model, without significant difference (P = 0.730). The patients expected to the low- (< 5%), intermediate- (≥ 5%, < 9%), and high-risk (≥ 9%) categories were observed with actual bleeding rate of 2.2%, 3.9%, and 11.6%, respectively, in the deep learning model; 4.0%, 8.8%, and 18.2%, respectively, in the clinical model.

CONCLUSION

A deep learning model can predict and stratify the bleeding risk after ESD in patients with EGC.

Artificial Intelligence and the Future of Gastroenterology and Hepatology

The integration of artificial intelligence (AI) into gastroenterology and hepatology (GI) will inevitably transform the practice of GI in the coming decade. While the application of AI in health care is not new, advancements are occurring rapidly, and the future landscape of AI is beginning to come into focus. From endoscopic assistance via computer vision technology to the predictive capabilities of the vast information contained in the electronic health records, AI promises to optimize and expedite clinical and procedural practice and research in GI. The extensive body of literature already available on AI applications in gastroenterology may seem daunting at first; however, this review aims to provide a breakdown of the key studies conducted thus far and demonstrate the many potential ways this technology may impact the field. This review will also take a look into the future and imagine how GI can be transformed over the coming years, as well as potential limitations and pitfalls that must be overcome to realize this future.

Current advancements in application of artificial intelligence in clinical decision-making by gastroenterologists in gastrointestinal bleeding

Artificial Intelligence (AI) is a type of intelligence that comes from machines or computer systems that mimics human cognitive function. Recently, AI has been utilized in medicine and helped clinicians make clinical decisions. In gastroenterology, AI has assisted colon polyp detection, optical biopsy, and diagnosis of Helicobacter pylori infection. AI also has a broad role in the clinical prediction and management of gastrointestinal bleeding. Machine learning can determine the clinical risk of upper and lower gastrointestinal bleeding. AI can assist the management of gastrointestinal bleeding by identifying high-risk patients who might need urgent endoscopic treatment or blood transfusion, determining bleeding stigmata during endoscopy, and predicting recurrence of gastrointestinal bleeding. The present review will discuss the role of AI in the clinical prediction and management of gastrointestinal bleeding, primarily on how it could assist gastroenterologists in their clinical decision-making compared to conventional methods. This review will also discuss challenges in implementing AI in routine practice.

Development and validation of a model to predict rebleeding within three days after endoscopic hemostasis for high-risk peptic ulcer bleeding

Background

Peptic ulcer bleeding remains a typical medical emergency with significant morbidity and mortality. Peptic ulcer rebleeding often occurs within three days after emergent endoscopic hemostasis. Our study aims to develop a nomogram to predict rebleeding within three days after emergent endoscopic hemostasis for high-risk peptic ulcer bleeding.

Methods

We retrospectively reviewed the data of 386 patients with bleeding ulcers and high-risk stigmata who underwent emergent endoscopic hemostasis between March 2014 and October 2018. The least absolute shrinkage and selection operator method was used to identify predictors. The model was displayed as a nomogram. Internal validation was carried out using bootstrapping. The model was evaluated using the calibration plot, decision-curve analyses, and clinical impact curve.

Results

Overall, 386 patients meeting the inclusion criteria were enrolled, with 48 patients developed rebleeding within three days after initial endoscopic hemostasis. Predictors contained in the nomogram included albumin, prothrombin time, shock, haematemesis/melena and Forrest classification. The model showed good discrimination and good calibration with a C-index of 0.854 (C-index: 0.830 via bootstrapping validation). Decision-curve analyses and clinical impact curve also demonstrated that it was clinically valuable.

Conclusion

This study presents a nomogram that incorporates clinical, laboratory, and endoscopic features, effectively predicting rebleeding within three days after emergent endoscopic hemostasis and identifying high-risk rebleeding patients with peptic ulcer bleeding.

Trial registration This clinical trial has been registered in the ClinicalTrials.gov (ID: NCT04895904) approved by the International Committee of Medical Journal Editors (ICMJE).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02145-9.

Application Status and Prospects of Artificial Intelligence in Peptic Ulcers

Peptic ulcer (PU) is a common and frequently occurring disease. Although PU seriously threatens the lives and health of global residents, the applications of artificial intelligence (AI) have strongly promoted diversification and modernization in the diagnosis and treatment of PU. This minireview elaborates on the research progress of AI in the field of PU, from PU's pathogenic factor Helicobacter pylori (Hp) infection, diagnosis and differential diagnosis, to its management and complications (bleeding, obstruction, perforation and canceration). Finally, the challenges and prospects of AI application in PU are prospected and expounded. With the in-depth understanding of modern medical technology, AI remains a promising option in the management of PU patients and plays a more indispensable role. How to realize the robustness, versatility and diversity of multifunctional AI systems in PU and conduct multicenter prospective clinical research as soon as possible are the top priorities in the future.

Risks of post-colonoscopic polypectomy bleeding and thromboembolism with warfarin and direct oral anticoagulants: a population-based analysis

BACKGROUND

There were limited data on the risk of post-polypectomy bleeding (PPB) in patients on direct oral anticoagulants (DOAC). We aimed to evaluate the PPB and thromboembolic risks among DOAC and warfarin users in a population-based cohort.

METHODS

We performed a territory-wide retrospective cohort study involving patients in Hong Kong from 2012 to 2020. Patients who received an oral anticoagulant and had undergone colonoscopy with polypectomy were identified. Propensity-score models with inverse probability of treatment weighting were developed for the warfarin-DOAC and between-DOAC comparisons. The primary outcome was clinically significant delayed PPB, defined as repeat colonoscopy requiring haemostasis within 30 days. The secondary outcomes were 30-day blood transfusion requirement and new thromboembolic event.

RESULTS

Apixaban was associated with lower PPB risk than warfarin (adjusted HR (aHR) 0.39, 95% CI 0.24 to 0.63, p<0.001). Dabigatran (aHR 2.23, 95% CI 1.04 to 4.77, adjusted p (ap)=0.035) and rivaroxaban (aHR 2.72, 95% CI 1.35 to 5.48, ap=0.002) were associated with higher PPB risk than apixaban. In subgroup analysis, apixaban was associated with lower PPB risk in patients aged ≥70 years and patients with right-sided colonic polyps.For thromboembolic events, apixaban was associated with lower risk than warfarin (aHR 0.22, 95% CI 0.11 to 0.45, p<0.001). Dabigatran (aHR 2.60, 95% CI 1.06 to 6.41, ap=0.033) and rivaroxaban (aHR 2.96, 95% CI 1.19 to 7.37, ap =0.013) were associated with higher thromboembolic risk than apixaban.

CONCLUSIONS

Apixaban was associated with a significantly lower risk of PPB and thromboembolism than warfarin, dabigatran and rivaroxaban, particularly in older patients with right-sided polyps.

Artificial intelligence in gastroenterology: A state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

WSES project on decision support systems based on artificial neural networks in emergency surgery

The article is a scoping review of the literature on the use of decision support systems based on artificial neural networks in emergency surgery. The authors present modern literature data on the effectiveness of artificial neural networks for predicting, diagnosing and treating abdominal emergency conditions: acute appendicitis, acute pancreatitis, acute cholecystitis, perforated gastric or duodenal ulcer, acute intestinal obstruction, and strangulated hernia. The intelligent systems developed at present allow a surgeon in an emergency setting, not only to check his own diagnostic and prognostic assumptions, but also to use artificial intelligence in complex urgent clinical cases. The authors summarize the main limitations for the implementation of artificial neural networks in surgery and medicine in general. These limitations are the lack of transparency in the decision-making process; insufficient quality educational medical data; lack of qualified personnel; high cost of projects; and the complexity of secure storage of medical information data. The development and implementation of decision support systems based on artificial neural networks is a promising direction for improving the forecasting, diagnosis and treatment of emergency surgical diseases and their complications.

Current Status and Future Perspective of Artificial Intelligence in the Management of Peptic Ulcer Bleeding: A Review of Recent Literature

With the decreasing incidence of peptic ulcer bleeding (PUB) over the past two decades, the clinician experience of managing patients with PUB has also declined, especially for young endoscopists. A patient with PUB management requires collaborative care involving the emergency department, gastroenterologist, radiologist, and surgeon, from initial assessment to hospital discharge. The application of artificial intelligence (AI) methods has remarkably improved people's lives. In particular, AI systems have shown great potential in many areas of gastroenterology to increase human performance. Colonoscopy polyp detection or diagnosis by an AI system was recently introduced for commercial use to improve endoscopist performance. Although PUB is a longstanding health problem, these newly introduced AI technologies may soon impact endoscopists' clinical practice by improving the quality of care for these patients. To update the current status of AI application in PUB, we reviewed recent relevant literature and provided future perspectives that are required to integrate such AI tools into real-world practice.

Artificial intelligence in small intestinal diseases: Application and prospects

The small intestine is located in the middle of the gastrointestinal tract, so small intestinal diseases are more difficult to diagnose than other gastrointestinal diseases. However, with the extensive application of artificial intelligence in the field of small intestinal diseases, with its efficient learning capacities and computational power, artificial intelligence plays an important role in the auxiliary diagnosis and prognosis prediction based on the capsule endoscopy and other examination methods, which improves the accuracy of diagnosis and prediction and reduces the workload of doctors. In this review, a comprehensive retrieval was performed on articles published up to October 2020 from PubMed and other databases. Thereby the application status of artificial intelligence in small intestinal diseases was systematically introduced, and the challenges and prospects in this field were also analyzed.

Risk Prediction of Barrett's Esophagus in a Taiwanese Health Examination Center Based on Regression Models

Determining the target population for the screening of Barrett's esophagus (BE), a precancerous condition of esophageal adenocarcinoma, remains a challenge in Asia. The aim of our study was to develop risk prediction models for BE using logistic regression (LR) and artificial neural network (ANN) methods. Their predictive performances were compared. We retrospectively analyzed 9646 adults aged ≥20 years undergoing upper gastrointestinal endoscopy at a health examinations center in Taiwan. Evaluated by using 10-fold cross-validation, both models exhibited good discriminative power, with comparable area under curve (AUC) for the LR and ANN models (Both AUC were 0.702). Our risk prediction models for BE were developed from individuals with or without clinical indications of upper gastrointestinal endoscopy. The models have the potential to serve as a practical tool for identifying high-risk individuals of BE among the general population for endoscopic screening.

Application of artificial intelligence to endoscopy on common gastrointestinal benign diseases

Artificial intelligence (AI) has been widely involved in every aspect of healthcare in the preclinical stage. In the digestive system, AI has been trained to assist auxiliary examinations including histopathology, endoscopy, ultrasonography, computerized tomography, and magnetic resonance imaging in detection, diagnosis, classification, differentiation, prognosis, and quality control. In the field of endoscopy, the application of AI, such as automatic detection, diagnosis, classification, and invasion depth, in early gastrointestinal (GI) cancers has received wide attention. There is a paucity of studies of AI application on common GI benign diseases based on endoscopy. In the review, we provide an overview of AI applications to endoscopy on common GI benign diseases including in the esophagus, stomach, intestine, and colon. It indicates that AI will gradually become an indispensable part of normal endoscopic detection and diagnosis of common GI benign diseases as clinical data, algorithms, and other related work are constantly repeated and improved.

Barrett esophagus: What to expect from Artificial Intelligence?

The evaluation and assessment of Barrett's esophagus is challenging for both expert and nonexpert endoscopists. However, the early diagnosis of cancer in Barrett's esophagus is crucial for its prognosis, and could save costs. Pre-clinical and clinical studies on the application of Artificial Intelligence (AI) in Barrett's esophagus have shown promising results. In this review, we focus on the current challenges and future perspectives of implementing AI systems in the management of patients with Barrett's esophagus.

Application of Artificial Intelligence in Gastrointestinal Endoscopy

Artificial intelligence (AI), also known as computer-aided diagnosis, is a technology that enables machines to process information and functions at or above human level and has great potential in gastrointestinal endoscopy applications. At present, the research on medical image recognition usually adopts the deep-learning algorithm based on the convolutional neural network. AI has been used in gastrointestinal endoscopy including esophagogastroduodenoscopy, capsule endoscopy, colonoscopy, etc. AI can help endoscopic physicians improve the diagnosis rate of various lesions, reduce the rate of missed diagnosis, improve the quality of endoscopy, assess the severity of the disease, and improve the efficiency of endoscopy. The diversity, susceptibility, and imaging specificity of gastrointestinal endoscopic images are all difficulties and challenges on the road to intelligence. We need more large-scale, high-quality, multicenter prospective studies to explore the clinical applicability of AI, and ethical issues need to be taken into account.

Artificial intelligence and its impact on quality improvement in upper and lower gastrointestinal endoscopy

Artificial intelligence (AI) and its application in medicine has grown large interest. Within gastrointestinal (GI) endoscopy, the field of colonoscopy and polyp detection is the most investigated, however, upper GI follows the lead. Since endoscopy is performed by humans, it is inherently an imperfect procedure. Computer-aided diagnosis may improve its quality by helping prevent missing lesions and supporting optical diagnosis for those detected. An entire evolution in AI systems has been established in the last decades, resulting in optimization of the diagnostic performance with lower variability and matching or even outperformance of expert endoscopists. This shows a great potential for future quality improvement of endoscopy, given the outstanding diagnostic features of AI. With this narrative review, we highlight the potential benefit of AI to improve overall quality in daily endoscopy and describe the most recent developments for characterization and diagnosis as well as the recent conditions for regulatory approval.

Striving for quality improvement: can artificial intelligence help?

Artificial intelligence (AI) is of keen interest for global health development as potential support for current human shortcomings. Gastrointestinal (GI) endoscopy is an excellent substrate for AI, since it holds the genuine potential to improve quality in GI endoscopy and overall patient care by improving detection and diagnosis guiding the endoscopists in performing endoscopy to the highest quality standards. The possibility of large data acquisitioning to refine algorithms makes implementation of AI into daily practice a potential reality. With the start of a new era adopting deep learning, large amounts of data can easily be processed, resulting in better diagnostic performances. In the upper gastrointestinal tract, research currently focusses on the detection and characterization of neoplasia, including Barrett's, squamous cell and gastric carcinoma, with an increasing amount of AI studies demonstrating the potential and benefit of AI-augmented endoscopy. Deep learning applied to small bowel video capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. In the colon, multiple prospective trials including five randomized trials, showed a consistent improvement in polyp and adenoma detection rates, one of the main quality indicators in endoscopy. There are however potential additional roles for AI to assist in quality improvement of endoscopic procedures, training and therapeutic decision making. Further large-scale, multicenter validation trials are required before AI-augmented diagnostic gastrointestinal endoscopy can be integrated into our routine clinical practice.

Utilizing artificial intelligence in endoscopy: a clinician's guide

INTRODUCTION

Artificial intelligence (AI) that surpasses human ability in image recognition is expected to be applied in the field of gastrointestinal endoscopes. Accordingly, its research and development (R &D) is being actively conducted. With the development of endoscopic diagnosis, there is a shortage of specialists who can perform high-precision endoscopy. We will examine whether AI with excellent image recognition ability can overcome this problem.

AREAS COVERED

Since 2016, papers on artificial intelligence using convolutional neural network (CNN in other word Deep Learning) have been published. CNN is generally capable of more accurate detection and classification than conventional machine learning. This is a review of papers using CNN in the gastrointestinal endoscopy area, along with the reasons why AI is required in clinical practice. We divided this review into four parts: stomach, esophagus, large intestine, and capsule endoscope (small intestine).

EXPERT OPINION

Potential applications for the AI include colorectal polyp detection and differentiation, gastric and esophageal cancer detection, and lesion detection in capsule endoscopy. The accuracy of endoscopic diagnosis will increase if the AI and endoscopist perform the endoscopy together.

Techniques to integrate artificial intelligence systems with medical information in gastroenterology

Gastrointestinal (GI) endoscopy is the central element in contemporary gastroenterology as it provides direct evidence to guide targeted therapy. To increase the accuracy of GI endoscopy and to reduce human-related errors, artificial intelligence (AI) has been applied in GI endoscopy, which has been proved to be effective in diagnosing and treating numerous diseases. Therefore, we review current research on the efficacy of AI-assisted GI endoscopy in order to assess its functions, advantages and how the design can be improved.

Development and Evaluation of a Machine Learning Prediction Model for Flap Failure in Microvascular Breast Reconstruction

BACKGROUND

Despite high success rates, flap failure remains an inherent risk in microvascular breast reconstruction. Identifying patients who are at high risk for flap failure would enable us to recommend alternative reconstructive techniques. However, as flap failure is a rare event, identification of risk factors is statistically challenging. Machine learning is a form of artificial intelligence that automates analytical model building. It has been proposed that machine learning can build superior prediction models when the outcome of interest is rare.

METHODS

In this study we evaluate machine learning resampling and decision-tree classification models for the prediction of flap failure in a large retrospective cohort of microvascular breast reconstructions.

RESULTS

A total of 1012 patients were included in the study. Twelve patients (1.1%) experienced flap failure. The ROSE informed oversampling technique and decision-tree classification resulted in a strong prediction model (AUC 0.95) with high sensitivity and specificity. In the testing cohort, the model maintained acceptable specificity and predictive power (AUC 0.67), but sensitivity was reduced. The model identified four high-risk patient groups. Obesity, comorbidities and smoking were found to contribute to flap loss. The flap failure rate in high-risk patients was 7.8% compared with 0.44% in the low-risk cohort (p = 0.001).

CONCLUSIONS

This machine-learning risk prediction model suggests that flap failure may not be a random event. The algorithm indicates that flap failure is multifactorial and identifies a number of potential contributing factors that warrant further investigation.

Diagnosis and Management of Non-Variceal Gastrointestinal Hemorrhage: A Review of Current Guidelines and Future Perspectives

Non-variceal gastrointestinal bleeding (GIB) is a significant cause of mortality and morbidity worldwide which is encountered in the ambulatory and hospital settings. Hemorrhage form the gastrointestinal (GI) tract is categorized as upper GIB, small bowel bleeding (also formerly referred to as obscure GIB) or lower GIB. Although the etiologies of GIB are variable, a strong, consistent risk factor is use of non-steroidal anti-inflammatory drugs. Advances in the endoscopic diagnosis and treatment of GIB have led to improved outcomes. We present an updated review of the current practices regarding the diagnosis and management of non-variceal GIB, and possible future directions.

Application of Artificial Intelligence to Gastroenterology and Hepatology

Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.

A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology

Background and aim  The growing number of publications on the application of artificial intelligence (AI) in medicine underlines the enormous importance and potential of this emerging field of research. In gastrointestinal endoscopy, AI has been applied to all segments of the gastrointestinal tract most importantly in the detection and characterization of colorectal polyps. However, AI research has been published also in the stomach and esophagus for both neoplastic and non-neoplastic disorders. The various technical as well as medical aspects of AI, however, remain confusing especially for non-expert physicians. This physician-engineer co-authored review explains the basic technical aspects of AI and provides a comprehensive overview of recent publications on AI in gastrointestinal endoscopy. Finally, a basic insight is offered into understanding publications on AI in gastrointestinal endoscopy.