A primer on artificial intelligence and its application to endoscopy

An artificial intelligence system for chronic atrophic gastritis diagnosis and risk stratification under white light endoscopy

BACKGROUND AND AIMS

The diagnosis and stratification of gastric atrophy (GA) predict patients' gastric cancer progression risk and determine endoscopy surveillance interval. We aimed to construct an artificial intelligence (AI) system for GA endoscopic identification and risk stratification based on the Kimura-Takemoto classification.

METHODS

We constructed the system using two trained models and verified its performance. First, we retrospectively collected 869 images and 119 videos to compare its performance with that of endoscopists in identifying GA. Then, we included original image cases of 102 patients to validate the system for stratifying GA and comparing it with endoscopists with different experiences.

RESULTS

The sensitivity of model 1 was higher than that of endoscopists (92.72% vs. 76.85 %) at image level and also higher than that of experts (94.87% vs. 85.90 %) at video level. The system outperformed experts in stratifying GA (overall accuracy: 81.37 %, 73.04 %, p = 0.045). The accuracy of this system in classifying non-GA, mild GA, moderate GA, and severe GA was 80.00 %, 77.42 %, 83.33 %, and 85.71 %, comparable to that of experts and better than that of seniors and novices.

CONCLUSIONS

We established an expert-level system for GA endoscopic identification and risk stratification. It has great potential for endoscopic assessment and surveillance determinations.

Application value of an artificial intelligence-based diagnosis and recognition system in gastroscopy training for graduate students in gastroenterology: a preliminary study

OBJECTIVE

This study aimed to discuss the application value of an artificial intelligence-based diagnosis and recognition system (AIDRS) in the teaching activities for Bachelor of Medicine and Bachelor of Surgery (MBBS) in China. The learning performance of graduate students in gastroenterology during gastroscopy training with and without AIDRS was assessed.

METHODS

The study recruited 32 graduate students of the gastroenterology program at Jiangsu province hospital of Chinese medicine and Xiangyang No. 1 People's Hospital from March 2018 to March 2022 and randomly divided them into AIDRS (n = 16) and non-AIDRS (n = 16) groups. The AIDRS software was used for real-time monitoring of blind spots of gastroscopy to aid in lesion diagnosis and recognition in the AIDRS group. Only a conventional gastroscopic procedure was implemented in the non-AIDRS group. The final performance score, success rate of gastroscopy, lesion detection rate, and pain score of patients were compared between the two groups during gastroscopy. A self-prepared teaching and learning satisfaction questionnaire was administered to the two groups of students.

RESULTS

The AIDRS group had a higher final performance score (92.60 ± 2.83 vs. 89.21 ± 3.57, t = 2.98, P < 0.05), a higher success rate of gastroscopy (448/480 vs. 417/480, χ2 = 11.23, P < 0.05), and a higher detection rate of lesions (51/52 vs. 41/53, χ2 = 8.56, P < 0.05) compared with the non-AIDRS group. The pain scores of patients were lower in the AIDRS group than in the non-AIDRS group (3.40 [2.23, 3.98] vs. 4.45 [3.72, 4.75], Z = 3.04, P < 0.05). Besides, the average time for gastroscopy was lower in the AIDRS group than in the non-AIDRS group (7.15 ± 1.24 vs. 8.21 ± 1.26, t = 2.38, P = 0.02). The overall satisfaction level with the teaching program was higher in the AIDRS group (43.51 ± 2.29 vs. 40.93 ± 2.07, t = 3.33, P < 0.05).

CONCLUSION

In the context of medicine-education cooperation, AIDRS offered valuable assistance in gastroscopy training and increased the success rate of gastroscopy and teaching and learning satisfaction. AIDRS is worthy of wider-scale promotion.

Colorectal cancer screening: A review of current knowledge and progress in research

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide, being the third most commonly diagnosed malignancy and the second leading cause of cancer-related deaths globally. Despite the progress in screening, early diagnosis, and treatment, approximately 20%-25% of CRC patients still present with metastatic disease at the time of their initial diagnosis. Furthermore, the burden of disease is still expected to increase, especially in individuals younger than 50 years old, among whom early-onset CRC incidence has been increasing. Screening and early detection are pivotal to improve CRC-related outcomes. It is well established that CRC screening not only reduces incidence, but also decreases deaths from CRC. Diverse screening strategies have proven effective in decreasing both CRC incidence and mortality, though variations in efficacy have been reported across the literature. However, uncertainties persist regarding the optimal screening method, age intervals and periodicity. Moreover, adherence to CRC screening remains globally low. In recent years, emerging technologies, notably artificial intelligence, and non-invasive biomarkers, have been developed to overcome these barriers. However, controversy exists over the actual impact of some of the new discoveries on CRC-related outcomes and how to effectively integrate them into daily practice. In this review, we aim to cover the current evidence surrounding CRC screening. We will further critically assess novel approaches under investigation, in an effort to differentiate promising innovations from mere novelties.

Deep learning in negative small-bowel capsule endoscopy improves small-bowel lesion detection and diagnostic yield

OBJECTIVES

Although several studies have shown the usefulness of artificial intelligence to identify abnormalities in small-bowel capsule endoscopy (SBCE) images, few studies have proven its actual clinical usefulness. Thus, the aim of this study was to examine whether meaningful findings could be obtained when negative SBCE videos were reanalyzed with a deep convolutional neural network (CNN) model.

METHODS

Clinical data of patients who received SBCE for suspected small-bowel bleeding at two academic hospitals between February 2018 and July 2020 were retrospectively collected. All SBCE videos read as negative were reanalyzed with the CNN algorithm developed in our previous study. Meaningful findings such as angioectasias and ulcers were finally decided after reviewing CNN-selected images by two gastroenterologists.

RESULTS

Among 202 SBCE videos, 103 (51.0%) were read as negative by humans. Meaningful findings were detected in 63 (61.2%) of these 103 videos after reanalyzing them with the CNN model. There were 79 red spots or angioectasias in 40 videos and 66 erosions or ulcers in 35 videos. After reanalysis, the diagnosis was changed for 10 (10.3%) patients who had initially negative SBCE results. During a mean follow-up of 16.5 months, rebleeding occurred in 19 (18.4%) patients. The rebleeding rate was 23.6% (13/55) for patients with meaningful findings and 16.1% (5/31) for patients without meaningful findings (P = 0.411).

CONCLUSION

Our CNN algorithm detected meaningful findings in negative SBCE videos that were missed by humans. The use of deep CNN for SBCE image reading is expected to compensate for human error.

Few-shot learning for the classification of intestinal tuberculosis and Crohn's disease on endoscopic images: A novel learn-to-learn framework

Background and aim

Standard deep learning methods have been found inadequate in distinguishing between intestinal tuberculosis (ITB) and Crohn's disease (CD), a shortcoming largely attributed to the scarcity of available samples. In light of this limitation, our objective is to develop an innovative few-shot learning (FSL) system, specifically tailored for the efficient categorization and differential diagnosis of CD and ITB, using endoscopic image data with minimal sample requirements.

Methods

A total of 122 white-light endoscopic images (99 CD images and 23 ITB images) were collected (one ileum image from each patient). A 2-way, 3-shot FSL model that integrated dual transfer learning and metric learning strategies was devised. Xception architecture was selected as the foundation and then underwent a dual transfer process utilizing oesophagitis images sourced from HyperKvasir. Subsequently, the eigenvectors derived from the Xception for each query image were converted into predictive scores, which were calculated using the Euclidean distances to six reference images from the support sets.

Results

The FSL model, which leverages dual transfer learning, exhibited enhanced performance metrics (AUC 0.81) compared to a model relying on single transfer learning (AUC 0.56) across three evaluation rounds. Additionally, its performance surpassed that of a less experienced endoscopist (AUC 0.56) and even a more seasoned specialist (AUC 0.61).

Conclusions

The FSL model we have developed demonstrates efficacy in distinguishing between CD and ITB using a limited dataset of endoscopic imagery. FSL holds value for enhancing the diagnostic capabilities of rare conditions.

A newly developed deep learning-based system for automatic detection and classification of small bowel lesions during double-balloon enteroscopy examination

BACKGROUND

Double-balloon enteroscopy (DBE) is a standard method for diagnosing and treating small bowel disease. However, DBE may yield false-negative results due to oversight or inexperience. We aim to develop a computer-aided diagnostic (CAD) system for the automatic detection and classification of small bowel abnormalities in DBE.

DESIGN AND METHODS

A total of 5201 images were collected from Renmin Hospital of Wuhan University to construct a detection model for localizing lesions during DBE, and 3021 images were collected to construct a classification model for classifying lesions into four classes, protruding lesion, diverticulum, erosion & ulcer and angioectasia. The performance of the two models was evaluated using 1318 normal images and 915 abnormal images and 65 videos from independent patients and then compared with that of 8 endoscopists. The standard answer was the expert consensus.

RESULTS

For the image test set, the detection model achieved a sensitivity of 92% (843/915) and an area under the curve (AUC) of 0.947, and the classification model achieved an accuracy of 86%. For the video test set, the accuracy of the system was significantly better than that of the endoscopists (85% vs. 77 ± 6%, p < 0.01). For the video test set, the proposed system was superior to novices and comparable to experts.

CONCLUSIONS

We established a real-time CAD system for detecting and classifying small bowel lesions in DBE with favourable performance. ENDOANGEL-DBE has the potential to help endoscopists, especially novices, in clinical practice and may reduce the miss rate of small bowel lesions.

Use of Artificial Intelligence in the Diagnosis of Colorectal Cancer

Colorectal cancer (CRC) is one of the most common forms of cancer. Therefore, diagnosing the condition early and accurately is critical for improved patient outcomes and effective treatment. Recently, artificial intelligence (AI) algorithms such as support vector machine (SVM) and convolutional neural network (CNN) have demonstrated promise in medical image analysis. This paper, conducted from a systematic review perspective, aimed to determine the effectiveness of AI integration in CRC diagnosis, emphasizing accuracy, sensitivity, and specificity. From a methodological perspective, articles that were included were those that had been conducted in the past decade. Also, the articles needed to have been documented in English, with databases such as Embase, PubMed, and Google Scholar used to obtain relevant research studies. Similarly, keywords were used to arrive at relevant articles. These keywords included AI, CRC, specificity, sensitivity, accuracy, efficacy, effectiveness, disease diagnosis, screening, machine learning, area under the curve (AUC), and deep learning. From the results, most scholarly studies contend that AI is superior in medical image analysis, the development of subtle patterns, and decision support. However, while deploying these algorithms, a key theme is that the collaboration between medical experts and AI systems needs to be seamless. In addition, the AI algorithms ought to be refined continuously in the current world of big data and ensure that they undergo rigorous validation to provide more informed decision-making for or against adopting those AI tools in clinical settings. In conclusion, therefore, balancing between human expertise and technological innovation is likely to pave the way for the realization of AI's full potential concerning its promising role in improving CRC diagnosis, upon which there might be significant patient outcome improvements, disease detection, and the achievement of a more effective healthcare system.

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.

Creeping Fat in Crohn's Disease-Surgical, Histological, and Radiological Approaches

During the course of Crohn's disease, the response of mesenteric adipose tissue to the production of inflammatory mediators and bacterial invasion through the intestinal mucosa results in the formation of creeping fat. Creeping fat describes the arresting finger-like projections that surround the inflamed bowel. In this review, the microscopic and macroscopic features of creeping fat and histological evidence for the importance of this tissue are discussed. Moreover, the most recent insights into the radiological assessment of creeping fat in patients with Crohn's disease are reported. Advances in imaging techniques have revolutionized the possibility of visualization and quantification of adipose tissue depots with excellent accuracy. Visceral fat has been significantly correlated with various Crohn's-disease-related outcomes. Despite the difficulties in distinguishing physiologic perienteric fat from creeping fat, the growing interest in fat-wrapping in Crohn's disease has rejuvenated radiologic research. With regard to the noninvasive fat-wrapping assessment, a novel CT enterography-based mesenteric creeping fat index has been developed for the mitigation of the confounding effect of normal retroperitoneal and perienteric adipose tissue. Research on machine learning algorithms and computational radiomics in conjunction with mechanistic studies may be the key for the elucidation of the complex role of creeping fat in Crohn's disease.

Artificial Intelligence-Aided Optical Imaging for Cancer Theranostics

The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-making for individualized cancer medicine. In particular, optical imaging methods are able to visualize both the structural and functional information of tumors tissues with high contrast, low cost, and noninvasive property. However, no systematic work has been performed to inspect the recent advances on AI-aided optical imaging for cancer theranostics. In this review, we demonstrated how AI can guide optical imaging methods to improve the accuracy on tumor detection, automated analysis and prediction of its histopathological section, its monitoring during treatment, and its prognosis by using computer vision, deep learning and natural language processing. By contrast, the optical imaging techniques involved mainly consisted of various tomography and microscopy imaging methods such as optical endoscopy imaging, optical coherence tomography, photoacoustic imaging, diffuse optical tomography, optical microscopy imaging, Raman imaging, and fluorescent imaging. Meanwhile, existing problems, possible challenges and future prospects for AI-aided optical imaging protocol for cancer theranostics were also discussed. It is expected that the present work can open a new avenue for precision oncology by using AI and optical imaging tools.

Artificial Intelligence in Pediatric Endoscopy: Current Status and Future Applications

The application of artificial intelligence (AI) has great promise for improving pediatric endoscopy. The majority of preclinical studies have been undertaken in adults, with the greatest progress being made in the context of colorectal cancer screening and surveillance. This development has only been possible with advances in deep learning, like the convolutional neural network model, which has enabled real-time detection of pathology. Comparatively, the majority of deep learning systems developed in inflammatory bowel disease have focused on predicting disease severity and were developed using still images rather than videos. The application of AI to pediatric endoscopy is in its infancy, thus providing an opportunity to develop clinically meaningful and fair systems that do not perpetuate societal biases. In this review, we provide an overview of AI, summarize the advances of AI in endoscopy, and describe its potential application to pediatric endoscopic practice and education.

Quality of bowel preparation in patients with inflammatory bowel disease undergoing colonoscopy: What factors to consider?

An adequate bowel preparation in patients with inflammatory bowel disease (IBD) is a prerequisite for successful colonoscopy for screening, diagnosis, and surveillance. Several bowel preparation formulations are available, both high- and low-volume based on polyethylene glycol. Generally, low-volume formulations are also based on several compounds such as magnesium citrate preparations with sodium picosulphate, oral sulphate solution, and oral sodium phosphate-based solutions. Targeted studies on the quality of bowel preparation prior to colonoscopy in the IBD population are still required, with current evidence from existing studies being inconclusive. New frontiers are also moving towards the use of alternatives to anterograde ones, using preparations based on retrograde colonic lavage.

An Introduction to Artificial Intelligence in Developmental and Behavioral Pediatrics

ABSTRACT

Technological breakthroughs, together with the rapid growth of medical information and improved data connectivity, are creating dramatic shifts in the health care landscape, including the field of developmental and behavioral pediatrics. While medical information took an estimated 50 years to double in 1950, by 2020, it was projected to double every 73 days. Artificial intelligence (AI)-powered health technologies, once considered theoretical or research-exclusive concepts, are increasingly being granted regulatory approval and integrated into clinical care. In the United States, the Food and Drug Administration has cleared or approved over 160 health-related AI-based devices to date. These trends are only likely to accelerate as economic investment in AI health care outstrips investment in other sectors. The exponential increase in peer-reviewed AI-focused health care publications year over year highlights the speed of growth in this sector. As health care moves toward an era of intelligent technology powered by rich medical information, pediatricians will increasingly be asked to engage with tools and systems underpinned by AI. However, medical students and practicing clinicians receive insufficient training and lack preparedness for transitioning into a more AI-informed future. This article provides a brief primer on AI in health care. Underlying AI principles and key performance metrics are described, and the clinical potential of AI-driven technology together with potential pitfalls is explored within the developmental and behavioral pediatric health context.

Artificial intelligence in improving the outcome of surgical treatment in colorectal cancer

Background

A considerable number of recent research have used artificial intelligence (AI) in the area of colorectal cancer (CRC). Surgical treatment of CRC still remains the most important curative component. Artificial intelligence in CRC surgery is not nearly as advanced as it is in screening (colonoscopy), diagnosis and prognosis, especially due to the increased complexity and variability of structures and elements in all fields of view, as well as a general shortage of annotated video banks for utilization.

Methods

A literature search was made and relevant studies were included in the minireview.

Results

The intraoperative steps which, at this moment, can benefit from AI in CRC are: phase and action recognition, excision plane navigation, endoscopy control, real-time circulation analysis, knot tying, automatic optical biopsy and hyperspectral imaging. This minireview also analyses the current advances in robotic treatment of CRC as well as the present possibility of automated CRC robotic surgery.

Conclusions

The use of AI in CRC surgery is still at its beginnings. The development of AI models capable of reproducing a colorectal expert surgeon's skill, the creation of large and complex datasets and the standardization of surgical colorectal procedures will contribute to the widespread use of AI in CRC surgical treatment.

Artificial Intelligence in Hepatology- Ready for the Primetime

Artificial Intelligence (AI) is a mathematical process of computer mediating designing of algorithms to support human intelligence. AI in hepatology has shown tremendous promise to plan appropriate management and hence improve treatment outcomes. The field of AI is in a very early phase with limited clinical use. AI tools such as machine learning, deep learning, and 'big data' are in a continuous phase of evolution, presently being applied for clinical and basic research. In this review, we have summarized various AI applications in hepatology, the pitfalls and AI's future implications. Different AI models and algorithms are under study using clinical, laboratory, endoscopic and imaging parameters to diagnose and manage liver diseases and mass lesions. AI has helped to reduce human errors and improve treatment protocols. Further research and validation are required for future use of AI in hepatology.

Robotics in therapeutic endoscopy (with video)

Since its inception, endoscopy has evolved from a solely diagnostic procedure to an expanding therapeutic field within gastroenterology. The incorporation of robotics in gastroenterology initially addressed shortcomings of flexible endoscopes in natural orifice transluminal endoscopy. Developing therapeutic endoscopic robotic platforms now offer operators improved ergonomics, visualization, dexterity, precision, and control and the possibility of increasing proficiency and standardization of complex endoscopic procedures including endoscopic submucosal dissection, endoscopic full-thickness resection, and endoscopic suturing. The following review discusses the history, potential applications, and tools currently available and in development for robotics in therapeutic endoscopy.

Artificial Intelligence-Assisted Endoscopic Diagnosis of Early Upper Gastrointestinal Cancer: A Systematic Review and Meta-Analysis

Objective

The aim of this study was to assess the diagnostic ability of artificial intelligence (AI) in the detection of early upper gastrointestinal cancer (EUGIC) using endoscopic images.

Methods

Databases were searched for studies on AI-assisted diagnosis of EUGIC using endoscopic images. The pooled area under the curve (AUC), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) with 95% confidence interval (CI) were calculated.

Results

Overall, 34 studies were included in our final analysis. Among the 17 image-based studies investigating early esophageal cancer (EEC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.95 (95% CI, 0.95–0.96), 0.95 (95% CI, 0.94–0.95), 10.76 (95% CI, 7.33–15.79), 0.07 (95% CI, 0.04–0.11), and 173.93 (95% CI, 81.79–369.83), respectively. Among the seven patient-based studies investigating EEC detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.94 (95% CI, 0.91–0.96), 0.90 (95% CI, 0.88–0.92), 6.14 (95% CI, 2.06–18.30), 0.07 (95% CI, 0.04–0.11), and 69.13 (95% CI, 14.73–324.45), respectively. Among the 15 image-based studies investigating early gastric cancer (EGC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.94, 0.87 (95% CI, 0.87–0.88), 0.88 (95% CI, 0.87–0.88), 7.20 (95% CI, 4.32–12.00), 0.14 (95% CI, 0.09–0.23), and 48.77 (95% CI, 24.98–95.19), respectively.

Conclusions

On the basis of our meta-analysis, AI exhibited high accuracy in diagnosis of EUGIC.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/, identifier PROSPERO (CRD42021270443).

Intestinal strictures in Crohn's disease: a 2021 update

Intestinal strictures remain one of the most intractable and common complications of Crohn's disease (CD). Approximately 70% of CD patients will develop fibrotic strictures after 10 years of CD diagnosis. Since specific antifibrotic therapies are unavailable, endoscopic balloon dilation and surgery remain the mainstay treatments despite a high recurrence rate. Besides, there are no reliable methods for accurately evaluating intestinal fibrosis. This is largely due to the fact that the mechanisms of initiation and propagation of intestinal fibrosis are poorly understood. There is growing evidence implying that the pathogenesis of stricturing CD involves the intricate interplay of factors including aberrant immune and nonimmune responses, host-microbiome dysbiosis, and genetic susceptibility. Currently, the progress on intestinal strictures has been fueled by the advent of novel techniques, such as single-cell sequencing, multi-omics, and artificial intelligence. Here, we perform a timely and comprehensive review of the substantial advances in intestinal strictures in 2021, aiming to provide prompt information regarding fibrosis and set the stage for the improvement of diagnosis, treatment, and prognosis of intestinal strictures.

Artificial Intelligence for Colonoscopy: Past, Present, and Future

During the past decades, many automated image analysis methods have been developed for colonoscopy. Real-time implementation of the most promising methods during colonoscopy has been tested in clinical trials, including several recent multi-center studies. All trials have shown results that may contribute to prevention of colorectal cancer. We summarize the past and present development of colonoscopy video analysis methods, focusing on two categories of artificial intelligence (AI) technologies used in clinical trials. These are (1) analysis and feedback for improving colonoscopy quality and (2) detection of abnormalities. Our survey includes methods that use traditional machine learning algorithms on carefully designed hand-crafted features as well as recent deep-learning methods. Lastly, we present the gap between current state-of-the-art technology and desirable clinical features and conclude with future directions of endoscopic AI technology development that will bridge the current gap.

Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer

Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients’ survival rate. In recent years, due to the explosion of clinical and omics data, and groundbreaking research in machine learning, artificial intelligence (AI) has shown a great application potential in clinical field of CRC, providing new auxiliary approaches for clinicians to identify high-risk patients, select precise and personalized treatment plans, as well as to predict prognoses. This review comprehensively analyzes and summarizes the research progress and clinical application value of AI technologies in CRC screening, diagnosis, treatment, and prognosis, demonstrating the current status of the AI in the main clinical stages. The limitations, challenges, and future perspectives in the clinical implementation of AI are also discussed.

Artificial Intelligence for Detecting and Delineating Margins of Early ESCC Under WLI Endoscopy

INTRODUCTION

Conventional white light imaging (WLI) endoscopy is the most common screening technique used for detecting early esophageal squamous cell carcinoma (ESCC). Nevertheless, it is difficult to detect and delineate margins of early ESCC using WLI endoscopy. This study aimed to develop an artificial intelligence (AI) model to detect and delineate margins of early ESCC under WLI endoscopy.

METHODS

A total of 13,083 WLI images from 1,239 patients were used to train and test the AI model. To evaluate the detection performance of the model, 1,479 images and 563 images were used as internal and external validation data sets, respectively. For assessing the delineation performance of the model, 1,114 images and 211 images were used as internal and external validation data sets, respectively. In addition, 216 images were used to compare the delineation performance between the model and endoscopists.

RESULTS

The model showed an accuracy of 85.7% and 84.5% in detecting lesions in internal and external validation, respectively. For delineating margins, the model achieved an accuracy of 93.4% and 95.7% in the internal and external validation, respectively, under an overlap ratio of 0.60. The accuracy of the model, senior endoscopists, and expert endoscopists in delineating margins were 98.1%, 78.6%, and 95.3%, respectively. The proposed model achieved similar delineating performance compared with that of expert endoscopists but superior to senior endoscopists.

DISCUSSION

We successfully developed an AI model, which can be used to accurately detect early ESCC and delineate the margins of the lesions under WLI endoscopy.

Adoption of New Technologies: Artificial Intelligence

Over the past decade, artificial intelligence (AI) has been broadly applied to many aspects of human life, with recent groundbreaking successes in facial recognition, natural language processing, autonomous driving, and medical imaging. Gastroenterology has applied AI to a vast array of clinical problems, and some of the earliest prospective trials examining AI in medicine have been in computer vision applied to endoscopy. Evidence is mounting for 2 broad areas of AI as applied to gastroenterology: computer-aided detection and computer-aided diagnosis.

Multi-step validation of a deep learning-based system for the quantification of bowel preparation: a prospective, observational study

BACKGROUND

Inadequate bowel preparation is associated with a decrease in adenoma detection rate (ADR). A deep learning-based bowel preparation assessment system based on the Boston bowel preparation scale (BBPS) has been previously established to calculate the automatic BBPS (e-BBPS) score (ranging 0-20). The aims of this study were to investigate whether there was a statistically inverse relationship between the e-BBPS score and the ADR, and to determine the threshold of e-BBPS score for adequate bowel preparation in colonoscopy screening.

METHODS

In this prospective, observational study, we trained and internally validated the e-BBPS system using retrospective colonoscopy images and videos from the Endoscopy Center of Wuhan University, annotated by endoscopists. We externally validated the system using colonoscopy images and videos from the First People's Hospital of Yichang and the Third Hospital of Wuhan. To prospectively validate the system, we recruited consecutive patients at Renmin Hospital of Wuhan University aged between 18 and 75 years undergoing colonoscopy. The exclusion criteria included: contraindication to colonoscopy, family polyposis syndrome, inflammatory bowel disease, history of surgery for colorectal or colorectal cancer, known or suspected bowel obstruction or perforation, patients who were pregnant or lactating, inability to receive caecal intubation, and lumen obstruction. We did colonoscopy procedures and collected withdrawal videos, which were reviewed and the e-BBPS system was applied to all colon segments. The primary outcome of this study was ADR, defined as the proportion of patients with one or more conventional adenomas detected during colonoscopy. We calculated the ADR of each e-BBPS score and did a correlation analysis using Spearman analysis.

FINDINGS

From May 11 to Aug 10, 2020, 616 patients underwent screening colonoscopies, which evaluated. There was a significant inverse correlation between the e-BBPS score and ADR (Spearman's rank -0·976, p<0·010). The ADR for the e-BBPS scores 1-8 was 28·57%, 28·68%, 26·79%, 19·19%, 17·57%, 17·07%, 14·81%, and 0%, respectively. According to the 25% ADR standard for screening colonoscopy, an e-BBPS score of 3 was set as a threshold to guarantee an ADR of more than 25%, and so high-quality endoscopy. Patients with scores of more than 3 had a significantly lower ADR than those with a score of 3 or less (ADR 15·93% vs 28·03%, p<0·001, 95% CI 0·28-0·66, odds ratio 0·43).

INTERPRETATION

The e-BBPS system has potential to provide a more objective and refined threshold for the quantification of adequate bowel preparation.

FUNDING

Project of Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision and Hubei Province Major Science and Technology Innovation Project.

Convolution neural network for the diagnosis of wireless capsule endoscopy: a systematic review and meta-analysis

Background

Wireless capsule endoscopy (WCE) is considered to be a powerful instrument for the diagnosis of intestine diseases. Convolution neural network (CNN) is a type of artificial intelligence that has the potential to assist the detection of WCE images. We aimed to perform a systematic review of the current research progress to the CNN application in WCE.

Methods

A search in PubMed, SinoMed, and Web of Science was conducted to collect all original publications about CNN implementation in WCE. Assessment of the risk of bias was performed by Quality Assessment of Diagnostic Accuracy Studies-2 risk list. Pooled sensitivity and specificity were calculated by an exact binominal rendition of the bivariate mixed-effects regression model. I² was used for the evaluation of heterogeneity.

Results

16 articles with 23 independent studies were included. CNN application to WCE was divided into detection on erosion/ulcer, gastrointestinal bleeding (GI bleeding), and polyps/cancer. The pooled sensitivity of CNN for erosion/ulcer is 0.96 [95% CI 0.91, 0.98], for GI bleeding is 0.97 (95% CI 0.93–0.99), and for polyps/cancer is 0.97 (95% CI 0.82–0.99). The corresponding specificity of CNN for erosion/ulcer is 0.97 (95% CI 0.93–0.99), for GI bleeding is 1.00 (95% CI 0.99–1.00), and for polyps/cancer is 0.98 (95% CI 0.92–0.99).

Conclusion

Based on our meta-analysis, CNN-dependent diagnosis of erosion/ulcer, GI bleeding, and polyps/cancer approached a high-level performance because of its high sensitivity and specificity. Therefore, future perspective, CNN has the potential to become an important assistant for the diagnosis of WCE.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00464-021-08689-3.

Use of Artificial Intelligence to Improve the Quality Control of Gastrointestinal Endoscopy

With the rapid development of science and technology, artificial intelligence (AI) systems are becoming ubiquitous, and their utility in gastroenteroscopy is beginning to be recognized. Digestive endoscopy is a conventional and reliable method of examining and diagnosing digestive tract diseases. However, with the increase in the number and types of endoscopy, problems such as a lack of skilled endoscopists and difference in the professional skill of doctors with different degrees of experience have become increasingly apparent. Most studies thus far have focused on using computers to detect and diagnose lesions, but improving the quality of endoscopic examination process itself is the basis for improving the detection rate and correctly diagnosing diseases. In the present study, we mainly reviewed the role of AI in monitoring systems, mainly through the endoscopic examination time, reducing the blind spot rate, improving the success rate for detecting high-risk lesions, evaluating intestinal preparation, increasing the detection rate of polyps, automatically collecting maps and writing reports. AI can even perform quality control evaluations for endoscopists, improve the detection rate of endoscopic lesions and reduce the burden on endoscopists.

Application of Artificial Intelligence in Medicine: An Overview

Artificial intelligence (AI) is a new technical discipline that uses computer technology to research and develop the theory, method, technique, and application system for the simulation, extension, and expansion of human intelligence. With the assistance of new AI technology, the traditional medical environment has changed a lot. For example, a patient's diagnosis based on radiological, pathological, endoscopic, ultrasonographic, and biochemical examinations has been effectively promoted with a higher accuracy and a lower human workload. The medical treatments during the perioperative period, including the preoperative preparation, surgical period, and postoperative recovery period, have been significantly enhanced with better surgical effects. In addition, AI technology has also played a crucial role in medical drug production, medical management, and medical education, taking them into a new direction. The purpose of this review is to introduce the application of AI in medicine and to provide an outlook of future trends.

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.

Artificial Intelligence-Based Polyp Detection in Colonoscopy: Where Have We Been, Where Do We Stand, and Where Are We Headed?

BACKGROUND

In the past, image-based computer-assisted diagnosis and detection systems have been driven mainly from the field of radiology, and more specifically mammography. Nevertheless, with the availability of large image data collections (known as the "Big Data" phenomenon) in correlation with developments from the domain of artificial intelligence (AI) and particularly so-called deep convolutional neural networks, computer-assisted detection of adenomas and polyps in real-time during screening colonoscopy has become feasible.

SUMMARY

With respect to these developments, the scope of this contribution is to provide a brief overview about the evolution of AI-based detection of adenomas and polyps during colonoscopy of the past 35 years, starting with the age of "handcrafted geometrical features" together with simple classification schemes, over the development and use of "texture-based features" and machine learning approaches, and ending with current developments in the field of deep learning using convolutional neural networks. In parallel, the need and necessity of large-scale clinical data will be discussed in order to develop such methods, up to commercially available AI products for automated detection of polyps (adenoma and benign neoplastic lesions). Finally, a short view into the future is made regarding further possibilities of AI methods within colonoscopy.

KEY MESSAGES

Research of image-based lesion detection in colonoscopy data has a 35-year-old history. Milestones such as the Paris nomenclature, texture features, big data, and deep learning were essential for the development and availability of commercial AI-based systems for polyp detection.