Artificial intelligence in gastroenterology: where are we and where are we going?

Background: The use of artificial intelligence (AI) is rapidly advancing in gastroenterology, most notably in the area of endoscopy, but also more widely throughout the speciality. This article reviews what AI is, how it works and some of the key advances it is bringing. AI can already improve patient triage so that resources can be better targeted at sick patients. In endoscopy, AI can improve the detection of polyps during colonoscopy and the accuracy of diagnosis, while in Barrett’s oesophagus, it can improve the detection of pre-cancerous dysplasia so that all endoscopists can emulate the performance of world-class experts. Systems are being developed to automate the assessment of bowel preparation quality and report writing. The power of novel generative AI such as ChatGPT could drive major improvements in communication between busy clinicians and patients. Healthcare professionals need to ensure they understand how to manage the ‘black-box’ that is AI

Lynch syndrome: from detection to treatment

Lynch syndrome (LS) is an inherited cancer predisposition syndrome associated with high lifetime risk of developing tumours, most notably colorectal and endometrial. It arises in the context of pathogenic germline variants in one of the mismatch repair genes, that are necessary to maintain genomic stability. LS remains underdiagnosed in the population despite national recommendations for empirical testing in all new colorectal and endometrial cancer cases. There are now well-established colorectal cancer surveillance programmes, but the high rate of interval cancers identified, coupled with a paucity of high-quality evidence for extra-colonic cancer surveillance, means there is still much that can be achieved in diagnosis, risk-stratification and management. The widespread adoption of preventative pharmacological measures is on the horizon and there are exciting advances in the role of immunotherapy and anti-cancer vaccines for treatment of these highly immunogenic LS-associated tumours. In this review, we explore the current landscape and future perspectives for the identification, risk stratification and optimised management of LS with a focus on the gastrointestinal system. We highlight the current guidelines on diagnosis, surveillance, prevention and treatment and link molecular disease mechanisms to clinical practice recommendations

Initial Responses to False Positives in AI-Supported Continuous Interactions: A Colonoscopy Case Study

The use of artificial intelligence (AI) in clinical support systems is increasing. In this article, we focus on AI support for continuous interaction scenarios. A thorough understanding of end-user behaviour during these continuous human-AI interactions, in which user input is sustained over time and during which AI suggestions can appear at any time, is still missing. We present a controlled lab study involving 21 endoscopists and an AI colonoscopy support system. Using a custom-developed application and an off-the-shelf videogame controller, we record participants’ navigation behaviour and clinical assessment across 14 endoscopic videos. Each video is manually annotated to mimic an AI recommendation, being either true positive or false positive in nature. We find that time between AI recommendation and clinical assessment is significantly longer for incorrect assessments. Further, the type of medical content displayed significantly affects decision time. Finally, we discover that the participant’s clinical role plays a large part in the perception of clinical AI support systems. Our study presents a realistic assessment of the effects of imperfect and continuous AI support in a clinical scenario

Automated analysis of intraoperative phase in laparoscopic cholecystectomy: A comparison of one attending surgeon and their residents

OBJECTIVE: This study compares the intraoperative phase times in laparoscopic cholecystectomy performed by an attending surgeon and supervised residents over 10-years to assess operative times as a marker of performance and any impact of case severity on times. DESIGN: Laparoscopic cholecystectomy videos were uploaded to Touch Surgery™ Enterprise, a combined software and hardware solution for securely recording, storing, and analysing surgical videos, which provide analytics of intraoperative phase times. Case severity and visualisation of the critical view of safety (CVS) were manually assessed using modified 10-point intraoperative gallbladder scoring system (mG10) and CVS scores, respectively. Attending and residents' times were compared unmatched and matched by mG10. SETTING: Secondary analysis of anonymized laparoscopic cholecystectomy video, recorded as standard of care. PARTICIPANTS: Adult patients who underwent elective laparoscopic cholecystectomy a single UK hospital. Cases were performed by one attending and their residents. RESULTS: 159 (attending=96, resident=63) laparoscopic cholecystectomy videos and intraoperative phase times were reviewed on Touch Surgery™ Enterprise and analyzed. Attending cases were more challenging (p=0.037). Residents achieved higher CVS scores (p=0.034) and showed longer dissection of hepatocystic triangle (HCT) times (p=0.012) in more challenging cases. Residents' total operative time (p=0.001) and dissection of HCT (p=0.002) times exceeded the attending's in low-severity matched cases (mG10=1). Residents' total operative times (p<0.001), port insertion/gallbladder exposure (p=0.032), and dissection of HCT (p<0.001) exceeded the attending's in matched cases (mG10=2). Residents' total operative (p<0.001), dissection of HCT (p<0.001), and gallbladder dissection (p=0.010) times exceeded the attendings in unmatched cases. CONCLUSIONS: Residents' total operative and dissection of HCT times significantly exceeded the attending's unmatched cases and low-severity matched cases which could suggest training need, however, also reflects an expected assessment of competence, and validates time as a marker of performance

The impact of virtual reality simulation training on operative performance in laparoscopic cholecystectomy: meta-analysis of randomized clinical trials

BACKGROUND: Simulation training can improve the learning curve of surgical trainees. This research aimed to systematically review randomized clinical trials (RCT) evaluating the performance of junior surgical trainees following virtual reality training (VRT) and other training methods in laparoscopic cholecystectomy. METHODS: MEDLINE (PubMed), Embase (Ovid SP), Web of Science, Scopus and LILACS were searched for trials randomizing participants to VRT or no additional training (NAT) or simulation training (ST). Outcomes of interest were the reported performance using global rating scores (GRS), the Objective Structured Assessment of Technical Skill (OSATS) and Global Operative Assessment of Laparoscopic Skills (GOALS), error counts and time to completion of task during laparoscopic cholecystectomy on either porcine models or humans. Study quality was assessed using the Cochrane Risk of Bias Tool. PROSPERO ID: CRD42020208499. RESULTS: A total of 351 titles/abstracts were screened and 96 full texts were reviewed. Eighteen RCT were included and 15 manuscripts had data available for meta-analysis. Thirteen studies compared VRT and NAT, and 4 studies compared VRT and ST. One study compared VRT with NAT and ST and reported GRS only. Meta-analysis showed OSATS score (mean difference (MD) 6.22, 95%CI 3.81 to 8.36, P < 0.001) and time to completion of task (MD -8.35 min, 95%CI 13.10 to 3.60, P = <0.001) significantly improved after VRT compared with NAT. No significant difference was found in GOALS score. No significant differences were found between VRT and ST groups. Intraoperative errors were reported as reduced in VRT groups compared with NAT but were not suitable for meta-analysis. CONCLUSION: Meta-analysis suggests that performance measured by OSATS and time to completion of task is improved with VRT compared with NAT for junior trainee in laparoscopic cholecystectomy. However, conclusions are limited by methodological heterogeneity and more research is needed to quantify the potential benefit to surgical training

Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia

In this work, we have concentrated our efforts on the interpretability of classification results coming from a fully convolutional neural network. Motivated by the classification of oesophageal tissue for real-time detection of early squamous neoplasia, the most frequent kind of oesophageal cancer in Asia, we present a new dataset and a novel deep learning method that by means of deep supervision and a newly introduced concept, the embedded Class Activation Map (eCAM), focuses on the interpretability of results as a design constraint of a convolutional network. We present a new approach to visualise attention that aims to give some insights on those areas of the oesophageal tissue that lead a network to conclude that the images belong to a particular class and compare them with those visual features employed by clinicians to produce a clinical diagnosis. In comparison to a baseline method which does not feature deep supervision but provides attention by grafting Class Activation Maps, we improve the F1-score from 87.3% to 92.7% and provide more detailed attention maps

Automated colonoscopy withdrawal phase duration estimation using cecum detection and surgical tasks classification

Colorectal cancer is the third most common type of cancer with almost two million new cases worldwide. They develop from neoplastic polyps, most commonly adenomas, which can be removed during colonoscopy to prevent colorectal cancer from occurring. Unfortunately, up to a quarter of polyps are missed during colonoscopies. Studies have shown that polyp detection during a procedure correlates with the time spent searching for polyps, called the withdrawal time. The different phases of the procedure (cleaning, therapeutic, and exploration phases) make it difficult to precisely measure the withdrawal time, which should only include the exploration phase. Separating this from the other phases requires manual time measurement during the procedure which is rarely performed. In this study, we propose a method to automatically detect the cecum, which is the start of the withdrawal phase, and to classify the different phases of the colonoscopy, which allows precise estimation of the final withdrawal time. This is achieved using a Resnet for both detection and classification trained with two public datasets and a private dataset composed of 96 full procedures. Out of 19 testing procedures, 18 have their withdrawal time correctly estimated, with a mean error of 5.52 seconds per minute per procedure

Artificial intelligence and inflammatory bowel disease: practicalities and future prospects

Artificial intelligence (AI) is an emerging technology predicted to have significant applications in healthcare. This review highlights AI applications that impact the patient journey in inflammatory bowel disease (IBD), from genomics to endoscopic applications in disease classification, stratification and self-monitoring to risk stratification for personalised management. We discuss the practical AI applications currently in use while giving a balanced view of concerns and pitfalls and look to the future with the potential of where AI can provide significant value to the care of the patient with IBD

Interobserver Variability in the Assessment of Fluorescence Angiography in the Colon

BACKGROUND: Fluorescence angiography in colorectal surgery is a technique that may lead to lower anastomotic leak rates. However, the interpretation of the fluorescent signal is not standardised and there is a paucity of data regarding interobserver agreement. The aim of this study is to assess interobserver variability in selection of the transection point during fluorescence angiography before anastomosis. METHODS: An online survey with still images of fluorescence angiography was distributed through colorectal surgery channels containing images from 13 patients where several areas for transection were displayed to be chosen by raters. Agreement was assessed overall and between pre-planned rater cohorts (experts vs non-experts; trainees vs consultants; colorectal specialists vs non colorectal specialists), using Fleiss' kappa statistic. RESULTS: 101 raters had complete image ratings. No significant difference was found between raters when choosing a point of optimal bowel transection based on fluorescence angiography still images. There was no difference between pre-planned cohorts analysed (experts vs non-experts; trainees vs consultants; colorectal specialists vs non colorectal specialists). Agreement between these cohorts was poor (<.26). CONCLUSION: Whilst there is no learning curve for the technical adoption of FA, understanding the fluorescent signal characteristics is key to successful use. We found significant variation exists in interpretation of static fluorescence angiography data. Further efforts should be employed to standardise fluorescence angiography assessment