Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study.
Wang Pu,Berzin Tyler M,Glissen Brown Jeremy Romek,Bharadwaj Shishira,Becq Aymeric,Xiao Xun,Liu Peixi,Li Liangping,Song Yan,Zhang Di,Li Yi,Xu Guangre,Tu Mengtian,Liu Xiaogang
Gut
OBJECTIVE:The effect of colonoscopy on colorectal cancer mortality is limited by several factors, among them a certain miss rate, leading to limited adenoma detection rates (ADRs). We investigated the effect of an automatic polyp detection system based on deep learning on polyp detection rate and ADR. DESIGN:In an open, non-blinded trial, consecutive patients were prospectively randomised to undergo diagnostic colonoscopy with or without assistance of a real-time automatic polyp detection system providing a simultaneous visual notice and sound alarm on polyp detection. The primary outcome was ADR. RESULTS:Of 1058 patients included, 536 were randomised to standard colonoscopy, and 522 were randomised to colonoscopy with computer-aided diagnosis. The artificial intelligence (AI) system significantly increased ADR (29.1%vs20.3%, p<0.001) and the mean number of adenomas per patient (0.53vs0.31, p<0.001). This was due to a higher number of diminutive adenomas found (185vs102; p<0.001), while there was no statistical difference in larger adenomas (77vs58, p=0.075). In addition, the number of hyperplastic polyps was also significantly increased (114vs52, p<0.001). CONCLUSIONS:In a low prevalent ADR population, an automatic polyp detection system during colonoscopy resulted in a significant increase in the number of diminutive adenomas detected, as well as an increase in the rate of hyperplastic polyps. The cost-benefit ratio of such effects has to be determined further. TRIAL REGISTRATION NUMBER:ChiCTR-DDD-17012221; Results.
10.1136/gutjnl-2018-317500
A systematic review and meta-analysis of artificial intelligence-diagnosed endoscopic remission in ulcerative colitis.
iScience
Endoscopic remission is an important therapeutic goal in ulcerative colitis (UC). The Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and Mayo Endoscopic Score (MES) are the commonly used endoscopic scoring criteria. This systematic review and meta-analysis aimed to evaluate the accuracy of artificial intelligence (AI) in diagnosing endoscopic remission in UC. We also performed a meta-analysis of each of the four endoscopic remission criteria (UCEIS = 0, MES = 0, UCEIS = <1, MES = <1). Eighteen studies involving 13,687 patients were included. The combined sensitivity and specificity of AI for diagnosing endoscopic remission in UC was 87% (95% confidence interval [CI]:81-92%) and 92% (95% CI: 89-94%), respectively. The area under the curve (AUC) was 0.96 (95% CI: 0.94-0.97). The results showed that the AI model performed well regardless of which criteria were used to define endoscopic remission of UC.
10.1016/j.isci.2023.108120
Big data in IBD: big progress for clinical practice.
Gut
IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation sequencing, high-throughput omics data generation and molecular networks have catalysed IBD research. The advent of artificial intelligence, in particular, machine learning, and systems biology has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically translatable knowledge. In this narrative review, we discuss how big data integration and machine learning have been applied to translational IBD research. Approaches such as machine learning may enable patient stratification, prediction of disease progression and therapy responses for fine-tuning treatment options with positive impacts on cost, health and safety. We also outline the challenges and opportunities presented by machine learning and big data in clinical IBD research.
10.1136/gutjnl-2019-320065
Artificial Intelligence for Disease Assessment in Inflammatory Bowel Disease: How Will it Change Our Practice?
Gastroenterology
Artificial intelligence (AI) has arrived and it will directly impact how we assess, monitor, and manage inflammatory bowel disease (IBD). Advances in the machine learning methodologies that power AI have produced astounding results for replicating expert judgment and predicting clinical outcomes, particularly in the analysis of imaging. This review will cover general concepts for AI in IBD, with descriptions of common machine learning methods, including decision trees and neural networks. Applications of AI in IBD will cover recent achievements in endoscopic image interpretation and scoring, new capabilities for cross-sectional image analysis, natural language processing for automated understanding of clinical text, and progress in AI-powered clinical decision support tools. In addition to detailing current evidence supporting the capabilities of AI for replicating expert clinical judgment, speculative commentary on how AI may advance concepts of disease activity assessment, care pathways, and pathophysiologic mechanisms of IBD will be addressed.
10.1053/j.gastro.2021.12.238
Artificial Intelligence Enabled Histological Prediction of Remission or Activity and Clinical Outcomes in Ulcerative Colitis.
Gastroenterology
BACKGROUND & AIMS:Microscopic inflammation has significant prognostic value in ulcerative colitis (UC); however, its assessment is complex with high interobserver variability. We aimed to develop and validate an artificial intelligence (AI) computer-aided diagnosis system to evaluate UC biopsies and predict prognosis. METHODS:A total of 535 digitalized biopsies (273 patients) were graded according to the PICaSSO Histologic Remission Index (PHRI), Robarts, and Nancy Histological Index. A convolutional neural network classifier was trained to distinguish remission from activity on a subset of 118 biopsies, calibrated on 42 and tested on 375. The model was additionally tested to predict the corresponding endoscopic assessment and occurrence of flares at 12 months. The system output was compared with human assessment. Diagnostic performance was reported as sensitivity, specificity, prognostic prediction through Kaplan-Meier, and hazard ratios of flares between active and remission groups. We externally validated the model in 154 biopsies (58 patients) with similar characteristics but more histologically active patients. RESULTS:The system distinguished histological activity/remission with sensitivity and specificity of 89% and 85% (PHRI), 94% and 76% (Robarts Histological Index), and 89% and 79% (Nancy Histological Index). The model predicted the corresponding endoscopic remission/activity with 79% and 82% accuracy for UC endoscopic index of severity and Paddington International virtual ChromoendoScopy ScOre, respectively. The hazard ratio for disease flare-up between histological activity/remission groups according to pathologist-assessed PHRI was 3.56, and 4.64 for AI-assessed PHRI. Both histology and outcome prediction were confirmed in the external validation cohort. CONCLUSION:We developed and validated an AI model that distinguishes histologic remission/activity in biopsies of UC and predicts flare-ups. This can expedite, standardize, and enhance histologic assessment in practice and trials.
10.1053/j.gastro.2023.02.031
Using Computer Vision to Improve Endoscopic Disease Quantification in Therapeutic Clinical Trials of Ulcerative Colitis.
Gastroenterology
BACKGROUND & AIMS:Endoscopic assessment of ulcerative colitis (UC) typically reports only the maximum severity observed. Computer vision methods may better quantify mucosal injury detail, which varies among patients. METHODS:Endoscopic video from the UNIFI clinical trial (A Study to Evaluate the Safety and Efficacy of Ustekinumab Induction and Maintenance Therapy in Participants With Moderately to Severely Active Ulcerative Colitis) comparing ustekinumab and placebo for UC were processed in a computer vision analysis that spatially mapped Mayo Endoscopic Score (MES) to generate the Cumulative Disease Score (CDS). CDS was compared with the MES for differentiating ustekinumab vs placebo treatment response and agreement with symptomatic remission at week 44. Statistical power, effect, and estimated sample sizes for detecting endoscopic differences between treatments were calculated using both CDS and MES measures. Endoscopic video from a separate phase 2 clinical trial replication cohort was performed for validation of CDS performance. RESULTS:Among 748 induction and 348 maintenance patients, CDS was lower in ustekinumab vs placebo users at week 8 (141.9 vs 184.3; P < .0001) and week 44 (78.2 vs 151.5; P < .0001). CDS was correlated with the MES (P < .0001) and all clinical components of the partial Mayo score (P < .0001). Stratification by pretreatment CDS revealed ustekinumab was more effective than placebo (P < .0001) with increasing effect in severe vs mild disease (-85.0 vs -55.4; P < .0001). Compared with the MES, CDS was more sensitive to change, requiring 50% fewer participants to demonstrate endoscopic differences between ustekinumab and placebo (Hedges' g = 0.743 vs 0.460). CDS performance in the JAK-UC replication cohort was similar to UNIFI. CONCLUSIONS:As an automated and quantitative measure of global endoscopic disease severity, the CDS offers artificial intelligence enhancement of traditional MES capability to better evaluate UC in clinical trials and potentially practice.
10.1053/j.gastro.2023.09.049
Ulcerative Colitis in Adults: A Review.
JAMA
Importance:Ulcerative colitis (UC) is a chronic inflammatory condition of the colon, with a prevalence exceeding 400 per 100 000 in North America. Individuals with UC have a lower life expectancy and are at increased risk for colectomy and colorectal cancer. Observations:UC impairs quality of life secondary to inflammation of the colon causing chronic diarrhea and rectal bleeding. Extraintestinal manifestations, such as primary sclerosing cholangitis, occur in approximately 27% of patients with UC. People with UC require monitoring of symptoms and biomarkers of inflammation (eg, fecal calprotectin), and require colonoscopy at 8 years from diagnosis for surveillance of dysplasia. Risk stratification by disease location (eg, Montreal Classification) and disease activity (eg, Mayo Score) can guide management of UC. First-line therapy for induction and maintenance of remission of mild to moderate UC is 5-aminosalicylic acid. Moderate to severe UC may require oral corticosteroids for induction of remission as a bridge to medications that sustain remission (biologic monoclonal antibodies against tumor necrosis factor [eg, infliximab], α4β7 integrins [vedolizumab], and interleukin [IL] 12 and IL-23 [ustekinumab]) and oral small molecules that inhibit janus kinase (eg, tofacitinib) or modulate sphingosine-1-phosphate (ozanimod). Despite advances in medical therapies, the highest response to these treatments ranges from 30% to 60% in clinical trials. Within 5 years of diagnosis, approximately 20% of patients with UC are hospitalized and approximately 7% undergo colectomy. The risk of colorectal cancer after 20 years of disease duration is 4.5%, and people with UC have a 1.7-fold higher risk for colorectal cancer compared with the general population. Life expectancy in people with UC is approximately 80.5 years for females and 76.7 years for males, which is approximately 5 years shorter than people without UC. Conclusions and Relevance:UC affects approximately 400 of every 100 000 people in North America. An effective treatment for mild to moderate UC is 5-aminosalicylic acid, whereas moderate to severe UC can be treated with advanced therapies that target specific inflammation pathways, including monoclonal antibodies to tumor necrosis factor, α4β7 integrins, and IL-12 and IL-23 cytokines, as well as oral small molecule therapies targeting janus kinase or sphingosine-1-phosphate.
10.1001/jama.2023.15389
Epidemiology and Pathogenesis of Ulcerative Colitis.
Du Lillian,Ha Christina
Gastroenterology clinics of North America
Ulcerative colitis (UC) is a complex chronic, immune-mediated inflammatory disorder of the colon. Factors associated with increased risk of UC include diet, particularly Western diet influences in newly industrialized nations, medications, and lifestyle factors that may influence the host's microbiome or immune response to antigens. Although much evidence identifying potential genetic and host-related factors is currently available, there are still many unanswered questions. As the global UC incidence and prevalence continues to increase, there are multiple opportunities for continued investigation to clarify our understanding of UC, identify potential predictors of disease severity, response to therapy, and novel therapeutic targets.
10.1016/j.gtc.2020.07.005
Ulcerative colitis.
Lancet (London, England)
Ulcerative colitis is a lifelong inflammatory disease affecting the rectum and colon to a variable extent. In 2023, the prevalence of ulcerative colitis was estimated to be 5 million cases around the world, and the incidence is increasing worldwide. Ulcerative colitis is thought to occur in people with a genetic predisposition following environmental exposures; gut epithelial barrier defects, the microbiota, and a dysregulated immune response are strongly implicated. Patients usually present with bloody diarrhoea, and the diagnosis is based on a combination of clinical, biological, endoscopic, and histological findings. The aim of medical management is, first, to induce a rapid clinical response and normalise biomarkers and, second, to maintain clinical remission and reach endoscopic normalisation to prevent long-term disability. Treatments for inducing remission include 5-aminosalicylic acid drugs and corticosteroids. Maintenance treatments include 5-aminosalicylic acid drugs, thiopurines, biologics (eg, anti-cytokines and anti-integrins), and small molecules (Janus kinase inhibitors and sphingosine-1-phosphate receptor modulators). Although the therapeutic options are expanding, 10-20% of patients still require proctocolectomy for medically refractory disease. The keys to breaking through this therapeutic ceiling might be the combination of therapeutics with precision and personalised medicine.
10.1016/S0140-6736(23)00966-2