Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease – What the Clinician Needs to Know

Chen, David; Fulmer, Clifton G.; Gordon, Ilyssa O.; Syed, Sana; Stidham, Ryan W.; Casteele, Niels Vande; Qin, Yi; Falloon, Katherine; Cohen, Benjamin L.; Wyllie, Robert; Rieder, Florian

doi:10.1093/ecco-jcc/jjab169

Cited by 5 publications

(7 citation statements)

References 133 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of artificial intelligence algorithms ( 263 ) may be able to capture and decipher complex, nonlinear relationships, that otherwise would be left unconsidered, and could be consider as an upgrade of the 4P medicine. So far, the only application of artificial intelligence in the field of IBD is that of endoscopy, field in which it proved useful in detecting preneoplastic alterations ( 264 ). A broader clinical application of artificial intelligence should be sought in future studies.…”

Section: Discussionmentioning

confidence: 99%

Personalize, participate, predict, and prevent: 4Ps in inflammatory bowel disease

et al. 2023

View full text Add to dashboard Cite

Inflammatory bowel disease (IBD), which includes Crohn’s disease (CD) and ulcerative colitis (UC), is a complex, immune-mediated, disorder which leads to several gastrointestinal and systemic manifestations determining a poor quality of life, disability, and other negative health outcomes. Our knowledge of this condition has greatly improved over the last few decades, and a comprehensive management should take into account both biological (i.e., disease-related, patient-related) and non-biological (i.e., socioeconomic, cultural, environmental, behavioral) factors which contribute to the disease phenotype. From this point of view, the so called 4P medicine framework, including personalization, prediction, prevention, and participation could be useful for tailoring ad hoc interventions in IBD patients. In this review, we discuss the cutting-edge issues regarding personalization in special settings (i.e., pregnancy, oncology, infectious diseases), patient participation (i.e., how to communicate, disability, tackling stigma and resilience, quality of care), disease prediction (i.e., faecal markers, response to treatments), and prevention (i.e., dysplasia through endoscopy, infections through vaccinations, and post-surgical recurrence). Finally, we provide an outlook discussing the unmet needs for implementing this conceptual framework in clinical practice.

show abstract

Section: Discussionmentioning

confidence: 99%

Personalize, participate, predict, and prevent: 4Ps in inflammatory bowel disease

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Recently, AI has also been applied to various tasks in IBD. [20][21][22] However, IBD is still not in the center of AIbased research. AI should primarily be used for clinical constellations with suboptimal performance of conventional diagnostics.…”

Section: Endoscopistsmentioning

confidence: 99%

Artificial‐intelligence‐based decision support tools for the differential diagnosis of colitis

Guimarães

Finkler

Reichert

et al. 2023

Eur J Clin Investigation

View full text Add to dashboard Cite

Background Whereas Artificial Intelligence (AI) based tools have recently been introduced in the field of gastroenterology, application in inflammatory bowel disease (IBD) is in its infancies. We established AI‐based algorithms to distinguish IBD from infectious and ischemic colitis using endoscopic images and clinical data. Methods First, we trained and tested a Convolutional Neural Network (CNN) using 1796 real‐world images from 494 patients, presenting with three diseases (IBD [n = 212], ischemic colitis [n = 157], and infectious colitis [n = 125]). Moreover, we evaluated a Gradient Boosted Decision Trees (GBDT) algorithm using five clinical parameters as well as a hybrid approach (CNN + GBDT). Patients and images were randomly split into two completely independent datasets. The proposed approaches were benchmarked against each other and three expert endoscopists on the test set. Results For the image‐based CNN, the GBDT algorithm and the hybrid approach global accuracies were .709, .792, and .766, respectively. Positive predictive values were .602, .702, and .657. Global areas under the receiver operating characteristics (ROC) and precision recall (PR) curves were .727/.585, .888/.823, and .838/.733, respectively. Global accuracy did not differ between CNN and endoscopists (.721), but the clinical parameter‐based GBDT algorithm outperformed CNN and expert image classification. Conclusions Decision support systems exclusively based on endoscopic image analysis for the differential diagnosis of colitis, representing a complex clinical challenge, seem not yet to be ready for primetime and more diverse image datasets may be necessary to improve performance in future development. The clinical value of the proposed clinical parameters algorithm should be evaluated in prospective cohorts.

show abstract

“…Natural language processing (NLP) has been proposed as a potential tool to retrieve clinically meaningful information from free-text documents in gastroenterology. [10][11][12] The study aimed to evaluate the impact of introducing a direct-access IBD-physician delivered endoscopy pathway for new primary care referrals with suspected IBD.…”

Section: Endoscopymentioning

confidence: 99%

“…Natural language processing (NLP) has been proposed as a potential tool to retrieve clinically meaningful information from free-text documents in gastroenterology 10–12…”

Section: Introductionmentioning

confidence: 99%

Impact of direct-access IBD physician delivered endoscopy on clinical outcomes: a pre-implementation and post-implementation study

Stammers

Rahmany

Downey

et al. 2022

Frontline Gastroenterol

View full text Add to dashboard Cite

IntroductionPatients with suspected inflammatory bowel disease (IBD) referred from primary care often face diagnostic and treatment delays. This study aimed to compare a novel direct-access IBD endoscopy pathway with the traditional care model.MethodSingle centre real-world study analysing primary care referrals with suspected IBD. Group A: patients triaged to direct-access IBD endoscopy. Group B: patients undergoing traditional outpatient appointments before the availability of direct-access IBD endoscopy. Demographics, fecal calprotectin (FCP), C-reactive protein (CRP), disease activity score, endoscopy findings, treatment and follow-up were collected and statistically analysed. Ranked semantic analysis of IBD symptoms contained within referral letters was performed.ResultsReferral letters did not differ significantly in Groups A and B. Demographic data, FCP and CRP values were similar. Referral to treatment time (RTT) at the time of IBD endoscopy was reduced from 177 days (Group B) to 24 days (Group A) (p<0.0001). Diagnostic yield of IBD was 35.6% (Group B) versus 62.0% (Group A) (p=0.0003). 89.2% of patients underwent colonoscopy in Group B versus 46.4% in Group A. DNA rates were similar in both groups. The direct to IBD endoscopy pathway saved 100% of initial IBD consultant clinics with a 2.5-fold increase in IBD nurse-led follow-up.ConclusionOur novel pathway resulted in an 86% reduction in RTT with associated increased diagnostic yield while saving 100% of initial IBD consultant outpatient appointments. Replication in other trusts may improve patient experience and accelerate time to diagnosis/treatment while optimising the use of healthcare resources.

show abstract

Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease – What the Clinician Needs to Know

Cited by 5 publications

References 133 publications

Personalize, participate, predict, and prevent: 4Ps in inflammatory bowel disease

Personalize, participate, predict, and prevent: 4Ps in inflammatory bowel disease

Artificial‐intelligence‐based decision support tools for the differential diagnosis of colitis

Impact of direct-access IBD physician delivered endoscopy on clinical outcomes: a pre-implementation and post-implementation study

Contact Info

Product

Resources

About