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

Machine and deep learning in inflammatory bowel disease

PURPOSE OF REVIEW

The Management of inflammatory bowel disease (IBD) has evolved with the introduction and widespread adoption of biologic agents; however, the advent of artificial intelligence technologies like machine learning and deep learning presents another watershed moment in IBD treatment. Interest in these methods in IBD research has increased over the past 10 years, and they offer a promising path to better clinical outcomes for IBD patients.

RECENT FINDINGS

Developing new tools to evaluate IBD and inform clinical management is challenging because of the expansive volume of data and requisite manual interpretation of data. Recently, machine and deep learning models have been used to streamline diagnosis and evaluation of IBD by automating review of data from several diagnostic modalities with high accuracy. These methods decrease the amount of time that clinicians spend manually reviewing data to formulate an assessment.

SUMMARY

Interest in machine and deep learning is increasing in medicine, and these methods are poised to revolutionize the way that we treat IBD. Here, we highlight the recent advances in using these technologies to evaluate IBD and discuss the ways that they can be leveraged to improve clinical outcomes.

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

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.

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

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.

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

INTRODUCTION

Patients 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.

METHOD

Single 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.

RESULTS

Referral 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.

CONCLUSION

Our 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.