Artificial intelligence and inflammatory bowel disease: practicalities and future prospects

Impact of artificial intelligence on prognosis, shared decision-making, and precision medicine for patients with inflammatory bowel disease: a perspective and expert opinion

INTRODUCTION

Artificial intelligence (AI) is expected to impact all facets of inflammatory bowel disease (IBD) management, including disease assessment, treatment decisions, discovery and development of new biomarkers and therapeutics, as well as clinician-patient communication.

AREAS COVERED

This perspective paper provides an overview of the application of AI in the clinical management of IBD through a review of the currently available AI models that could be potential tools for prognosis, shared decision-making, and precision medicine. This overview covers models that measure treatment response based on statistical or machine-learning methods, or a combination of the two. We briefly discuss a computational model that allows integration of immune/biological system knowledge with mathematical modeling and also involves a 'digital twin', which allows measurement of temporal trends in mucosal inflammatory activity for predicting treatment response. A viewpoint on AI-enabled wearables and nearables and their use to improve IBD management is also included.

EXPERT OPINION

Although challenges regarding data quality, privacy, and security; ethical concerns; technical limitations; and regulatory barriers remain to be fully addressed, a growing body of evidence suggests a tremendous potential for integration of AI into daily clinical practice to enable precision medicine and shared decision-making.

Artificial intelligence & clinical nutrition: What the future might have in store

Artificial Intelligence (AI) is a rapidly emerging technology in healthcare that has the potential to revolutionise clinical nutrition. AI can assist in analysing complex data, interpreting medical images, and providing personalised nutrition interventions for patients. Clinical nutrition is a critical aspect of patient care, and AI can help clinicians make more informed decisions regarding patients' nutritional requirements, disease prevention, and management. AI algorithms can analyse large datasets to identify novel associations between diet and disease outcomes, enabling clinicians to make evidence-based nutritional recommendations. AI-powered devices and applications can also assist in tracking dietary intake, providing feedback, and motivating patients towards healthier food choices. However, the adoption of AI in clinical nutrition raises several ethical and regulatory concerns, such as data privacy and bias. Further research is needed to assess the clinical effectiveness and safety of AI-powered nutrition interventions. In conclusion, AI has the potential to transform clinical nutrition, but its integration into clinical practice should be carefully monitored to ensure patient safety and benefit. This article discusses the current and future applications of AI in clinical nutrition and highlights its potential benefits.

The Pediatric Crohn Disease Morbidity Index (PCD-MI): Development of a Tool to Assess Long-Term Disease Burden Using a Data-Driven Approach

BACKGROUND/OBJECTIVE

Heterogeneity and chronicity of Crohn disease (CD) make prediction of outcomes difficult. To date, no longitudinal measure can quantify burden over a patient's disease course, preventing assessment and integration into predictive modeling. Here, we aimed to demonstrate the feasibility of constructing a data driven, longitudinal disease burden score.

METHODS

Literature was reviewed for tools used in assessment of CD activity. Themes were identified to construct a pediatric CD morbidity index (PCD-MI). Scores were assigned to variables. Data were extracted automatically from the electronic patient records at Southampton Children's Hospital, diagnosed from 2012 to 2019 (inclusive). PCD-MI scores were calculated, adjusted for duration of follow up and assessed for variation (ANOVA) and distribution (Kolmogorov-Smirnov).

RESULTS

Nineteen clinical/biological features across five themes were included in the PCD-MI including blood/fecal/radiological/endoscopic results, medication usage, surgery, growth parameters, and extraintestinal manifestations. Maximal score was 100 after accounting for follow-up duration. PCD-MI was assessed in 66 patients, mean age 12.5 years. Following quality filtering, 9528 blood/fecal test results and 1309 growth measures were included. Mean PCD-MI score was 14.95 (range 2.2-32.5); data were normally distributed ( P = 0.2) with 25% of patients having a PCD-MI < 10. There was no difference in the mean PCD-MI when split by year of diagnosis, F -statistic 1.625, P = 0.147.

CONCLUSIONS

PCD-MI is a calculatable measure for a cohort of patients diagnosed over an 8-year period, integrating a wide-range of data with potential to determine high or low disease burden. Future iterations of the PCD-MI require refinement of included features, optimized scores, and validation on external cohorts.

Advances in Endoscopy for Pediatric Inflammatory Bowel Disease

Endoscopic characterization of pediatric inflammatory bowel disease (IBD) has developed in accordance with advances in treatment and improved understanding of disease progression and complications. Reliable and consistent endoscopic reporting practices and tools continue to evolve. The roles of endoscopic ultrasonography, capsule endoscopy, and deep enteroscopy in the care of children and adolescents with IBD are beginning to be clarified. Opportunities for therapeutic intervention with endoscopy in pediatric IBD, including endoscopic balloon dilation and electroincision therapy, require further study. This review discusses the current utility of endoscopic assessment in Pediatric Inflammatory Bowel Disease, as well as emerging and evolving techniques to improve patient care.

Using machine learning to impact on long-term clinical care: principles, challenges, and practicalities

The rise of machine learning in healthcare has significant implications for paediatrics. Long-term conditions with significant disease heterogeneity comprise large portions of the routine work performed by paediatricians. Improving outcomes through discovery of disease and treatment prediction models, alongside novel subgroup clustering of patients, are some of the areas in which machine learning holds significant promise. While artificial intelligence has percolated into routine use in our day to day lives through advertising algorithms, song or movie selections and sifting of spam emails, the ability of machine learning to utilise highly complex and dimensional data has not yet reached its full potential in healthcare. In this review article, we discuss some of the foundations of machine learning, including some of the basic algorithms. We emphasise the importance of correct utilisation of machine learning, including adequate data preparation and external validation. Using nutrition in preterm infants and paediatric inflammatory bowel disease as examples, we discuss the evidence and potential utility of machine learning in paediatrics. Finally, we review some of the future applications, alongside challenges and ethical considerations related to application of artificial intelligence. IMPACT: Machine learning is a widely used term; however, understanding of the process and application to healthcare is lacking. This article uses clinical examples to explore complex machine learning terms and algorithms. We discuss limitations and potential future applications within paediatrics and neonatal medicine.