For patients with inflammatory bowel disease, antibiotics can be a double-edged sword. The broad-spectrum drugs often prescribed for gut flare-ups can kill helpful microbes alongside harmful ones, sometimes worsening symptoms over time. When fighting gut inflammation, you don't always want to bring a sledgehammer to a knife fight. Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and McMaster University have identified a new compound that takes a more targeted approach. The molecule, called enterololin, suppresses a group of bacteria linked to Crohn's disease flare-ups while leaving the rest of the microbiome largely intact.

Extracting structured clinical information from radiology reports is challenging, especially in low-resource languages. This is pronounced in Crohn's disease, with sparsely represented multi-organ findings. We developed Hierarchical Structured Matching Prediction BERT (HSMP-BERT), a prompt-based model for extraction from Hebrew radiology text. In an administrative database study, we analyzed 9,683 reports from Crohn's patients imaged 2010-2023 across Israeli providers. A subset of 512 reports was radiologist-annotated for findings across six gastrointestinal organs and 15 pathologies, yielding 90 structured labels per subject. Multilabel-stratified split (66% train+validation; 33% test), preserving label prevalence. Performance was evaluated with accuracy, F1, Cohen's $κ$, AUC, PPV, NPV, and recall. On 24 organ-finding combinations with $>$15 positives, HSMP-BERT achieved mean F1 0.83$\pm$0.08 and $κ$ 0.65$\pm$0.17, outperforming the SMP zero-shot baseline (F1 0.49$\pm$0.07, $κ$ 0.06$\pm$0.07) and standard fine-tuning (F1 0.30$\pm$0.27, $κ$ 0.27$\pm$0.34; paired t-test $p < 10^{-7}$). Hierarchical inference cuts runtime 5.1$\times$ vs. traditional inference. Applied to all reports, it revealed associations among ileal wall thickening, stenosis, and pre-stenotic dilatation, plus age- and sex-specific trends in inflammatory findings. HSMP-BERT offers a scalable solution for structured extraction in radiology, enabling population-level analysis of Crohn's disease and demonstrating AI's potential in low-resource settings.

Real-world medical data often includes measurements from multiple signals that are collected at irregular and asynchronous time intervals. For example, different types of blood tests can be measured at different times and frequencies, resulting in fragmented and unevenly scattered temporal data. Similar issues of irregular sampling of different attributes occur in other domains, such as monitoring of large systems using event log files or the spread of fake news on social networks. Effectively learning from such data requires models that can handle sets of temporally sparse and heterogeneous signals. In this paper, we propose Graph Mixing Additive Networks (GMAN), a novel and interpretable-by-design model for learning over irregular sets of temporal signals. Our method achieves state-of-the-art performance in real-world medical tasks, including a 4-point increase in the AUROC score of in-hospital mortality prediction, compared to existing methods. We further showcase GMAN's flexibility by applying it to a fake news detection task. We demonstrate how its interpretability capabilities, including node-level, graph-level, and subset-level importance, allow for transition phases detection and gaining medical insights with real-world high-stakes implications. Finally, we provide theoretical insights on GMAN expressive power.

Crohn's disease and intestinal tuberculosis share many overlapping features such as clinical, radiological, endoscopic, and histological features - particularly granulomas, making it challenging to clinically differentiate them. Our research leverages 3D CTE scans, computer vision, and machine learning to improve this differentiation to avoid harmful treatment mismanagement such as unnecessary anti-tuberculosis therapy for Crohn's disease or exacerbation of tuberculosis with immunosuppressants. Our study proposes a novel method to identify radiologist - identified biomarkers such as VF to SF ratio, necrosis, calcifications, comb sign and pulmonary TB to enhance accuracy. We demonstrate the effectiveness by using different ML techniques on the features extracted from these biomarkers, computing SHAP on XGBoost for understanding feature importance towards predictions, and comparing against SOTA methods such as pretrained ResNet and CTFoundation.

Disease identification is a core, routine activity in observational health research. Cohorts impact downstream analyses, such as how a condition is characterized, how patient risk is defined, and what treatments are studied. It is thus critical to ensure that selected cohorts are representative of all patients, independently of their demographics or social determinants of health. While there are multiple potential sources of bias when constructing phenotype definitions which may affect their fairness, it is not standard in the field of phenotyping to consider the impact of different definitions across subgroups of patients. In this paper, we propose a set of best practices to assess the fairness of phenotype definitions. We leverage established fairness metrics commonly used in predictive models and relate them to commonly used epidemiological cohort description metrics. We describe an empirical study for Crohn's disease and diabetes type 2, each with multiple phenotype definitions taken from the literature across two sets of patient subgroups (gender and race). We show that the different phenotype definitions exhibit widely varying and disparate performance according to the different fairness metrics and subgroups. We hope that the proposed best practices can help in constructing fair and inclusive phenotype definitions.

A new study finds that an artificial intelligence model can predict whether Crohn's disease will recur after surgery. A deep learning model trained to analyze histological images of surgical specimens accurately classified patients with and without Crohn's disease recurrence, investigators report in The American Journal of Pathology. According to researchers, more than 500,000 individuals in the United States have Crohn's disease. Crohn's disease is a chronic inflammatory bowel disease that damages the digestive system lining. It can cause digestive system inflammation, which may result in abdominal pain, severe diarrhea, exhaustion, weight loss, and malnutrition.

Today, Motilent, the first company to specialise in the assessment of digestive diseases using AI medical image analysis, announces a total of  £1.2M National Institute for Health and Care Research (NIHR) funding to develop and roll out its  technology into more than 10 UK hospitals, including UCLH, Nottingham University Hospital and Frimley Park Hospital. Crohn’s Disease is a painful, debilitating inflammatory bowel condition that affects 115,000 people in the UK, with 33% diagnosed before age 21. Currently, anti-inflammatory medications are the standard of care. However, for the 40% who do not experience inflammatory symptoms, these medications are ineffective and can cause severe side effects, as well as costing the UK economy over £280 million every year. Currently, 69% of the UK population experiences persistent gut issues, […]

According to a study, published in the journal Genome Medicine, a team of researchers at Rutgers University was able to develop an artificial intelligence (AI) method that may provide more insight into Crohn's disease. Crohn's disease, an inflammatory bowel disease, is characterized by various traits that can affect any part of the gut. It is estimated that the disease could affect close to 800,000 adults in the U.S., according to the study's co-authors. As such, researchers have pivoted their attention to AI for a more comprehensive understanding of identifying and treating Crohn's disease. For the study, the team investigated genetic signatures associated with the illness in 111 participants.

The Rutgers-led study, published in the journal Genome Medicine, used artificial intelligence to examine genetic signatures of Crohn's in 111 people. The method revealed previously undiscovered genes linked to the disease, and accurately predicted whether thousands of other people had the disease. "Our method is not a clinical diagnosis tool, but it generates interesting observations that need to be followed up," said senior author Yana Bromberg, an associate professor in the Department of Biochemistry and Microbiology at Rutgers University-New Brunswick. "Further experimental work could reveal the molecular reasons behind some forms of Crohn's disease and, potentially, lead to better treatments of the disease." Crohn's affects up to 780,000 people in the United States, the study notes.

Researchers have developed a computer method using AI techniques that could lead to a deeper understanding of, and treatment for, Crohn's disease, a chronic gastrointestinal inflammation that affects up to 780,000 people in the US alone. The team from Rutgers University, which published the results of the study in the journal Genome Medicine, used artificial intelligence to examine genetic signatures of Crohn's in 111 people, including 64 people with a Crohn's disease diagnosis. Researchers then used an artificial intelligence method, known as AVA,Dx (Analysis of Variation for Association with Disease), to identify genes whose functions changed more in Crohn's patients than in healthy people. The researchers were able to highlight known Crohn's disease genes, as well as new potential Crohn's genes. AVA,Dx also identified 16 percent of Crohn's patients at 99 percent precision, and 58 percent of the patients with 82 percent precision in over 3,000 individuals from separately sequenced panels.