Early histological findings may predict the clinical phenotype in Crohn's colitis

Deep Learning Models Capture Histological Disease Activity in Crohn's Disease and Ulcerative Colitis with High Fidelity

BACKGROUND AND AIMS

Histological disease activity in inflammatory bowel disease [IBD] is associated with clinical outcomes and is an important endpoint in drug development. We developed deep learning models for automating histological assessments in IBD.

METHODS

Histology images of intestinal mucosa from phase 2 and phase 3 clinical trials in Crohn's disease [CD] and ulcerative colitis [UC] were used to train artificial intelligence [AI] models to predict the Global Histology Activity Score [GHAS] for CD and Geboes histopathology score for UC. Three AI methods were compared. AI models were evaluated on held-back testing sets, and model predictions were compared against an expert central reader and five independent pathologists.

RESULTS

The model based on multiple instance learning and the attention mechanism [SA-AbMILP] demonstrated the best performance among competing models. AI-modelled GHAS and Geboes subgrades matched central readings with moderate to substantial agreement, with accuracies ranging from 65% to 89%. Furthermore, the model was able to distinguish the presence and absence of pathology across four selected histological features, with accuracies for colon in both CD and UC ranging from 87% to 94% and for CD ileum ranging from 76% to 83%. For both CD and UC and across anatomical compartments [ileum and colon] in CD, comparable accuracies against central readings were found between the model-assigned scores and scores by an independent set of pathologists.

CONCLUSIONS

Deep learning models based upon GHAS and Geboes scoring systems were effective at distinguishing between the presence and absence of IBD microscopic disease activity.

AI-luminating Artificial Intelligence in Inflammatory Bowel Diseases: A Narrative Review on the Role of AI in Endoscopy, Histology, and Imaging for IBD

Endoscopy, histology, and cross-sectional imaging serve as fundamental pillars in the detection, monitoring, and prognostication of inflammatory bowel disease (IBD). However, interpretation of these studies often relies on subjective human judgment, which can lead to delays, intra- and interobserver variability, and potential diagnostic discrepancies. With the rising incidence of IBD globally coupled with the exponential digitization of these data, there is a growing demand for innovative approaches to streamline diagnosis and elevate clinical decision-making. In this context, artificial intelligence (AI) technologies emerge as a timely solution to address the evolving challenges in IBD. Early studies using deep learning and radiomics approaches for endoscopy, histology, and imaging in IBD have demonstrated promising results for using AI to detect, diagnose, characterize, phenotype, and prognosticate IBD. Nonetheless, the available literature has inherent limitations and knowledge gaps that need to be addressed before AI can transition into a mainstream clinical tool for IBD. To better understand the potential value of integrating AI in IBD, we review the available literature to summarize our current understanding and identify gaps in knowledge to inform future investigations.

CB-HRNet: A Class-Balanced High-Resolution Network for the evaluation of endoscopic activity in patients with ulcerative colitis

Endoscopic evaluation is the key to the management of ulcerative colitis (UC). However, there is interobserver variability in interpreting endoscopic images among gastroenterologists. Furthermore, it is time-consuming. Convolutional neural networks (CNNs) can help overcome these obstacles and has yielded preliminary positive results. We aimed to develop a new CNN-based algorithm to improve the performance for evaluation tasks of endoscopic images in patients with UC. A total of 12,163 endoscopic images from 308 patients with UC were collected from January 2014 to December 2021. The training set and test set images were randomly divided into 37,515 and 3191 after excluding possible interference and data augmentation. Mayo Endoscopic Subscores (MES) were predicted by different CNN-based models with different loss functions. Their performances were evaluated by several metrics. After comparing the results of different CNN-based models with different loss functions, High-Resolution Network with Class-Balanced Loss achieved the best performances in all MES classification subtasks. It was especially great at determining endoscopic remission in UC, which achieved a high accuracy of 95.07% and good performances in other evaluation metrics with sensitivity 92.87%, specificity 95.41%, kappa coefficient 0.8836, positive predictive value 93.44%, negative predictive value 95.00% and area value under the receiver operating characteristic curve 0.9834, respectively. In conclusion, we proposed a new CNN-based algorithm, Class-Balanced High-Resolution Network (CB-HRNet), to evaluate endoscopic activity of UC with excellent performance. Besides, we made an open-source dataset and it can be a new benchmark in the task of MES classification.

Automatically detecting Crohn's disease and Ulcerative Colitis from endoscopic imaging

BACKGROUND

The SI-CURA project (Soluzioni Innovative per la gestione del paziente e il follow up terapeutico della Colite UlceRosA) is an Italian initiative aimed at the development of artificial intelligence solutions to discriminate pathologies of different nature, including inflammatory bowel disease (IBD), namely Ulcerative Colitis (UC) and Crohn's disease (CD), based on endoscopic imaging of patients (P) and healthy controls (N).

METHODS

In this study we develop a deep learning (DL) prototype to identify disease patterns through three binary classification tasks, namely (1) discriminating positive (pathological) samples from negative (healthy) samples (P vs N); (2) discrimination between Ulcerative Colitis and Crohn's Disease samples (UC vs CD) and, (3) discrimination between Ulcerative Colitis and negative (healthy) samples (UC vs N).

RESULTS

The model derived from our approach achieves a high performance of Matthews correlation coefficient (MCC) > 0.9 on the test set for P versus N and UC versus N, and MCC > 0.6 on the test set for UC versus CD.

CONCLUSION

Our DL model effectively discriminates between pathological and negative samples, as well as between IBD subgroups, providing further evidence of its potential as a decision support tool for endoscopy-based diagnosis.

Artificial Intelligence for Disease Assessment in Inflammatory Bowel Disease: How Will it Change Our Practice?

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.

Histopathological Features in Colonic Biopsies at Diagnosis Predict Long-term Disease Course in Patients with Crohn's Disease

BACKGROUND AND AIMS

Crohn's disease [CD] is characterised by a heterogeneous disease course. Patient stratification at diagnosis using clinical, serological, or genetic markers does not predict disease course sufficiently to facilitate clinical decision making. The current study aimed to investigate the additive predictive value of histopathological features to discriminate between a long-term mild and severe disease course.

METHODS

Diagnostic biopsies from treatment-naïve CD patients with mild or severe disease courses in the first 10 years after diagnosis were reviewed by two gastrointestinal pathologists after developing a standardised form comprising 15 histopathological features. Multivariable logistic regression models were built to identify predictive features and compute receiver operating characteristic [ROC] curves. Models were internally validated using bootstrapping to obtain optimism-corrected performance estimates.

RESULTS

In total, 817 biopsies from 137 patients [64 mild, 73 severe cases] were included. Using clinical baseline characteristics, disease course could only moderately be predicted (area under receiver operating characteristic curve [AUROC]: 0.738 [optimism 0.018], 95% confidence interval [CI] 0.65-0.83, sensitivity 83.6%, specificity 53.1%). When adding histopathological features, in colonic biopsies a combination of [1] basal plasmacytosis, [2] severe lymphocyte infiltration in lamina propria, [3] Paneth cell metaplasia, and [4] absence of ulcers were identified and resulted in significantly better prediction of a severe course (AUROC: 0.883 [optimism 0.033], 95% CI 0.82-0.94, sensitivity 80.4%, specificity 84.2%).

CONCLUSIONS

In this first study investigating the additive predictive value of histopathological features in biopsies at CD diagnosis, we found that certain features of chronic inflammation in colonic biopsies contributed to prediction of a severe disease course, thereby presenting a novel approach to improving stratification and facilitating clinical decision making.

Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review

BACKGROUND AND AIMS

There is increasing interest in machine learning-based prediction models in inflammatory bowel diseases [IBD]. We synthesised and critically appraised studies comparing machine learning vs traditional statistical models, using routinely available clinical data for risk prediction in IBD.

METHODS

Through a systematic review till January 1, 2021, we identified cohort studies that derived and/or validated machine learning models, based on routinely collected clinical data in patients with IBD, to predict the risk of harbouring or developing adverse clinical outcomes, and reported its predictive performance against a traditional statistical model for the same outcome. We appraised the risk of bias in these studies using the Prediction model Risk of Bias ASsessment [PROBAST] tool.

RESULTS

We included 13 studies on machine learning-based prediction models in IBD, encompassing themes of predicting treatment response to biologics and thiopurines and predicting longitudinal disease activity and complications and outcomes in patients with acute severe ulcerative colitis. The most common machine learning models used were tree-based algorithms, which are classification approaches achieved through supervised learning. Machine learning models outperformed traditional statistical models in risk prediction. However, most models were at high risk of bias, and only one was externally validated.

CONCLUSIONS

Machine learning-based prediction models based on routinely collected data generally perform better than traditional statistical models in risk prediction in IBD, though frequently have high risk of bias. Future studies examining these approaches are warranted, with special focus on external validation and clinical applicability.

Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases

The use of artificial intelligence-based tools is regarded as a promising approach to increase clinical efficiency in diagnostic imaging, improve the interpretability of results, and support decision-making for the detection and prevention of diseases. Radiology, endoscopy and pathology images are suitable for deep-learning analysis, potentially changing the way care is delivered in gastroenterology. The aim of this review is to examine the key aspects of different neural network architectures used for the evaluation of gastrointestinal conditions, by discussing how different models behave in critical tasks, such as lesion detection or characterization (i.e. the distinction between benign and malignant lesions of the esophagus, the stomach and the colon). To this end, we provide an overview on recent achievements and future prospects in deep learning methods applied to the analysis of radiology, endoscopy and histologic whole-slide images of the gastrointestinal tract.

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.

Potential for Standardization and Automation for Pathology and Endoscopy in Inflammatory Bowel Disease

Automated image analysis methods have shown potential for replicating expert interpretation of histology and endoscopy images, which traditionally require highly specialized and experienced reviewers. Inflammatory bowel disease (IBD) diagnosis, severity assessment, and treatment decision-making require multimodal expert data interpretation and integration, which could be significantly aided by applications of machine learning analyses. This review introduces fundamental concepts of machine learning for imaging analysis and highlights research and development of automated histology and endoscopy interpretation in IBD. Proof-of-concept studies strongly suggest that histologic and endoscopic images can be interpreted with similar accuracy as knowledge experts. Encouraging results support the potential of automating existing disease activity scoring instruments with high reproducibility, speed, and accessibility, therefore improving the standardization of IBD assessment. Though challenges surrounding ground truth definitions, technical barriers, and the need for extensive multicenter evaluation must be resolved before clinical implementation, automated image analysis is likely to both improve access to standardized IBD assessment and advance the fundamental concepts of how disease is measured.

Identifying Patients With Inflammatory Bowel Diseases at High vs Low Risk of Complications

People with Crohn's disease and ulcerative colitis have varying presentations and clinical consequences of their disease. Patients commonly ask about their prognosis, and what this diagnosis means for them. They are asking their clinicians to predict the future. The importance of predicting the course of any disease is to guide patient expectations and to guide treatment decisions. In the past decade the strategy of inflammatory bowel disease (IBD) treatment has shifted to treat patients earlier in the course of their disease, before irreversible damage occurs. Treatment approaches for disease categorized as mild, moderate or severe has most often been based on a current assessment of symptoms or disease activity without including a longitudinal assessment of a patient's disease course including past disease complications and surgeries. While a patient's current disease activity most typically drives these treatment decisions, optimally, treatment decisions would be made accounting for past disease activity and complications and the predicted future disease course. When developing a treatment plan for an individual patient, the immediate goal is to treat the current disease activity for relief of symptoms, and the long-term goal is to prevent progression of their disease due to complications. Since not all patients will progress to a complicated disease course, it is important to be able to select the right patients for the right therapy. Therefore, developing methods of stratifying patients into low-risk versus high-risk of complications will be an important aspect of treating IBD now and in the future.

Application of Artificial Intelligence to Gastroenterology and Hepatology

Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.

Effect of weaning age on the small intestine mucosa of rats

Weaning of mammalian progeny is associated with a change in food composition and mother-offspring separation. Weaning results in a critical period of low voluntary feed intake, during which the animal is adapting to the starter diet. To evaluate the effects of weaning age on morphological changes that occur in the intestines of rats, we assessed intestinal histomorphometry and somatic growth in 21-days-old pups and 90-days-old mature rats that had been weaned early (day 16), normally (day 21), or late (day 26). Early weaning resulted in deeper crypts, lower villous/crypt ratio, and a smaller villous area on day 21. Crown-tail length correlated positively with the crypt depth and negatively with the villous/crypt ratio. At age 90 days, early weaned animals had shallower crypts, a greater villous/crypt ratio, and a smaller villous area compared with their normally weaned counterparts. The rats' crown-tail length correlated negatively with the crypt depth and positively with the villous/crypt ratio. Early weaning significantly affects the intestinal mucosa, which may impact food absorption and lead to differences in somatic growth compared with late weaning. Over time there may be a phase of compensation with increased villus height and crypt depth.