Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease

Harnessing technology for infectious disease response in conflict zones: Challenges, innovations, and policy implications

Epidemic outbreaks of infectious diseases in conflict zones are complex threats to public health and humanitarian activities that require creativity approaches of reducing their damage. This narrative review focuses on the technology intersection with infectious disease response in conflict zones, and complexity of healthcare infrastructure, population displacement, and security risks. This narrative review explores how conflict-related destruction is harmful towards healthcare systems and the impediments to disease surveillance and response activities. In this regards, the review also considered the contributions of technological innovations, such as the improvement of epidemiological surveillance, mobile health (mHealth) technologies, genomic sequencing, and surveillance technologies, in strengthening infectious disease management in conflict settings. Ethical issues related to data privacy, security and fairness are also covered. By advisement on policy that focuses on investment in surveillance systems, diagnostic capacity, capacity building, collaboration, and even ethical governance, stakeholders can leverage technology to enhance the response to infectious disease in conflict settings and, thus, protect the global health security. This review is full of information for researchers, policymakers, and practitioners who are dealing with the issues of infectious disease outbreaks in conflicts worn areas.

Precision Medicine in Inflammatory Bowel Disease: A Spotlight on Emerging Molecular Biomarkers

Inflammatory bowel diseases (IBD) remain challenging in terms of understanding their causes and in terms of diagnosing, treating, and monitoring patients. Modern diagnosis combines biomarkers, imaging, and endoscopic methods. Common biomarkers like CRP and fecal calprotectin, while invaluable tools, have limitations and are not entirely specific to IBD. The limitations of existing markers and the invasiveness of endoscopic procedures highlight the need to discover and implement new markers. With an ideal biomarker, we could predict the risk of disease development, as well as the possibility of response to a particular therapy, which would be significant in elucidating the pathogenesis of the disease. Recent research in the fields of machine learning, proteomics, epigenetics, and gut microbiota provides further insight into the pathogenesis of the disease and is also revealing new biomarkers. New markers, such as BAFF, PGE-MUM, oncostatin M, microRNA panels, αvβ6 antibody, and S100A12 from stool, are increasingly being identified, with αvβ6 antibody and oncostatin M being potentially close to being presented into clinical practice. However, the specificity of certain markers still remains problematic. Furthermore, the use of expensive and less accessible technology for detecting new markers, such as microRNAs, represents a limitation for widespread use in clinical practice. Nevertheless, the need for non-invasive, comprehensive markers is becoming increasingly important regarding the complexity of treatment and overall management of IBD.

The Role of Artificial Intelligence in the Diagnosis and Treatment of Ulcerative Colitis

BACKGROUND AND OBJECTIVES

This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition.

METHOD

A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English.

RESULTS

Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis.

CONCLUSIONS

When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine.

Endoscopy-based IBD identification by a quantized deep learning pipeline

BACKGROUND

Discrimination between patients affected by inflammatory bowel diseases and healthy controls on the basis of endoscopic imaging is an challenging problem for machine learning models. Such task is used here as the testbed for a novel deep learning classification pipeline, powered by a set of solutions enhancing characterising elements such as reproducibility, interpretability, reduced computational workload, bias-free modeling and careful image preprocessing.

RESULTS

First, an automatic preprocessing procedure is devised, aimed to remove artifacts from clinical data, feeding then the resulting images to an aggregated per-patient model to mimic the clinicians decision process. The predictions are based on multiple snapshots obtained through resampling, reducing the risk of misleading outcomes by removing the low confidence predictions. Each patient's outcome is explained by returning the images the prediction is based upon, supporting clinicians in verifying diagnoses without the need for evaluating the full set of endoscopic images. As a major theoretical contribution, quantization is employed to reduce the complexity and the computational cost of the model, allowing its deployment on small power devices with an almost negligible 3% performance degradation. Such quantization procedure holds relevance not only in the context of per-patient models but also for assessing its feasibility in providing real-time support to clinicians even in low-resources environments. The pipeline is demonstrated on a private dataset of endoscopic images of 758 IBD patients and 601 healthy controls, achieving Matthews Correlation Coefficient 0.9 as top performance on test set.

CONCLUSION

We highlighted how a comprehensive pre-processing pipeline plays a crucial role in identifying and removing artifacts from data, solving one of the principal challenges encountered when working with clinical data. Furthermore, we constructively showed how it is possible to emulate clinicians decision process and how it offers significant advantages, particularly in terms of explainability and trust within the healthcare context. Last but not least, we proved that quantization can be a useful tool to reduce the time and resources consumption with an acceptable degradation of the model performs. The quantization study proposed in this work points up the potential development of real-time quantized algorithms as valuable tools to support clinicians during endoscopy procedures.

Artificial Intelligence in Inflammatory Bowel Disease Endoscopy: Implications for Clinical Trials

Artificial intelligence shows promise for clinical research in inflammatory bowel disease endoscopy. Accurate assessment of endoscopic activity is important in clinical practice and inflammatory bowel disease clinical trials. Emerging artificial intelligence technologies can increase efficiency and accuracy of assessing the baseline endoscopic appearance in patients with inflammatory bowel disease and the impact that therapeutic interventions may have on mucosal healing in both of these contexts. In this review, state-of-the-art endoscopic assessment of mucosal disease activity in inflammatory bowel disease clinical trials is described, covering the potential for artificial intelligence to transform the current paradigm, its limitations, and suggested next steps. Site-based artificial intelligence quality evaluation and inclusion of patients in clinical trials without the need for a central reader is proposed; for following patient progress, a second reading using AI alongside a central reader with expedited reading is proposed. Artificial intelligence will support precision endoscopy in inflammatory bowel disease and is on the threshold of advancing inflammatory bowel disease clinical trial recruitment.

The Role of Artificial Intelligence in Monitoring Inflammatory Bowel Disease-The Future Is Now

Crohn's disease and ulcerative colitis remain debilitating disorders, characterized by progressive bowel damage and possible lethal complications. The growing number of applications for artificial intelligence in gastrointestinal endoscopy has already shown great potential, especially in the field of neoplastic and pre-neoplastic lesion detection and characterization, and is currently under evaluation in the field of inflammatory bowel disease management. The application of artificial intelligence in inflammatory bowel diseases can range from genomic dataset analysis and risk prediction model construction to the disease grading severity and assessment of the response to treatment using machine learning. We aimed to assess the current and future role of artificial intelligence in assessing the key outcomes in inflammatory bowel disease patients: endoscopic activity, mucosal healing, response to treatment, and neoplasia surveillance.

Predictive models for endoscopic disease activity in patients with ulcerative colitis: Practical machine learning-based modeling and interpretation

Background

Endoscopic disease activity monitoring is important for the long-term management of patients with ulcerative colitis (UC), there is currently no widely accepted non-invasive method that can effectively predict endoscopic disease activity. We aimed to develop and validate machine learning (ML) models for predicting it, which are desired to reduce the frequency of endoscopic examinations and related costs.

Methods

The patients with a diagnosis of UC in two hospitals from January 2016 to January 2021 were enrolled in this study. Thirty nine clinical and laboratory variables were collected. All patients were divided into four groups based on MES or UCEIS scores. Logistic regression (LR) and four ML algorithms were applied to construct the prediction models. The performance of models was evaluated in terms of accuracy, sensitivity, precision, F1 score, and area under the receiver-operating characteristic curve (AUC). Then Shapley additive explanations (SHAP) was applied to determine the importance of the selected variables and interpret the ML models.

Results

A total of 420 patients were entered into the study. Twenty four variables showed statistical differences among the groups. After synthetic minority oversampling technique (SMOTE) oversampling and RFE variables selection, the random forests (RF) model with 23 variables in MES and the extreme gradient boosting (XGBoost) model with 21 variables in USEIS, had the greatest discriminatory ability (AUC = 0.8192 in MES and 0.8006 in UCEIS in the test set). The results obtained from SHAP showed that albumin, rectal bleeding, and CRP/ALB contributed the most to the overall model. In addition, the above three variables had a more balanced contribution to each classification under the MES than the UCEIS according to the SHAP values.

Conclusion

This proof-of-concept study demonstrated that the ML model could serve as an effective non-invasive approach to predicting endoscopic disease activity for patients with UC. RF and XGBoost, which were first introduced into data-based endoscopic disease activity prediction, are suitable for the present prediction modeling.

Application of Metal-Based Nanozymes in Inflammatory Disease: A Review

Reactive oxygen species (ROS) are metabolites of normal cells in organisms, and normal levels of ROS in cells are essential for maintaining cell signaling and other intracellular functions. However, excessive inflammation and ischemia-reperfusion can cause an imbalance of tissue redox balance, and oxidative stress occurs in a tissue, resulting in a large amount of ROS, causing direct tissue damage. The production of many diseases is associated with excess ROS, such as stroke, sepsis, Alzheimer’s disease, and Parkinson’s disease. With the rapid development of nanomedicine, nanomaterials have been widely used to effectively treat various inflammatory diseases due to their superior physical and chemical properties. In this review, we summarize the application of some representative metal-based nanozymes in inflammatory diseases. In addition, we discuss the application of various novel nanomaterials for different therapies and the prospects of using nanoparticles (NPs) as biomedical materials.

Machine learning models and over-fitting considerations

Machine learning models may outperform traditional statistical regression algorithms for predicting clinical outcomes. Proper validation of building such models and tuning their underlying algorithms is necessary to avoid over-fitting and poor generalizability, which smaller datasets can be more prone to. In an effort to educate readers interested in artificial intelligence and model-building based on machine-learning algorithms, we outline important details on cross-validation techniques that can enhance the performance and generalizability of such models.

Classification of the Confocal Microscopy Images of Colorectal Tumor and Inflammatory Colitis Mucosa Tissue Using Deep Learning

Confocal microscopy image analysis is a useful method for neoplasm diagnosis. Many ambiguous cases are difficult to distinguish with the naked eye, thus leading to high inter-observer variability and significant time investments for learning this method. We aimed to develop a deep learning-based neoplasm classification model that classifies confocal microscopy images of 10× magnified colon tissues into three classes: neoplasm, inflammation, and normal tissue. ResNet50 with data augmentation and transfer learning approaches was used to efficiently train the model with limited training data. A class activation map was generated by using global average pooling to confirm which areas had a major effect on the classification. The proposed method achieved an accuracy of 81%, which was 14.05% more accurate than three machine learning-based methods and 22.6% better than the predictions made by four endoscopists. ResNet50 with data augmentation and transfer learning can be utilized to effectively identify neoplasm, inflammation, and normal tissue in confocal microscopy images. The proposed method outperformed three machine learning-based methods and identified the area that had a major influence on the results. Inter-observer variability and the time required for learning can be reduced if the proposed model is used with confocal microscopy image analysis for diagnosis.