Autoimmune diseases have long been recognized for their intricate nature and elusive mechanisms, presenting significant challenges in both diagnosis and treatment. The advent of artificial intelligence technology has opened up new possibilities for understanding, diagnosing, predicting, and managing autoimmune disorders. This study aims to explore the current state and emerging trends in the field through bibliometric analysis, providing guidance for future research directions.

The study employed the Web of Science Core Collection database for data acquisition and performed bibliometric analysis using CiteSpace, HistCite Pro, and VOSviewer.

Over the past two decades, 1,695 publications emerged in this research field, including 1,409 research articles and 286 reviews. This investigation unveils the global development landscape predominantly led by the United States and China. The research identifies key institutions, such as Brigham & Women's Hospital, influential journals like the Annals of the Rheumatic Diseases, distinguished authors including Katherine P. Liao, and pivotal articles. It visually maps out the research clusters' evolutionary path over time and explores their applications in patient identification, risk factors, prognosis assessment, diagnosis, classification of disease subtypes, monitoring and decision support, and drug discovery.

AI is increasingly recognized for its potential in the field of autoimmune diseases, yet it continues to face numerous challenges, including insufficient model validation and difficulties in data integration and computational power. Significant advancements have been demanded to enhance diagnostic precision, improve treatment methodologies, and establish robust frameworks for data protection, thereby facilitating more effective management of these complex conditions.

Artificial intelligence (AI) is being increasingly studied and implemented in gastroenterology. In inflammatory bowel disease (IBD), numerous AI models are being developed to assist with IBD diagnosis, standardization of endoscopic and radiologic disease activity, and predicting outcomes. Further prospective, multicenter studies representing diverse populations and novel applications are needed prior to routine implementation in clinical practice and expected improved outcomes for clinicians and patients.

Inflammatory bowel disease (IBD) in the pediatric population presents distinct characteristics compared to adult cases. Pathology plays a critical role in its diagnosis, and this review underscores key considerations in the pathologic evaluation of pediatric IBD. Recognizing inflammatory patterns in the upper gastrointestinal tract can improve disease classification and aid in diagnosing IBD in certain scenarios, such as isolated upper gastrointestinal or small bowel involvement. Additionally, familiarity with distinctive subtypes, including IBD associated with primary sclerosing cholangitis and monogenic forms of IBD, supports early comorbidity detection, enhances patient management, and improves prognostication.

Chronic diseases such as heart disease, cancer, and diabetes are leading drivers of mortality worldwide, underscoring the need for improved efforts around early detection and prediction. The pathophysiology and management of chronic diseases have benefitted from emerging fields in molecular biology like genomics, transcriptomics, proteomics, glycomics, and lipidomics. The complex biomarker and mechanistic data from these "omics" studies present analytical and interpretive challenges, especially for traditional statistical methods. Machine learning (ML) techniques offer considerable promise in unlocking new pathways for data-driven chronic disease risk assessment and prognosis. This review provides a comprehensive overview of state-of-the-art applications of ML algorithms for chronic disease detection and prediction across datasets, including medical imaging, genomics, wearables, and electronic health records. Specifically, we review and synthesize key studies leveraging major ML approaches ranging from traditional techniques such as logistic regression and random forests to modern deep learning neural network architectures. We consolidate existing literature to date around ML for chronic disease prediction to synthesize major trends and trajectories that may inform both future research and clinical translation efforts in this growing field. While highlighting the critical innovations and successes emerging in this space, we identify the key challenges and limitations that remain to be addressed. Finally, we discuss pathways forward toward scalable, equitable, and clinically implementable ML solutions for transforming chronic disease screening and prevention.

Given the paucity of long-term longitudinal data for inflammatory bowel disease type unclassified (IBDU), we aimed to clarify IBDU disease course and reclassification rate by presenting nationwide data with up to 20 years of follow-up.

We analyzed a prospectively identified 11-year cohort of pediatric patients diagnosed with IBDU between January 1, 2003 and December 31, 2013 at all Scottish pediatric IBD centers and followed up into adult services until December 31, 2022. Data were obtained from electronic medical records at fixed timepoints (5 and 10 years post-diagnosis) and at the final follow-up.

Overall, 102 patients were included in the analysis (57/102 [56%] male, median [interquartile range {IQR}] age at diagnosis: 11.5 [9.1-13.2] years) with a median (IQR) follow-up length of 10.5 (8.6-14.0) years. A change of diagnosis was made in 61 of 102 patients (60%); of these, 30 patients (29%) were reclassified to Crohn's disease (CD) and 31 patients (30%) to ulcerative colitis (UC). Patients who remained with IBDU had higher 1- to 5-year remission rates (IBDU 30/39 [77%] vs reclassified 16/57 [28%], P < .05), with lower rates of moderate-to-severe disease (IBDU 3/39 [8%] vs reclassified 31/57 [54%], P < .05) and less need for biologics across all timepoints (IBDU vs reclassified: first timepoint 1/39 [3%] vs 17/57 [30%], second timepoint 1/33 [3%] vs 26/56 [46%], third timepoint 0/18 [0%] vs 16/33 [49%]; all P < .05). Higher rates of surgical resections were observed in reclassified patients (reclassified 11/61 [18%] vs IBDU 1/41 [2%], P = .02).

In our nationwide pediatric IBDU cohort, 60% of patients were reclassified to either UC or CD over 10.5 years of median follow-up; those who remained with IBDU had a milder disease course.

Inflammatory bowel disease (IBD) includes Crohn's Disease (CD) and Ulcerative Colitis (UC). Correct diagnosis requires the identification of precise morphological features such basal plasmacytosis. However, histopathological interpretation can be challenging, and it is subject to high variability.

The IBD-Artificial Intelligence (AI) project aims at the development of an AI-based evaluation system to support the diagnosis of IBD, semi-automatically quantifying basal plasmacytosis.

A deep learning model was trained to detect and quantify plasma cells on a public dataset of 4981 annotated images. The model was then tested on an external validation cohort of 356 intestinal biopsies of CD, UC and healthy controls. AI diagnostic performance was calculated compared to human gold standard.

The system correctly found that CD and UC samples had a greater prevalence of basal plasma cells with mean number of PCs within ROIs of 38.22 (95 % CI: 31.73, 49.04) for CD, 55.16 (46.57, 65.93) for UC, and 17.25 (CI: 12.17, 27.05) for controls. Overall, OR=4.968 (CI: 1.835, 14.638) was found for IBD compared to normal mucosa (CD: +59 %; UC: +129 %). Additionally, as expected, UC samples were found to have more plasma cells in colon than CD cases.

Our model accurately replicated human assessment of basal plasmacytosis, underscoring the value of AI models as a potential aid IBD diagnosis.

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.

Since data on predictors of complicated Crohn's disease (CD) from unselected populations are scarce, we aimed to utilize a large nationwide cohort, the epi-IIRN, to explore predictors of disease course in children and adults with CD.

Data of patients with CD were retrieved from Israel's 4 health maintenance organizations, whose records cover 98% of the population (2005-2020). Time-to-event modeled a complicated disease course, defined as CD-related surgery, steroid-dependency, or the need for >1 class of biologics. Hierarchical clustering categorized disease severity at diagnosis based on available laboratory results.

A total of 16 659 patients (2999 [18%] pediatric-onset) with 121 695 person-years of follow-up were included; 3761 (23%) had a complicated course (750 [4.5%] switched to a second biologic class, 1547 [9.3%] steroid-dependency, 1463 [8.8%] CD-related surgery). Complicated disease was more common in pediatric- than adult-onset disease (26% vs 22%, odds ratio, 1.3; 95% confidence interval [CI], 1.2-1.4). In a Cox multivariate model, complicated disease was predicted by induction therapy with biologics (hazard ratio [HR], 2.1; 95% CI, 1.2-3.6) and severity of laboratory tests at diagnosis (HR, 1.7; 95% CI, 1.2-2.2), while high socioeconomic status was protective (HR, 0.94; 95% CI, 0.91-0.96). In children, laboratory tests predicted disease course (HR, 1.8; 95% CI, 1.2-2.5), as well as malnutrition (median BMI Z score -0.41; 95% CI, -1.42 to 0.43 in complicated disease vs -0.24; 95% CI, -1.23 to 0.63] in favorable disease; P < .001).

In this nationwide cohort, CD course was complicated in one-fourth of patients, predicted by laboratory tests, type of induction therapy, socioeconomic status, in addition to malnutrition in children.

Inflammatory bowel diseases (IBD) are classified into two entities, namely Crohn's disease (CD) and ulcerative colitis (UC), which differ in disease trajectories, genetics, epidemiological, clinical, endoscopic, and histopathological aspects. As no single golden standard modality for diagnosing IBD exists, the differential diagnosis among UC, CD, and non-IBD involves a multidisciplinary approach, considering professional groups that include gastroenterologists, endoscopists, radiologists, and pathologists. In this context, histological examination of endoscopic or surgical specimens plays a fundamental role. Nevertheless, in differentiating IBD from non-IBD colitis, the histopathological evaluation of the morphological lesions is limited by sampling and subjective human judgment, leading to potential diagnostic discrepancies. To overcome these limitations, artificial intelligence (AI) techniques are emerging to enable automated analysis of medical images with advantages in accuracy, precision, and speed of investigation, increasing interest in the histological analysis of gastrointestinal inflammation. This review aims to provide an overview of the most recent knowledge and advances in AI methods, summarizing its applications in the histopathological analysis of endoscopic biopsies from IBD patients, and discussing its strengths and limitations in daily clinical practice.

Autoimmune disorders affect 4.5% to 9.4% of children, significantly reducing their quality of life. The diagnosis and prognosis of autoimmune diseases are uncertain because of the variety of onset and development. Machine learning can identify clinically relevant patterns from vast amounts of data. Hence, its introduction has been beneficial in the diagnosis and management of patients.

This narrative review was conducted through searching various electronic databases, including PubMed, Scopus, and Web of Science. This study thoroughly explores the current knowledge and identifies the remaining gaps in the applications of machine learning specifically in the context of pediatric autoimmune and related diseases.

Machine learning algorithms have the potential to completely change how pediatric autoimmune disorders are identified, treated, and managed. Machine learning can assist physicians in making more precise and fast judgments, identifying new biomarkers and therapeutic targets, and personalizing treatment strategies for each patient by utilizing massive datasets and powerful analytics.

The high complexity of systemic autoimmune diseases (SADs) has hindered precise management. This study aims to investigate heterogeneity in SADs.

We applied a joint cluster analysis, which jointed multiple correspondence analysis and k-means, to immunomarkers and measured the heterogeneity of clusters by examining differences in immunomarkers and clinical manifestations. The electronic health records of patients who received an antinuclear antibody test and were diagnosed with SADs, namely systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and Sjögren's syndrome (SS), were retrieved between 2001 and 2016 from hospitals in Taiwan.

With distinctive patterns of immunomarkers, a total of 11,923 patients with the three SADs were grouped into six clusters. None of the clusters was composed only of a single SAD, and these clusters demonstrated considerable differences in clinical manifestation. Both patients with SLE and SS had a more dispersed distribution in the six clusters. Among patients with SLE, the occurrence of renal compromise was higher in Clusters 3 and 6 (52% and 51%) than in the other clusters (p < 0.001). Cluster 3 also had a high proportion of patients with discoid lupus (60%) than did Cluster 6 (39%; p < 0.001). Patients with SS in Cluster 3 were the most distinctive because of the high occurrence of immunity disorders (63%) and other and unspecified benign neoplasm (58%) with statistical significance compared with the other clusters (all p < 0.05).

The immunomarker-driven clustering method could recognise more clinically relevant subgroups of the SADs and would provide a more precise diagnosis basis.

Interest in artificial intelligence (AI) applications for ulcerative colitis (UC) has grown tremendously in recent years. In the past 5 years, there have been over 80 studies focused on machine learning (ML) tools to address a wide range of clinical problems in UC, including diagnosis, prognosis, identification of new UC biomarkers, monitoring of disease activity, and prediction of complications. AI classifiers such as random forest, support vector machines, neural networks, and logistic regression models have been used to model UC clinical outcomes using molecular (transcriptomic) and clinical (electronic health record and laboratory) datasets with relatively high performance (accuracy, sensitivity, and specificity). Application of ML algorithms such as computer vision, guided image filtering, and convolutional neural networks have also been utilized to analyze large and high-dimensional imaging datasets such as endoscopic, histologic, and radiological images for UC diagnosis and prediction of complications (post-surgical complications, colorectal cancer). Incorporation of these ML tools to guide and optimize UC clinical practice is promising but will require large, high-quality validation studies that overcome the risk of bias as well as consider cost-effectiveness compared to standard of care.

Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.

Alcoholic-associated liver disease (ALD) and metabolic dysfunction-associated steatotic liver disease (MASLD) show a high prevalence rate worldwide. As gut microbiota represents current state of ALD and MASLD via gut-liver axis, typical characteristics of gut microbiota can be used as a potential diagnostic marker in ALD and MASLD. Machine learning (ML) algorithms improve diagnostic performance in various diseases. Using gut microbiota-based ML algorithms, we evaluated the diagnostic index for ALD and MASLD. Fecal 16S rRNA sequencing data of 263 ALD (control, elevated liver enzyme [ELE], cirrhosis, and hepatocellular carcinoma [HCC]) and 201 MASLD (control and ELE) subjects were collected. For external validation, 126 ALD and 84 MASLD subjects were recruited. Four supervised ML algorithms (support vector machine, random forest, multilevel perceptron, and convolutional neural network) were used for classification with 20, 40, 60, and 80 features, in which three nonsupervised ML algorithms (independent component analysis, principal component analysis, linear discriminant analysis, and random projection) were used for feature reduction. A total of 52 combinations of ML algorithms for each pair of subgroups were performed with 60 hyperparameter variations and Stratified ShuffleSplit tenfold cross validation. The ML models of the convolutional neural network combined with principal component analysis achieved areas under the receiver operating characteristic curve (AUCs) > 0.90. In ALD, the diagnostic AUC values of the ML strategy (vs. control) were 0.94, 0.97, and 0.96 for ELE, cirrhosis, and liver cancer, respectively. The AUC value (vs. control) for MASLD (ELE) was 0.93. In the external validation, the AUC values of ALD and MASLD (vs control) were > 0.90 and 0.88, respectively. The gut microbiota-based ML strategy can be used for the diagnosis of ALD and MASLD.ClinicalTrials.gov NCT04339725.

Diagnosing inflammatory bowel disease (IBD) can often be challenging, and differentiating between Crohn's disease and ulcerative colitis can be particularly difficult. Diagnostic procedures for IBD include laboratory tests, endoscopy, pathological tests, and imaging tests. Serological and stool tests can be easily performed in an outpatient setting and provide critical diagnostic clues. Although endoscopy is an invasive procedure, it offers essential diagnostic information and allows for tissue biopsy and therapeutic procedures. Video capsule endoscopy and device-assisted enteroscopy are endoscopic procedures used to evaluate the small bowel. In addition to endoscopy, magnetic resonance imaging, computed tomography, and ultrasound (US) are valuable tools for small bowel assessment. Among these, US is noninvasive and easily utilized, making its use highly practical in daily clinical practice. Endoscopic biopsy aids in the diagnosis of IBD and is crucial for assessing the histological activity of the disease, facilitating a thorough evaluation of disease remission, and aiding in the development of treatment strategies. Recent advances in artificial intelligence hold promise for enhancing various aspects of IBD management, including diagnosis, monitoring, and precision medicine. This review compiles current procedures and promising future tools for the diagnosis of IBD, providing comprehensive insights.

This review aims to present a comprehensive overview of the current landscape of artificial intelligence (AI) applications in the analysis of tubular gastrointestinal biopsies. These publications cover a spectrum of conditions, ranging from inflammatory ailments to malignancies. Moving beyond the conventional diagnosis based on hematoxylin and eosin-stained whole-slide images, the review explores additional implications of AI, including its involvement in interpreting immunohistochemical results, molecular subtyping, and the identification of cellular spatial biomarkers. Furthermore, the review examines how AI can contribute to enhancing the quality and control of diagnostic processes, introducing new workflow options, and addressing the limitations and caveats associated with current AI platforms in this context.

Identifying the gut microbiota associated with host immunity in the AIDS stage.

We performed a cross-sectional study.

We recruited people with HIV (PWH) in the AIDS or non-AIDS stage and evaluated their gut microbiota and metabolites by using 16S ribosomal RNA (rRNA) sequencing and liquid chromatography-mass spectrometry (LC-MS). Machine learning models were used to analyze the correlations between key bacteria and CD4 + T cell count, CD4 + T cell activation, bacterial translocation, gut metabolites, and KEGG functional pathways.

We recruited 114 PWH in the AIDS stage and 203 PWH in the non-AIDS stage. The α-diversity of gut microbiota was downregulated in the AIDS stage ( P  < 0.05). Several machine learning models could be used to identify key gut microbiota associated with AIDS, including the logistic regression model with area under the curve (AUC), sensitivity, specificity, and Brier scores of 0.854, 0.813, 0.813, and 0.160, respectively. The decreased key bacteria ASV1 ( Bacteroides sp.), ASV8 ( Fusobacterium sp.), ASV30 ( Roseburia sp.), ASV37 ( Bacteroides sp.), and ASV41 ( Lactobacillus sp.) in the AIDS stage were positively correlated with the CD4 + T cell count, the EndoCAb IgM level, 4-hydroxyphenylpyruvic acid abundance, and the predicted cell growth pathway, and negatively correlated with the CD3 + CD4 + CD38 + HLA-DR + T cell count and the sCD14 level.

Machine learning has the potential to recognize key gut microbiota related to AIDS. The key five bacteria in the AIDS stage and their metabolites might be related to CD4 + T cell reduction and immune activation.

Children experiencing refractory constipation, resistant to conventional pharmacological approaches, develop severe symptoms that persist into adulthood, leading to a substantial decline in their quality of life. Early identification of refractory constipation may improve their management. We aimed to describe the characteristics of colonic anatomy in children with different types of constipation and develop a supervised machine-learning model for early identification.

In this retrospective study, patient characteristics and standardized colon size (SCS) ratios by barium enema (BE) were studied in patients with functional constipation (n=77), refractory constipation (n=63), and non-constipation (n=65). Statistical analyses were performed and a supervised machine learning (ML) model was developed based on these data for the classification of the three groups.

Significant differences in rectum diameter, sigmoid diameter, descending diameter, transverse diameter, and rectosigmoid length were found in the three groups. A linear support vector machine was utilized to build the early detection model. Using five features (SCS ratios of sigmoid colon, descending colon, transverse colon, rectum, and rectosigmoid), the model demonstrated an accuracy of 81% [95% confidence interval (CI): 79.17% to 83.19%].

The application of using a supervised ML strategy obtained an accuracy of 81% in distinguishing children with refractory constipation. The combination of BE and ML model can be used for practical implications, which is important for guiding management in children with refractory constipation.

In this study, we explored whether the Korean version of Singer Loomis Type Deployment Inventory II (K-SLTDI) captures the opposing tendencies of Jung's theory of psychological type. The types are Extroverted Sensing, Extroverted Intuition, Extroverted Feeling, Extroverted Thinking, Introverted Sensing, Introverted Intuition, Introverted Feeling, and Introverted Thinking. A nationwide online survey was conducted in South Korea. We performed multidimensional scaling and classification analyses based on 521 Korean adult profiles with eight psychological types to test the bipolarity assumption. The results showed that the Procrustes-rotated four-dimensional space successfully represented four types of opposing tendencies. Moreover, the bipolarity assumption in the four dimensions of Jungian typology was tested and compared between lower and higher psychological distress populations via cluster analysis. Lastly, we explored patterns of responses in lower and higher psychological distress populations using intersubject correlation. Both similarity analyses and classification results consistently support the theoretical considerations on the conceptualization of Jung's type in independent order that the types could be derived without bipolar assumption as Singer and Loomis expected in their Type Development Inventory. Limitations in our study include the sample being randomly selected internet users during the COVID-19 pandemic, despite excellence in the use of the internet in the general Korean population.

The field of histopathological image analysis has evolved significantly with the advent of digital pathology, leading to the development of automated models capable of classifying tissues and structures within diverse pathological images. Artificial intelligence algorithms, such as convolutional neural networks, have shown remarkable capabilities in pathology image analysis tasks, including tumor identification, metastasis detection, and patient prognosis assessment. However, traditional manual analysis methods have generally shown low accuracy in diagnosing colorectal cancer using histopathological images. This study investigates the use of AI in image classification and image analytics using histopathological images using the histogram of oriented gradients method. The study develops an AI-based architecture for image classification using histopathological images, aiming to achieve high performance with less complexity through specific parameters and layers. In this study, we investigate the complicated state of histopathological image classification, explicitly focusing on categorizing nine distinct tissue types. Our research used open-source multi-centered image datasets that included records of 100.000 non-overlapping images from 86 patients for training and 7180 non-overlapping images from 50 patients for testing. The study compares two distinct approaches, training artificial intelligence-based algorithms and manual machine learning models, to automate tissue classification. This research comprises two primary classification tasks: binary classification, distinguishing between normal and tumor tissues, and multi-classification, encompassing nine tissue types, including adipose, background, debris, stroma, lymphocytes, mucus, smooth muscle, normal colon mucosa, and tumor. Our findings show that artificial intelligence-based systems can achieve 0.91 and 0.97 accuracy in binary and multi-class classifications. In comparison, the histogram of directed gradient features and the Random Forest classifier achieved accuracy rates of 0.75 and 0.44 in binary and multi-class classifications, respectively. Our artificial intelligence-based methods are generalizable, allowing them to be integrated into histopathology diagnostics procedures and improve diagnostic accuracy and efficiency. The CNN model outperforms existing machine learning techniques, demonstrating its potential to improve the precision and effectiveness of histopathology image analysis. This research emphasizes the importance of maintaining data consistency and applying normalization methods during the data preparation stage for analysis. It particularly highlights the potential of artificial intelligence to assess histopathological images.

Ulcerative colitis (UC) is a challenging form of inflammatory bowel disease, and its etiology is intricately linked to disturbances in the gut microbiome. To identify the potential alleviators of UC, we employed an integrative analysis combining microbial community modeling with advanced machine learning techniques. Using metagenomics data sourced from the Integrated Human Microbiome Project, we constructed individualized microbiome community models for each participant. Our analysis highlighted a significant decline in both α and β-diversity of strain-level microbial populations in UC subjects compared to controls. Distinct differences were also observed in the predicted fecal metabolite profiles and strain-to-metabolite contributions between the two groups. Using tree-based machine learning models, we successfully identified specific microbial strains and their associated metabolites as potential alleviators of UC. Notably, our experimental validation using a dextran sulfate sodium-induced UC mouse model demonstrated that the administration of Parabacteroides merdae ATCC 43,184 and N-acetyl-D-mannosamine provided notable relief from colitis symptoms. In summary, our study underscores the potential of an integrative approach to identify novel therapeutic avenues for UC, paving the way for future targeted interventions.

Inflammatory bowel disease (IBD) is a multifactorial chronic inflammatory disease resulting from dysregulation of the mucosal immune response and gut microbiota. Crohn's disease (CD) and ulcerative colitis (UC) are difficult to distinguish, and differential diagnosis is essential for establishing a long-term treatment plan for patients. Furthermore, the abundance of mucosal bacteria is associated with the severity of the disease. This study aimed to differentiate and diagnose these two diseases using the microbiome and identify specific biomarkers associated with disease activity.

Differences in the abundance and composition of the microbiome between IBD patients and healthy controls (HC) were observed. Compared to HC, the diversity of the gut microbiome in patients with IBD decreased; the diversity of the gut microbiome in patients with CD was significantly lower. Sixty-eight microbiota members (28 for CD and 40 for UC) associated with these diseases were identified. Additionally, as the disease progressed through different stages, the diversity of the bacteria decreased. The abundances of Alistipes shahii and Pseudodesulfovibrio aespoeensis were negatively correlated with the severity of CD, whereas the abundance of Polynucleobacter wianus was positively correlated. The severity of UC was negatively correlated with the abundance of A. shahii, Porphyromonas asaccharolytica and Akkermansia muciniphilla, while it was positively correlated with the abundance of Pantoea candidatus pantoea carbekii. A regularized logistic regression model was used for the differential diagnosis of the two diseases. The area under the curve (AUC) was used to examine the performance of the model. The model discriminated UC and CD at an AUC of 0.873 (train set), 0.778 (test set), and 0.633 (validation set) and an area under the precision-recall curve (PRAUC) of 0.888 (train set), 0.806 (test set), and 0.474 (validation set).

Based on fecal whole-metagenome shotgun (WMS) sequencing, CD and UC were diagnosed using a machine-learning predictive model. Microbiome biomarkers associated with disease activity (UC and CD) are also proposed.

Inflammatory bowel disease [IBD] is a chronic inflammatory disorder with two main subtypes: Crohn's disease [CD] and ulcerative colitis [UC]. Prompt subtype diagnosis enables the correct treatment to be administered. Using genomic data, we aimed to assess machine learning [ML] to classify patients according to IBD subtype.

Whole exome sequencing [WES] from paediatric/adult IBD patients was processed using an in-house bioinformatics pipeline. These data were condensed into the per-gene, per-individual genomic burden score, GenePy. Data were split into training and testing datasets [80/20]. Feature selection with a linear support vector classifier, and hyperparameter tuning with Bayesian Optimisation, were performed [training data]. The supervised ML method random forest was utilised to classify patients as CD or UC, using three panels: 1] all available genes; 2] autoimmune genes; 3] 'IBD' genes. ML results were assessed using area under the receiver operating characteristics curve [AUROC], sensitivity, and specificity on the testing dataset.

A total of 906 patients were included in analysis [600 CD, 306 UC]. Training data included 488 patients, balanced according to the minority class of UC. The autoimmune gene panel generated the best performing ML model [AUROC = 0.68], outperforming an IBD gene panel [AUROC = 0.61]. NOD2 was the top gene for discriminating CD and UC, regardless of the gene panel used. Lack of variation in genes with high GenePy scores in CD patients was the best classifier of a diagnosis of UC.

We demonstrate promising classification of patients by subtype using random forest and WES data. Focusing on specific subgroups of patients, with larger datasets, may result in better classification.

Children's health is one of the most significant fields in medicine. Most diseases that result in children's death or long-term morbidity are caused by preventable and treatable etiologies, and they appear in the child at the early stages as mild symptoms. This research aims to develop a machine learning (ML) framework to detect the severity of disease in children. The proposed framework helps in discriminating children's urgent/severe conditions and notifying parents whether a child needs to visit the emergency room immediately or not. The model considers several variables to detect the severity of cases, which are the symptoms, risk factors (e.g., age), and the child's medical history. The framework is implemented by using nine ML methods. The results achieved show the high performance of the proposed framework in identifying serious pediatric diseases, where decision tree and random forest outperformed the other methods with an accuracy rate of 94%. This shows the reliability of the proposed framework to be used as a pediatric decision-making system for detecting serious pediatric illnesses. The results are promising when compared to recent state-of-the-art studies. The main contribution of this research is to propose a framework that is viable for use by parents when their child suffers from any commonly developed symptoms.

Inflammatory bowel disease (IBD) is an illness with increasing prevalence, particularly in emerging countries, which can have a substantial impact on the quality of life of the patient. The illness is rather heterogeneous with different evolution among patients. A machine learning approach is followed in this paper to identify potential genes that are related to IBD. This is done by following a Monte Carlo simulation approach. In total, 23 different machine learning techniques were tested (in addition to a base level obtained using artificial neural networks). The best model identified 74 genes selected by the algorithm as being potentially involved in IBD. IBD seems to be a polygenic illness, in which environmental factors might play an important role. Following a machine learning approach, it was possible to obtain a classification accuracy of 84.2% differentiating between patients with IBD and control cases in a large cohort of 2490 total cases. The sensitivity and specificity of the model were 82.6% and 84.4%, respectively. It was also possible to distinguish between the two main types of IBD: (1) Crohn's disease and (2) ulcerative colitis.

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.

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.

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.

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.

A small but persistent proportion of individuals do not gain the expected benefit from cochlear implants(CI). A step-change in the understanding of factors affecting outcomes could come through data science. This study evaluates clinical data capture to assess the quality and utility of CI user's health records for data science, by assessing the recording of otitis media. Otitis media was selected as it is associated with the development of sensorineural hearing loss and may affect cochlear implant outcomes.

A retrospective service improvement project evaluating the medical records of 594 people with a CI under the care of the University of Southampton Auditory Implant Service between 2014 and 2020.

The clinical records are suitable for data science research. Of the cohort studied 20% of Adults and more than 40% of the paediatric cases have a history of middle ear inflammation.

Data science has potential to improve cochlear implant outcomes and improve understanding of the mechanisms underlying poor performance, through retrospective secondary analysis of real-world data.

Implant centres and the British Cochlear Implant Group National Hearing Implant Registry are urged to consider the importance of consistently and accurate recording of patient data over time for each CI user. Data where links to hearing loss have been identified, such as middle ear inflammation, may be particularly valuable in future analyses and to inform clinical trials.

Epileptic seizures are associated with a higher incidence of Developmental Disabilities and Cerebral Palsy. Early evaluation and management of epilepsy is strongly recommended. We propose and discuss an application to predict epilespy (PredictMed-Epilepsy) and seizures via a deep-learning module (PredictMed-Seizures) encompassed within a multi-agent based healthcare system (PredictMed-MHS); this system is meant, in perspective, to be integrated into a clinical decision support system (PredictMed-CDSS). PredictMed-Epilespy, in particular, aims to identify factors associated with epilepsy in children with Developmental Disabilities and Cerebral Palsy by using a prediction-learning model named PredictMed. PredictMed-epilespy methods: We performed a longitudinal, multicenter, double-blinded, descriptive study of one hundred and two children with Developmental Disabilities and Cerebral Palsy (58 males, 44 females; 65 inpatients, 37 outpatients; 72 had epilepsy - 22 of intractable epilepsy, age: 16.6±1.2y, range: 12-18y). Data from 2005 to 2021 on Cerebral Palsy etiology, diagnosis, type of epilepsy and spasticity, clinical history, communication abilities, behaviors, intellectual disability, motor skills, and eating and drinking abilities were collected. The machine-learning model PredictMed was exploited to identify factors associated with epilepsy. The guidelines of the "Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis" Statement (TRIPOD) were followed. PredictMed-epilepsy results: Cerebral Palsy etiology [(prenatal > perinatal > postnatal causes) p=0.036], scoliosis (p=0.048), communication (p=0.018) and feeding disorders (p=0.002), poor motor function (p<0.001), intellectual disabilities (p=0.007), and type of spasticity [(quadriplegia/triplegia > diplegia > hemiplegia), p=0.002)] were associated with having epilepsy. The prediction model scored an average of 82% of accuracy, sensitivity, and specificity. Thus, PredictMed defined the computational phenotype of children with Developmental Disabilities/Cerebral Palsy at risk of epilepsy. Novel contribution of the work: We have been developing and we have prototypically implemented a Multi-Agent Systems (MAS) that encapsulates the PredictMed-Epilepsy module. More specifically, we have implemented the Patient Observing MAS (PoMAS), which, as a novelty w.r.t. the existing literature, includes a complex event processing module that provides real-time detention of short- and long-term events related to the patient's condition.

Complexity of inflammatory bowel diseases (IBD) lies on their management and their biology. Clinics, blood and fecal samples tests, endoscopy and histology are the main tools guiding IBD treatment, but they generate a large amount of data, difficult to analyze by clinicians. Because of its capacity to analyze large number of data, artificial intelligence is currently generating enthusiasm in medicine, and this technology could be used to improve IBD management. In this review, after a short summary on IBD management and artificial intelligence, we will report pragmatic examples of artificial intelligence utilisation in IBD. Lastly, we will discuss the limitations of this technology.

Although Mycobacterium avium subsp. paratuberculosis (MAP) has threatened public health and the livestock industry, the current diagnostic tools (e.g., fecal PCR and enzyme-linked immunosorbent assay [ELISA]) for MAP infection have some limitations, such as inconsistent results due to intermittent bacterial shedding or low sensitivity during the early stage of infection. Therefore, this study aimed to develop a novel biomarker focusing on elucidating the gut microbial signature of MAP-positive ruminants, since the clinical signs of MAP infection are closely related to dysbiosis. 16S rRNA-based gut microbial community analysis revealed both a decrease in microbial diversity and the emergence of several distinct taxa following MAP infection. To determine the discriminant taxa diagnostic of MAP infection, machine learning-based feature selection and predictive model construction were applied to taxon abundance data or their transformed derivatives. The selected taxa, such as Clostridioides (formerly Clostridium) difficile, were used to build models using a support vector machine, linear support vector classification, k-nearest neighbor, and random forest with 10-fold cross-validation. The receiver operating characteristic-area under the curve (ROC-AUC) analysis of the models revealed their high accuracy, up to approximately 96%. Collectively, taxonomic signatures of cattle gut microbiotas according to MAP infection status could be identified by feature selection tools and applied to establish a predictive model for the infection state. IMPORTANCE Due to the limitations, such as intermittent bacterial shedding or poor sensitivity, of the current diagnostic tools for Johne's disease, novel biomarkers are urgently needed to aid control of the disease. Here, we explored the fecal microbiota of Johne's disease-affected cattle and tried to discover distinct microbial characteristics which have the potential to be novel noninvasive biomarkers. Through 16S rRNA sequencing and machine learning approaches, a dozen taxa were selected as taxonomic signatures to discriminate the disease state. In addition, when constructing predictive models using relative abundance data of the corresponding taxa, the models showed high accuracy for classification, even including animals with subclinical infection. Thus, our study suggested novel noninvasive microbiological biomarkers that are robustly expressed regardless of subclinical infection and the applicability of machine learning for diagnosis of Johne's disease.

Polygenic risk scores (PRSs) have been among the leading advances in biomedicine in recent years. As a proxy of genetic liability, PRSs are utilised across multiple fields and applications. While numerous statistical and machine learning methods have been developed to optimise their predictive accuracy, these typically distil genetic liability to a single number based on aggregation of an individual's genome-wide risk alleles. This results in a key loss of information about an individual's genetic profile, which could be critical given the functional sub-structure of the genome and the heterogeneity of complex disease. In this manuscript, we introduce a 'pathway polygenic' paradigm of disease risk, in which multiple genetic liabilities underlie complex diseases, rather than a single genome-wide liability. We describe a method and accompanying software, PRSet, for computing and analysing pathway-based PRSs, in which polygenic scores are calculated across genomic pathways for each individual. We evaluate the potential of pathway PRSs in two distinct ways, creating two major sections: (1) In the first section, we benchmark PRSet as a pathway enrichment tool, evaluating its capacity to capture GWAS signal in pathways. We find that for target sample sizes of >10,000 individuals, pathway PRSs have similar power for evaluating pathway enrichment as leading methods MAGMA and LD score regression, with the distinct advantage of providing individual-level estimates of genetic liability for each pathway -opening up a range of pathway-based PRS applications, (2) In the second section, we evaluate the performance of pathway PRSs for disease stratification. We show that using a supervised disease stratification approach, pathway PRSs (computed by PRSet) outperform two standard genome-wide PRSs (computed by C+T and lassosum) for classifying disease subtypes in 20 of 21 scenarios tested. As the definition and functional annotation of pathways becomes increasingly refined, we expect pathway PRSs to offer key insights into the heterogeneity of complex disease and treatment response, to generate biologically tractable therapeutic targets from polygenic signal, and, ultimately, to provide a powerful path to precision medicine.

Inflammatory bowel diseases, namely ulcerative colitis and Crohn's disease, are chronic and relapsing conditions that pose a growing burden on healthcare systems worldwide. Because of their complex and partly unknown etiology and pathogenesis, the management of ulcerative colitis and Crohn's disease can prove challenging not only from a clinical point of view but also for resource optimization. Artificial intelligence, an umbrella term that encompasses any cognitive function developed by machines for learning or problem solving, and its subsets machine learning and deep learning are becoming ever more essential tools with a plethora of applications in most medical specialties. In this regard gastroenterology is no exception, and due to the importance of endoscopy and imaging numerous clinical studies have been gradually highlighting the relevant role that artificial intelligence has in inflammatory bowel diseases as well. The aim of this review was to summarize the most recent evidence on the use of artificial intelligence in inflammatory bowel diseases in various contexts such as diagnosis, follow-up, treatment, prognosis, cancer surveillance, data collection, and analysis. Moreover, insights into the potential further developments in this field and their effects on future clinical practice were discussed.

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.

Inflammatory bowel disease (IBD) is a complex chronic immune disease with two subtypes: Crohn's disease and ulcerative colitis. Considering the differences in pathogenesis, etiology, clinical presentation, and response to therapy among patients, gastroenterologists mainly rely on endoscopy to diagnose and treat IBD during clinical practice. However, as exemplified by the increasingly comprehensive ulcerative colitis endoscopic scoring system, the endoscopic diagnosis, evaluation, and treatment of IBD still rely on the subjective manipulation and judgment of endoscopists. In recent years, the use of artificial intelligence (AI) has grown substantially in various medical fields, and an increasing number of studies have investigated the use of this emerging technology in the field of gastroenterology. Clinical applications of AI have focused on IBD pathogenesis, etiology, diagnosis, and patient prognosis. Large-scale datasets offer tremendous utility in the development of novel tools to address the unmet clinical and practice needs for treating patients with IBD. However, significant differences among AI methodologies, datasets, and clinical findings limit the incorporation of AI technology into clinical practice. In this review, we discuss practical AI applications in the diagnosis of IBD via gastroenteroscopy and speculate regarding a future in which AI technology provides value for the diagnosis and treatment of IBD patients.

(1) Background: Cerebral palsy (CP) is associated with a higher incidence of epileptic seizures. This study uses a prediction model to identify the factors associated with epilepsy in children with CP. (2) Methods: This is a retrospective longitudinal study of the clinical characteristics of 102 children with CP. In the study, there were 58 males and 44 females, 65 inpatients and 37 outpatients, 72 had epilepsy, and 22 had intractable epilepsy. The mean age was 16.6 ± 1.2 years, and the age range for this study was 12−18 years. Data were collected on the CP etiology, diagnosis, type of epilepsy and spasticity, clinical history, communication abilities, behaviors, intellectual disability, motor function, and feeding abilities from 2005 to 2020. A prediction model, Epi-PredictMed, was implemented to forecast the factors associated with epilepsy. We used the guidelines of “Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis” (TRIPOD). (3) Results: CP etiology [(prenatal > perinatal > postnatal causes) p = 0.036], scoliosis (p = 0.048), communication (p = 0.018), feeding disorders (p = 0.002), poor motor function (p < 0.001), intellectual disabilities (p = 0.007), and the type of spasticity [(quadriplegia/triplegia > diplegia > hemiplegia), p = 0.002)] were associated with having epilepsy. The model scored an average of 82% for accuracy, sensitivity, and specificity. (4) Conclusion: Prenatal CP etiology, spasticity, scoliosis, severe intellectual disabilities, poor motor skills, and communication and feeding disorders were associated with epilepsy in children with CP. To implement preventive and/or management measures, caregivers and families of children with CP and epilepsy should be aware of the likelihood that these children will develop these conditions.

The purpose of this narrative review is to describe the clinical applications of advanced computed tomography (CT) and magnetic resonance (MRI) techniques in patients affected by Crohn's disease (CD), giving insights about the added value of artificial intelligence (AI) in this field.

We performed a literature search comparing standardized and advanced imaging techniques for CD diagnosis. Cross-sectional imaging is essential for the identification of lesions, the assessment of active or relapsing disease and the evaluation of complications.

The studies reviewed show that new advanced imaging techniques and new MRI sequences could be integrated into standard protocols, to achieve a reliable quantification of CD activity, improve the lesions' characterization and the evaluation of therapy response. These promising tools are: dual-energy CT (DECT) post-processing techniques, diffusion-weighted MRI (DWI-MRI), dynamic contrast-enhanced MRI (DCE-MRI), Magnetization Transfer MRI (MT-MRI) and CINE-MRI. Furthermore, AI solutions show a potential when applied to radiological techniques in these patients. Machine learning (ML) algorithms and radiomic features prove to be useful in improving the diagnostic accuracy of clinicians and in attempting a personalized medicine approach, stratifying patients by predicting their prognosis.

Advanced imaging is crucial in the diagnosis, lesions' characterisation and in the estimation of the abdominal involvement in CD. New AI developments are promising tools that could support doctors in the management of CD affected patients.

The role of intestinal bacterial microbiota has been described as key in the pathophysiology of Crohn's disease (CD). CD is characterized by frequent relapses after periods of remission which are not entirely understood. In this paper, we investigate whether the heterogeneity in microbiota profiles in CD patients could be a suitable predictor for these relapses. This prospective observational study involved 259 CD patients, in which 41 provided an additional total of 62 consecutive fecal samples, with an average interval of 25 weeks in between each of these samples. Fecal microbiota was analyzed by massive genomic sequencing through 16 S rRNA amplicon sampling. We found that our 259 CD patients could be split into three distinct subgroups of microbiota (G1, G2, G3). From G1 to G3, we noticed a progressive decrease in alpha diversity (p ≤ 0.0001) but no change in the fecal calprotectin (FC) level. Focusing on the 103 consecutive samples from 41 CD patients, we showed that the patients microbiota profiles were remarkably stable over time and associated with increasing symptom severity. Investigating further this microbiota/severity association revealed that the first signs of aggravation are (1) a loss of the main anti-inflammatory Short-Chain Fatty Acids (SCFAs) Roseburia, Eubacterium, Subdoligranumum, Ruminococcus (P < 0.05), (2) an increase in pro-inflammatory pathogens Proteus, Finegoldia (P < 0.05) while (3) an increase of other minor SCFA producers such as Ezakiella, Anaerococcus, Megasphaera, Anaeroglobus, Fenollaria (P < 0.05). Further aggravation of clinical signs is significantly linked to the subsequent loss of these minor SCFAs species and to an increase in other proinflammatory Proteobacteria such as Klebsiella, Pseudomonas, Salmonella, Acinetobacter, Hafnia and proinflammatory Firmicutes such as Staphylococcus, Enterococcus, Streptococcus. (P < 0.05). To our knowledge, this is the first study (1) specifically identifying subgroups of microbiota profiles in CD patients, (2) relating these groups to the evolution of symptoms over time and (3) showing a two-step process in CD symptoms' worsening. This paves the way towards a better understanding of patient-to-patient heterogeneity, as well as providing early warning signals of future aggravation of the symptoms and eventually adapting empirically treatments.

Diagnosis of inflammatory bowel diseases (IBD) involves combining clinical, laboratory, endoscopic, histologic, and radiographic data. Artificial intelligence (AI) is rapidly being developed in various fields of medicine, including IBD. Because a key part in the diagnosis of IBD involves evaluating imaging data, AI is expected to play an important role in this aspect in the coming decades. We conducted a systematic literature review to highlight the current advancement of AI in diagnosing IBD from imaging data.

We performed an electronic PubMed search of the MEDLINE database for studies up to January 2022 involving IBD and AI. Studies using imaging data as input were included, and nonimaging data were excluded.

A total of 27 studies are reviewed, including 18 studies involving endoscopic images and nine studies involving other imaging data.

We highlight in this review the recent advancement of AI in diagnosing IBD from imaging data by summarizing the relevant studies, and discuss the future role of AI in clinical practice.

Inflammatory bowel disease (IBD) is a gastrointestinal chronic disease with an unpredictable disease course. Computational methods such as machine learning (ML) have the potential to stratify IBD patients for the provision of individualized care. The use of ML methods for IBD was surveyed, with an additional focus on how the field has changed over time.

On May 6, 2021, a systematic review was conducted through a search of MEDLINE and Embase databases, with the search structure ("machine learning" OR "artificial intelligence") AND ("Crohn* Disease" OR "Ulcerative Colitis" OR "Inflammatory Bowel Disease"). Exclusion criteria included studies not written in English, no human patient data, publication before 2001, studies that were not peer reviewed, nonautoimmune disease comorbidity research, and record types that were not primary research.

Seventy-eight (of 409) records met the inclusion criteria. Random forest methods were most prevalent, and there was an increase in neural networks, mainly applied to imaging data sets. The main applications of ML to clinical tasks were diagnosis (18 of 78), disease course (22 of 78), and disease severity (16 of 78). The median sample size was 263. Clinical and microbiome-related data sets were most popular. Five percent of studies used an external data set after training and testing for additional model validation.

Availability of longitudinal and deep phenotyping data could lead to better modeling. Machine learning pipelines that consider imbalanced data and that feature selection only on training data will generate more generalizable models. Machine learning models are increasingly being applied to more complex clinical tasks for specific phenotypes, indicating progress towards personalized medicine for IBD.