View clinical trials related to Gastrointestinal Diseases.
Filter by:Background: Accurate labeling of obstruction site on upright abdominal radiograph is a challenging task. The lack of ground truth leads to poor performance on supervised learning models. To address this issue, self-supervised learning (SSL) is proposed to classify normal, small bowel obstruction (SBO), and large bowel obstruction (LBO) radiographs using a few confirmed samples. Methods: A few number of confirmed and a large number of unlabeled radiographs were categorized based on the ground truth. The SSL model was firstly trained on the unlabeled radiographs, and then fine-tuned on the confirmed radiographs. ResNet50 and VGG16 were used for the embedded base encoders, whose weights and parameters were adjusted during training process. Furthermore, it was tested on an independent dataset, compared with supervised learning models and human interpreters. Finally, the t-SNE and Grad-CAM were used to visualize the model's interpretation.
Testing a questionnaire about treatment with steroids for skin, lung or gastric conditions
This cross-sectional case-control study aimed to determine whether there is a significant difference in the prevalence of impaired toilet training, LUTS, and functional bowel problems among children diagnosed with DCD and typically developing children (TDC).
This is a study to evaluate the prevalence of FGIDs in infants using the Thai version of Rome IV diagnostic questionnaire for functional gastrointestinal disorders in infants and evaluate the efficacy of Limosilactobacillus reuteri DSM 17938 to prevent FGIDs in infants.
In this study, the investigators aim to assess that infants with mild symptoms have significantly less (severe) symptoms after 14 days goat milk-based formula use compared to infants using cow's milk-based formula supplemented with probiotic drops.
Interleukin (IL)-6 is a cytokine produced in response to infection and tissue damage. IL-6 is believed to act as a key mediator in chronic inflammation and autoimmune diseases such as inflammatory bowel diseases. IL-6 is known to be involved in at least two distinct signalling pathways, classical and trans-signalling. The hypothesis is that classical signalling by IL-6 infers some beneficial effects (e.g. on gut barrier function), while excessive IL-6 trans-signalling may have detrimental effects. Olamkicept (FE 999301) has been shown in vitro to be a selective IL-6 trans-signalling inhibitor, and administered at lower doses (600 mg every 2nd week for 12 weeks) it has proven to induce clinical improvement for patients with ulcerative colitis. The aim of this trial is to investigate safety, tolerability, immunogenicity and pharmacokinetics of Olamkicept at higher doses (up to 2400 mg) to support the clinical development program. Our hypothesis is that treatment with higher doses of Olamkicept will result in greater clinical improvement for patients with inflammatory bowel diseases.
This study aims to evaluate the efficacy of fecal microbiota transplantation on the gastrointestinal symptoms, autistic symptoms and emotional behavior symptoms of patients with autism spectrum disorder, and investigate the relations between the brain-gut axis, cytokines and autism spectrum disorder. Fecal microbiota transplantation have the potentials to improve intestinal microbiota composition, regulate immunity, and then improve gastrointestinal symptoms, autistic symptoms, emotional behavior symptoms and sleep of children with autism spectrum disorder. Early intervention at school-age may even benefit development, improve cognition and prognosis.
The goal of this randomised trial is to learn about the role of AI in clinical coding practice. The main question it aims to answer is: Can the AI-based CAC system reduce the burden of clinical coding and also improve the quality of such coding? Participants will be asked to code clinical texts both while they use our CAC system and while they do not.
The study aims to evaluate the effectiveness of a dietary supplement (greens powder), on improving digestive health, quality of life, energy levels, and satiety over a 14-day period. Participants, aged 18-65 and experiencing mild digestive issues, will consume the greens powder daily and report outcomes through diaries and questionnaires.
Endoscopic ultrasound (EUS) visual impression is operator-dependant and can hinder diagnostic accuracy, especially in less experienced endoscopists. The implementation of artificial intelligence can potentially mitigate operator dependency and interpretation variability, helping or improving the overall accuracy. The investigators therefore aim to compare diagnostic accuracy between artificial intelligence (AI)-based model and the endoscopists when identifying normal anatomical structures in EUS-procedures.