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.
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http://dx.doi.org/10.3390/diagnostics13040735 | DOI Listing |
Bioinformatics
January 2025
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
Motivation: Ensuring connectivity and preventing fractures in tubular object segmentation are critical for downstream analyses. Despite advancements in deep neural networks (DNNs) that have significantly improved tubular object segmentation, existing methods still face limitations. They often rely heavily on precise annotations, hindering their scalability to large-scale unlabeled image datasets.
View Article and Find Full Text PDFJ Cell Mol Med
January 2025
Department of Medical Biology, Faculty of Medicine, Kutahya Health Sciences University, Kutahya, Turkey.
Chemotherapy is a potent tool against cancer, but drug resistance remains a major obstacle. To combat this, understanding the molecular mechanisms behind resistance in cancer cells and the protein expression changes driving these mechanisms is crucial. Targeting the Ubiquitin-Proteasome System (UPS) has proven effective in treating multiple myeloma and shows promise for solid tumours.
View Article and Find Full Text PDFBMC Health Serv Res
January 2025
Department of Industrial Engineering, Dalhousie University, PO Box 15000, Halifax, B3H 4R2, NS, Canada.
Background: The growing demand for healthcare services challenges patient flow management in health systems. Alternative Level of Care (ALC) patients who no longer need acute care yet face discharge barriers contribute to prolonged stays and hospital overcrowding. Predicting these patients at admission allows for better resource planning, reducing bottlenecks, and improving flow.
View Article and Find Full Text PDFJ Orthop Surg Res
January 2025
Department of Human Anatomy, Graduate School, Inner Mongolia Medical University, Hohhot, 010010, Inner Mongolia, China.
Purpose: The study aimed to develop a deep learning model for rapid, automated measurement of full-spine X-rays in adolescents with Adolescent Idiopathic Scoliosis (AIS). A significant challenge in this field is the time-consuming nature of manual measurements and the inter-individual variability in these measurements. To address these challenges, we utilized RTMpose deep learning technology to automate the process.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
College of Artificial Intelligence, Nanjing Agricultural University, Weigang No.1, Nanjing, 210095, Jiangsu, China.
Antimicrobial peptides (AMPs) have been widely recognized as a promising solution to combat antimicrobial resistance of microorganisms due to the increasing abuse of antibiotics in medicine and agriculture around the globe. In this study, we propose UniAMP, a systematic prediction framework for discovering AMPs. We observe that feature vectors used in various existing studies constructed from peptide information, such as sequence, composition, and structure, can be augmented and even replaced by information inferred by deep learning models.
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