Latest AI and machine learning research in gastroenterology for healthcare professionals.
Identifying cell types and states remains a time-consuming, error-prone challenge for spatial biology. While deep learning increasingly plays a role, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we develop TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates withou...
BACKGROUND/OBJECTIVES: Obesity is a global health issue, and in this context, bariatric surgery is considered the most effective treatment for severe cases. However, postoperative outcomes vary widely among individuals, driving the development of tools to predict body weight loss success. The main objective of this paper is to evaluate predictive variables for successful weight loss one year after...
Cancer remains the second leading cause of death worldwide, representing a substantial challenge to global health. Although traditional risk predictio...
Cancer continues to be among the most substantial health challenges globally. Among various natural compounds, berberine, an isoquinoline alkaloid obt...
BACKGROUND AND OBJECTIVE: In search of a key driver for the invasive growth of cancer metastasis, AKT2 is found to be exceptionally expressed in color...
BACKGROUND: Recent years have witnessed a proliferation of studies aimed at developing clinical models capable of predicting lymph node metastasis (LN...
Dysbiosis refers to the disruption of the gut microbiota balance and is the pathological basis of various diseases. The main pathogenic mechanisms inc...
Hepatocellular carcinoma (HCC) is a major complication of tyrosinemia type 1 (HT-1), an inborn error of metabolism affecting tyrosine catabolism. The ...
BACKGROUND: Systemic inflammatory response syndrome (SIRS) is a frequent and serious complication of acute pancreatitis (AP), often associated with in...
OBJECTIVE: To evaluate the feasibility of generative pre-trained transformer-4 (GPT-4) in generating structured reports (SRs) from mixed-language (Eng...
Protein misfolding diseases, including α1-antitrypsin deficiency (AATD), pose substantial health challenges, with their cellular progression still poo...
The accurate preclinical prediction of adverse drug reactions (ADRs), such as nausea and vomiting, remains a challenge. The Gastro-Intestinal Pacemake...
The importance of gastric cancer (GC) and the role of deep learning techniques in categorizing GC histopathology images have recently increased. Ident...
The American College of Radiology (ACR) Incidental Findings Committee (IFC) algorithm provides guidance for pancreatic cystic lesions (PCL) managemen...
BACKGROUND: We developed and evaluated a skeletal muscle deep-learning (SMDL) model using skeletal muscle computed tomography (CT) imaging to predict ...
BACKGROUND: Accurate diagnosis of ESD specimens is crucial for managing early gastric cancer. Identifying tumor areas in serially sectioned ESD specim...
Radiographic imaging is a non-invasive technique of considerable importance for evaluating tumor treatment response. However, redundancy in CT data an...
PURPOSE: Fluorescence imaging is critical for intraoperative intestinal perfusion assessment in colorectal surgery, yet its clinical adoption remains ...
Efficient segmentation of small hyperreflective dots, key biomarkers for diseases like macular edema, is critical for diagnosis and treatment monitori...
The use of machine learning (ML) techniques, particularly XGBoost and logistic regression, to predict sarcopenia among postsurgical gastric cancer pat...