Latest AI and machine learning research in gastroenterology for healthcare professionals.
This work introduces EffiSegNet, a novel segmentation framework leveraging transfer learning with a pre-trained Convolutional Neural Network (CNN) classifier as its backbone. Deviating from traditional architectures with a symmetric U-shape, EffiSegNet simplifies the decoder and utilizes full-scale feature fusion to minimize computational cost and the number of parameters. We evaluated our model o...
Depth estimation is a crucial task in endoscopy for three-dimensional reconstruction, surgical navigation, and augmented reality visualization. Stereo scope based depth estimation which involves capturing two images from different viewpoints, is a preferred method as it does not require specialized hardware. The depth information is encoded as the disparity between the left and right images. CNN-b...
In recent years, long non-coding RNAs (lncRNAs) have emerged as potential regulators of biological processes and genes, with the potential to serve as...
Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as vi...
Colorectal cancer is the most common malignant tumor of digestive tract, and the incidence of colorectal cancer in China is especially characterized b...
Splicing factors (SFs) are the major RNA-binding proteins (RBPs) and key molecules that regulate the splicing of mRNA molecules through binding to mRN...
OBJECTIVES: To develop an artificial intelligence (AI) tool with automated pancreas segmentation and measurement of pancreatic morphological informati...
OBJECTIVES: To evaluate the performance of machine learning models in predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy...
One of the most effective ways to treat liver cancer is to perform precise liver resection surgery, the key step of which includes precise digital i...
OBJECTIVE: To investigate the feasibility of developing a grading diagnostic model for schistosomiasis-induced liver fibrosis based on B-mode ultrason...
Automated insulin delivery (AID) is now integral to the clinical practice of type 1 diabetes (T1D). The objective of this pilot-feasibility study was...
Hepatocellular carcinoma (HCC) is a biologically heterogeneous tumor characterized by varying degrees of aggressiveness. The current treatment strateg...
PURPOSE: Radical surgery, the first-line treatment for patients with hepatocellular cancer (HCC), faces the dilemma of high early recurrence rates and...
To investigate the feasibility and accuracy of computer vision-based artificial intelligence technology in detecting and recognizing instruments and ...
UNLABELLED: Deep learning may detect biologically important signals embedded in tumor morphologic features that confer distinct prognoses. Tumor morph...
Recent studies have extensively used deep learning algorithms to analyze gene expression to predict disease diagnosis, treatment effectiveness, and su...
Biomedical research now commonly integrates diverse data types or views from the same individuals to better understand the pathobiology of complex dis...
Tumor mutational burden (TMB) is a significant predictive biomarker for selecting patients that may benefit from immune checkpoint inhibitor therapy. ...
The present letter to the editor is related to the study with the title "Automatic detection of small bowel (SB) lesions with different bleeding risk ...
BACKGROUND: nonalcoholic fatty liver disease (NAFLD) is a common liver disease affecting the global population and its impact on human health will con...