Gastroenterology

Toward automated small bowel capsule endoscopy reporting using a summarizing machine learning algorithm: The SUM UP study.

BACKGROUND AND OBJECTIVES: Deep learning (DL) algorithms demonstrate excellent diagnostic performance for the detection of vascular lesions via small bowel (SB) capsule endoscopy (CE), including vascular abnormalities with high (P2), intermediate (P1) or low (P0) bleeding potential, while dramatically decreasing the reading time. We aimed to improve the performance of a DL algorithm by characteriz...

"Artificial histology" in colonic Neoplasia: A critical approach.

BACKGROUND: The histological assessment of colorectal precancer and cancer lesions is challenging and primarily impacts the clinical strategies of secondary colon cancer prevention. Artificial intelligence (AI) models may potentially assist in the histological diagnosis of this spectrum of phenotypical changes.

Machine learning based on alcohol drinking-gut microbiota-liver axis in predicting the occurrence of early-stage hepatocellular carcinoma.

BACKGROUND: Alcohol drinking and gut microbiota are related to hepatocellular carcinoma (HCC), but the specific relationship between them remains uncl...

Development and validation of an individualized nomogram for predicting distant metastases in gastric cancer using a CT radiomics-clinical model.

PURPOSE: This study aimed to develop and validate a model for accurately assessing the risk of distant metastases in patients with gastric cancer (GC)...

Pediatric Liver Transplant Pathology: An Update and Practical Consideration.

This review provides a summary of the diagnostic approach to pediatric liver transplantation (LT) pathology. It emphasizes the pathologic features of ...

Multiparametric MRI-based Machine Learning Radiomics for Predicting Treatment Response to Transarterial Chemoembolization Combined with Targeted and Immunotherapy in Unresectable Hepatocellular Carcinoma: A Multicenter Study.

RATIONALE AND OBJECTIVES: To develop and validate multiple machine learning predictive models incorporating clinical features and pretreatment multipa...

Cost-Effectiveness for Artificial Intelligence in Colonoscopy.

Artificial intelligence (AI) is set to transform the field of colonoscopy through the implementation of computer-assisted detection and diagnosis. Whi...

-targeted AI-driven vaccines: a paradigm shift in gastric cancer prevention.

, a globally prevalent pathogen Group I carcinogen, presents a formidable challenge in gastric cancer prevention due to its increasing antimicrobial r...

Artificial intelligence measured 3D lumbosacral body composition and clinical outcomes in rectal cancer patients.

INTRODUCTION: Patient body composition (BC) has been shown to help predict clinical outcomes in rectal cancer patients. Artificial intelligence algori...

F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center study.

BACKGROUND: This study aims to develop habitat radiomic models to predict overall survival (OS) for hepatocellular carcinoma (HCC), based on the chara...

Ultrasound Versus Elastography in the Diagnosis of Hepatic Steatosis: Evaluation of Traditional Machine Learning Versus Deep Learning.

The prevalence of fatty liver disease is on the rise, posing a significant global health concern. If left untreated, it can progress into more serious...

Creating a standardized tool for the evaluation and comparison of artificial intelligence-based computer-aided detection programs in colonoscopy: a modified Delphi approach.

BACKGROUND AND AIMS: Multiple computer-aided detection (CADe) software programs have now achieved regulatory approval in the United States, Europe, an...

Artificial Intelligence in Inflammatory Bowel Disease.

Artificial intelligence (AI) is being increasingly studied and implemented in gastroenterology. In inflammatory bowel disease (IBD), numerous AI model...

Role of Artificial Intelligence for Colon Polyp Detection and Diagnosis and Colon Cancer.

The broad use of artificial intelligence (AI) and its various applications have already shown significant impact in medicine and in everyday life. In ...

Interpretable machine learning model based on CT semantic features and radiomics features to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors.

To develop and validate a machine learning (ML) model which combined computed tomography (CT) semantic and radiomics features to preoperatively predic...

Machine Learning Integration with Single-Cell Transcriptome Sequencing Datasets Reveals the Impact of Tumor-Associated Neutrophils on the Immune Microenvironment and Immunotherapy Outcomes in Gastric Cancer.

The characteristics of neutrophils play a crucial role in defining the tumor inflammatory environment. However, the function of tumor-associated neutr...

CLMS: Bridging domain gaps in medical imaging segmentation with source-free continual learning for robust knowledge transfer and adaptation.

Deep learning shows promise for medical image segmentation but suffers performance declines when applied to diverse healthcare sites due to data discr...

Convolutional neural network classification of ultrasound parametric images based on echo-envelope statistics for the quantitative diagnosis of liver steatosis.

PURPOSE: Early detection and quantitative evaluation of liver steatosis are crucial. Therefore, this study investigated a method for classifying ultra...

Antigen-independent single-cell circulating tumor cell detection using deep-learning-assisted biolasers.

Circulating tumor cells (CTCs) in the bloodstream are important biomarkers for clinical prognosis of cancers. Current CTC identification methods are b...

Generalizability of lesion detection and segmentation when ScaleNAS is trained on a large multi-organ dataset and validated in the liver.

BACKGROUND: Tumor assessment through imaging is crucial for diagnosing and treating cancer. Lesions in the liver, a common site for metastatic disease...