Gastroenterology

Leveraging SEER data through machine learning to predict distant lymph node metastasis and prognosticate outcomes in hepatocellular carcinoma patients.

OBJECTIVES: This study aims to develop and validate machine learning-based diagnostic and prognostic models to predict the risk of distant lymph node metastases (DLNM) in patients with hepatocellular carcinoma (HCC) and to evaluate the prognosis for this cohort.

Vascular liver segmentation: a narrative review on methods and new insights brought by artificial intelligence.

Liver vessel segmentation from routinely performed medical imaging is a useful tool for diagnosis, treatment planning and delivery, and prognosis evaluation for many diseases, particularly liver cancer. A precise representation of liver anatomy is crucial to define the extent of the disease and, when suitable, the consequent resective or ablative procedure, in order to guarantee a radical treatmen...

Identification of novel M2 macrophage-related molecule ATP6V1E1 and its biological role in hepatocellular carcinoma based on machine learning algorithms.

Hepatocellular carcinoma (HCC) remains the most prevalent form of primary liver cancer, characterized by late detection and suboptimal response to cur...

Automatic segmentation of esophageal cancer, metastatic lymph nodes and their adjacent structures in CTA images based on the UperNet Swin network.

OBJECTIVE: To create a deep-learning automatic segmentation model for esophageal cancer (EC), metastatic lymph nodes (MLNs) and their adjacent structu...

Hyperspectral imaging facilitating resect-and-discard strategy through artificial intelligence-assisted diagnosis of colorectal polyps: A pilot study.

BACKGROUND AND AIMS: The resect-and-discard strategy for colorectal polyps based on accurate optical diagnosis remains challenges. Our aim was to inve...

Development and validation of deep learning models for bowel obstruction on plain abdominal radiograph.

OBJECTIVE: Artificial intelligence (AI) could help medical practitioners in analyzing radiological images to determine the presence and site of bowel ...

Deep Neural Networks for Predicting Recurrence and Survival in Patients with Esophageal Cancer After Surgery

Esophageal cancer is a major cause of cancer-related mortality internationally, with high recurrence rates and poor survival even among patients tre...

Identification of A0 minimum ablative margins for colorectal liver metastases: multicentre, retrospective study using deformable CT registration and artificial intelligence-based autosegmentation.

BACKGROUND: Several ablation confirmation software methods for minimum ablative margin assessment have recently been developed to improve local outcom...

WCEbleedGen: A wireless capsule endoscopy dataset and its benchmarking for automatic bleeding classification, detection, and segmentation

Computer-based analysis of Wireless Capsule Endoscopy (WCE) is crucial. However, a medically annotated WCE dataset for training and evaluation of au...

Automatic Tumor Cellularity Measurement: AI-Based Pipeline for Multi-Organ Pathology Imaging.

Tumor Cellularity (TC) is an important metric for assessing organ tumor burden. However, manual cell counting is not feasible due to large volumes of ...

Personalized graph feature-based multi-omics data integration for cancer subtype identification

Cancer is a highly heterogeneous disease with significant variability in molecular features and clinical outcomes, making diagnosis and treatment ch...

GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images.

Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpret...

A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation

Accurately segmenting different organs from medical images is a critical prerequisite for computer-assisted diagnosis and intervention planning. Thi...

Utilization of machine learning methods for prediction of acute and late rectal toxicity due to curative prostate radiotherapy.

OBJECTIVE: Rectal toxicity is one of the primary dose-limiting side effects of prostate cancer radiotherapy, and consequential impairment on quality o...

BodySLAM: A Generalized Monocular Visual SLAM Framework for Surgical Applications

Endoscopic surgery relies on two-dimensional views, posing challenges for surgeons in depth perception and instrument manipulation. While Monocular ...

Effect of artificial intelligence implementation to the latest generation 4K colonoscopy.

<b>Indroduction:</b> Colonoscopy is an acclaimed screening test to detect colorectal cancer (CRC). The most important quality indicators f...

Establishment of diagnostic model and identification of diagnostic markers between liver cancer and cirrhosis based on multi-chip and machine learning.

OBJECTIVE: Most cases of hepatocellular carcinoma (HCC) arise as a consequence of cirrhosis. In this study, our objective is to construct a comprehens...

Deep-Transfer-Learning-Based Natural Language Processing of Serial Free-Text Computed Tomography Reports for Predicting Survival of Patients With Pancreatic Cancer.

PURPOSE: To explore the predictive potential of serial computed tomography (CT) radiology reports for pancreatic cancer survival using natural languag...

Prediction of Drug-Induced Liver Injury: From Molecular Physicochemical Properties and Scaffold Architectures to Machine Learning Approaches.

The process of developing new drugs is widely acknowledged as being time-intensive and requiring substantial financial investment. Despite ongoing eff...

Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations.

BACKGROUND: Homologous recombination plays a vital role in the occurrence and drug resistance of gastric cancer. This study aimed to screen new gastri...