Gastroenterology

Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study.

BACKGROUND: With Surgomics, we aim for personalized prediction of the patient's surgical outcome using machine-learning (ML) on multimodal intraoperative data to extract surgomic features as surgical process characteristics. As high-quality annotations by medical experts are crucial, but still a bottleneck, we prospectively investigate active learning (AL) to reduce annotation effort and present a...

Unraveling the complexities of pathological voice through saliency analysis.

The human voice is an essential communication tool, but various disorders and habits can disrupt it. Diagnosis of pathological and abnormal voices is very important. Conventional diagnosis of these voice pathologies can be invasive and costly. Voice pathology disorders can be effectively detected using Artificial Intelligence and computer-aided voice pathology classification tools. Previous studie...

Comparison of clinical utility of deep learning-based systems for small-bowel capsule endoscopy reading.

BACKGROUND AND AIM: Convolutional neural network (CNN) systems that automatically detect abnormalities from small-bowel capsule endoscopy (SBCE) image...

Clinical feasibility of deep learning reconstruction in liver diffusion-weighted imaging: Improvement of image quality and impact on apparent diffusion coefficient value.

PURPOSE: Diffusion-weighted imaging (DWI) of the liver suffers from low resolution, noise, and artifacts. This study aimed to investigate the effect o...

Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization.

Computer-aided classification of diseases of the gastrointestinal tract (GIT) has become a crucial area of research. Medical science and artificial in...

Polygenic modelling and machine learning approaches in pharmacogenomics: Importance in downstream analysis of genome-wide association study data.

Genome-wide association studies (GWAS) have identified genetic variations associated with adverse drug effects in pharmacogenomics (PGx) research. How...

Artificial intelligence quantifying endoscopic severity of ulcerative colitis in gradation scale.

OBJECTIVES: Existing endoscopic scores for ulcerative colitis (UC) objectively categorize disease severity based on the presence or absence of endosco...

Identifying primary tumor site of origin for liver metastases via a combination of handcrafted and deep learning features.

Liver is one of the most common sites for metastases, which can occur on account of primary tumors from multiple sites of origin. Identifying the prim...

Outcomes of totally robotic Roux-en-Y gastric bypass in patients with BMI ≥ 50 kg/m: can the robot level out "traditional" risk factors?

Roux-en-Y gastric bypass (RYGB) in patients with body mass index (BMI) ≥ 50 kg/m is a challenging procedure and BMI ≥ 50 kg/m has been identified as i...

Analysis of clinical efficacy and safety of hand-sewn anastomosis for the digestive tract with Da Vinci robot in rectal cancer surgery.

PURPOSE: The study aimed to analyze the clinical efficacy and safety of hand-sewn anastomosis for the digestive tract with Da Vinci robot in rectal ca...

Prediction of Five-Year Survival Rate for Rectal Cancer Using Markov Models of Convolutional Features of RhoB Expression on Tissue Microarray.

The ability to predict survival in cancer is clinically important because the finding can help patients and physicians make optimal treatment decision...

Short- and long-term outcomes of robot-assisted versus thoracoscopy-assisted minimally invasive esophagectomy.

Deep learning enables the discovery of a novel cuproptosis-inducing molecule for the inhibition of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common and deadly cancers in the world. The therapeutic outlook for HCC patients has significantly i...

A multitask deep learning radiomics model for predicting the macrotrabecular-massive subtype and prognosis of hepatocellular carcinoma after hepatic arterial infusion chemotherapy.

BACKGROUND: The macrotrabecular-massive (MTM) is a special subtype of hepatocellular carcinoma (HCC), which has commonly a dismal prognosis. This stud...

Towards deep-learning (DL) based fully automated target delineation for rectal cancer neoadjuvant radiotherapy using a divide-and-conquer strategy: a study with multicenter blind and randomized validation.

PURPOSE: Manual clinical target volume (CTV) and gross tumor volume (GTV) delineation for rectal cancer neoadjuvant radiotherapy is pivotal but labor-...

Dimensionality reduction for deep learning in infrared microscopy: a comparative computational survey.

While infrared microscopy provides molecular information at spatial resolution in a label-free manner, exploiting both spatial and molecular informati...

Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning.

OBJECTIVES: The goal of this study is to demonstrate the performance of radiomics and CNN-based classifiers in determining the primary origin of gastr...

Construction of Immune Infiltration-Related LncRNA Signatures Based on Machine Learning for the Prognosis in Colon Cancer.

Colon cancer is one of the malignant tumors with high morbidity, lethality, and prevalence across global human health. Molecular biomarkers play key r...

Long-term Outcomes of Robot-assisted Versus Laparoscopic Surgery for Colon Cancer: A Nationwide Register-based Cohort Study.

OBJECTIVE: To determine long-term survival in patients undergoing robot-assisted surgery (RAS) or laparoscopic surgery (LAS) for colon cancer.

Deep Learning and Minimally Invasive Endoscopy: Automatic Classification of Pleomorphic Gastric Lesions in Capsule Endoscopy.

INTRODUCTION: Capsule endoscopy (CE) is a minimally invasive examination for evaluating the gastrointestinal tract. However, its diagnostic yield for ...