Gastroenterology

A deep neural network improves endoscopic detection of early gastric cancer without blind spots.

BACKGROUND: Gastric cancer is the third most lethal malignancy worldwide. A novel deep convolution neural network (DCNN) to perform visual tasks has been recently developed. The aim of this study was to build a system using the DCNN to detect early gastric cancer (EGC) without blind spots during esophagogastroduodenoscopy (EGD).

Neural Networks for Deep Radiotherapy Dose Analysis and Prediction of Liver SBRT Outcomes.

Stereotactic body radiation therapy (SBRT) is a relatively novel treatment modality, with little post-treatment prognostic information reported. This study proposes a novel neural network based paradigm for accurate prediction of liver SBRT outcomes. We assembled a database of patients treated with liver SBRT at our institution. Together with a three-dimensional (3-D) dose delivery plans for each ...

Applications of artificial intelligence to endoscopy practice: The view from Japan Digestive Disease Week 2018.

Effects of Food Contamination on Gastrointestinal Morbidity: Comparison of Different Machine-Learning Methods.

Morbidity prediction can be useful in improving the effectiveness and efficiency of medical services, but accurate morbidity prediction is often diffi...

Impact of nominal photon energies on normal tissue sparing in knowledge-based radiotherapy treatment planning for rectal cancer patients.

The interactive adjustment of the optimization objectives during the treatment planning process has made it difficult to evaluate the impact of beam q...

Identification of esophageal cancer pathway deviation and construction of a diagnosis model using three kernel genes.

The purpose of this study is to better understand the role of interleukin 35 (IL35) in esophageal carcinoma by comparing the mRNA level in Barrett's e...

Addressing priority challenges in the detection and assessment of colorectal polyps from capsule endoscopy and colonoscopy in colorectal cancer screening using machine learning.

BACKGROUND: Colorectal capsule endoscopy (CCE) is a potentially valuable patient-friendly technique for colorectal cancer screening in large populatio...

Relative Factors Analysis of Imatinib Trough Concentration in Chinese Patients with Gastrointestinal Stromal Tumor.

AIMS: Imatinib plasma trough levels (IM Cmin) have been reported to have a considerable clinical impact in patients with gastrointestinal stromal tumo...

3D Fabrication of Fully Iron Magnetic Microrobots.

Biocompatibility and high responsiveness to magnetic fields are fundamental requisites to translate magnetic small-scale robots into clinical applicat...

Natural Language Processing of Radiology Reports in Patients With Hepatocellular Carcinoma to Predict Radiology Resource Utilization.

OBJECTIVE: Radiology is a finite health care resource in high demand at most health centers. However, anticipating fluctuations in demand is a challen...

Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer.

PURPOSE: Tumor-stroma ratio (TSR) serves as an independent prognostic factor in colorectal cancer and other solid malignancies. The recent introductio...

Robotic Cardiac Surgery Part I: Anesthetic Considerations in Totally Endoscopic Robotic Cardiac Surgery (TERCS).

The application and evolution of total endoscopic robotic cardiac surgery (TERCS) has become greater as institutions and surgeons become more comforta...

Artificial intelligence and colonoscopy: Current status and future perspectives.

BACKGROUND AND AIM: Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endos...

iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding.

An enhancer is a short (50-1500bp) region of DNA that plays an important role in gene expression and the production of RNA and proteins. Genetic varia...

Simultaneous spatiotemporal tracking and oxygen sensing of transient implants in vivo using hot-spot MRI and machine learning.

A varying oxygen environment is known to affect cellular function in disease as well as activity of various therapeutics. For transient structures, wh...

Non-invasive machine learning estimation of effort differentiates sleep-disordered breathing pathology.

OBJECTIVE: Obstructive sleep-disordered breathing (SDB) events, unlike central events, are associated with increased respiratory effort. Esophageal pr...

Dynamic Regulation of Level Set Parameters Using 3D Convolutional Neural Network for Liver Tumor Segmentation.

Segmentation of liver tumors plays an important role in the choice of therapeutic strategies for liver disease and treatment monitoring. In this paper...

Deoxyribonuclease I Activity, Cell-Free DNA, and Risk of Liver Cancer in a Prospective Cohort.

BACKGROUND: Circulating cell-free DNA (cfDNA) is a proposed latent biomarker for several cancers, including liver cancer. Deoxyribonucleases (DNases) ...

Unsupervised Learning Approach for Comparing Multiple Transposon Insertion Sequencing Studies.

Transposon insertion sequencing (TIS) is a widely used technique for conducting genome-scale forward genetic screens in bacteria. However, few methods...

Predicting overall survival of patients with hepatocellular carcinoma using a three-category method based on DNA methylation and machine learning.

Hepatocellular carcinoma (HCC) is closely associated with abnormal DNA methylation. In this study, we analyzed 450K methylation chip data from 377 HCC...