Gastroenterology

A deep learning model based on magnifying endoscopy with narrow-band imaging to evaluate intestinal metaplasia grading and OLGIM staging: A multicenter study.

BACKGROUND AND PURPOSE: Patients with stage III or IV of operative link for gastric intestinal metaplasia assessment (OLGIM) are at a higher risk of gastric cancer (GC). We aimed to construct a deep learning (DL) model based on magnifying endoscopy with narrow-band imaging (ME-NBI) to evaluate OLGIM staging.

Development and Preliminary Validation of a Novel Convolutional Neural Network Model for Predicting Treatment Response in Patients with Unresectable Hepatocellular Carcinoma Receiving Hepatic Arterial Infusion Chemotherapy.

The goal of this study was to evaluate the performance of a convolutional neural network (CNN) with preoperative MRI and clinical factors in predicting the treatment response of unresectable hepatocellular carcinoma (HCC) patients receiving hepatic arterial infusion chemotherapy (HAIC). A total of 191 patients with unresectable HCC who underwent HAIC in our hospital between May 2019 and March 2022...

Deep learning for automatic bowel-obstruction identification on abdominal CT.

RATIONALE AND OBJECTIVES: Automated evaluation of abdominal computed tomography (CT) scans should help radiologists manage their massive workloads, th...

Towards an EKG for SBO: A Neural Network for Detection and Characterization of Bowel Obstruction on CT.

A neural network was developed to detect and characterize bowel obstruction, a common cause of acute abdominal pain. In this retrospective study, 202 ...

Machine learning-based identification and characterization of mast cells in eosinophilic esophagitis.

BACKGROUND: Eosinophilic esophagitis (EoE) is diagnosed and monitored using esophageal eosinophil levels; however, EoE also exhibits a marked, underst...

Laparoscopic and Robot-assisted Laparoscopic Reimplantation for Lower Ureter Pathology. A Multi-institutional Comparative Study in 1343 Patients.

OBJECTIVE: To evaluate the outcomes of children with vesicoureteral reflux (VUR) and obstructive megaureter (OM) utilizing various laparoscopic and ro...

Effect of insurance status on perioperative outcomes after robotic pancreaticoduodenectomy: a propensity-score matched analysis.

The influence of Medicaid or being uninsured is prevailingly thought to negatively impact a patient's socioeconomic and postoperative course, yet litt...

From pixels to patient care: deep learning-enabled pathomics signature offers precise outcome predictions for immunotherapy in esophageal squamous cell cancer.

BACKGROUND: Immunotherapy has significantly improved survival of esophageal squamous cell cancer (ESCC) patients, however the clinical benefit was lim...

Fully-automated multi-organ segmentation tool applicable to both non-contrast and post-contrast abdominal CT: deep learning algorithm developed using dual-energy CT images.

A novel 3D nnU-Net-based of algorithm was developed for fully-automated multi-organ segmentation in abdominal CT, applicable to both non-contrast and ...

Segmentation of liver and liver lesions using deep learning.

Segmentation of organs and lesions could be employed for the express purpose of dosimetry in nuclear medicine, assisted image interpretations, and mas...

Elevated Plasma CXCL8 Concentrations in Significant Fibrosis but Not in Subclinical Rejection After Adult Liver Transplantation.

BACKGROUND: The noninvasive detection of subclinical graft injury including subclinical T cell-mediated rejection (subTCMR) is one of the unresolved c...

Upper gastrointestinal haemorrhage patients' survival: A causal inference and prediction study.

BACKGROUND: Upper gastrointestinal (GI) bleeding is a common medical emergency. This study aimed to develop models to predict critically ill patients ...

Artificial intelligence in liver imaging: methods and applications.

Liver disease is regarded as one of the major health threats to humans. Radiographic assessments hold promise in terms of addressing the current deman...

Screening oral drugs for their interactions with the intestinal transportome via porcine tissue explants and machine learning.

In vitro systems that accurately model in vivo conditions in the gastrointestinal tract may aid the development of oral drugs with greater bioavailabi...

Deep Learning for Automated Detection and Localization of Traumatic Abdominal Solid Organ Injuries on CT Scans.

Computed tomography (CT) is the most commonly used diagnostic modality for blunt abdominal trauma (BAT), significantly influencing management approach...

Establishment of a machine learning predictive model for non-alcoholic fatty liver disease: A longitudinal cohort study.

BACKGROUND AND AIMS: Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver disease, which lacks effective drug treatments. This study ai...

Artificial intelligence compared with human-derived patient educational materials on cirrhosis.

BACKGROUND: The study compared the readability, grade level, understandability, actionability, and accuracy of standard patient educational material a...

TFCNet: A texture-aware and fine-grained feature compensated polyp detection network.

PURPOSE: Abnormal tissue detection is a prerequisite for medical image analysis and computer-aided diagnosis and treatment. The use of neural networks...

The weight of BMI in impacting postoperative and oncologic outcomes in pancreaticoduodenectomy is attenuated by a robotic approach.

This study was undertaken to observe the effect of body mass index (BMI) on perioperative outcomes and survival when comparing robotic vs 'open' pancr...

Human-scale navigation of magnetic microrobots in hepatic arteries.

Using external actuation sources to navigate untethered drug-eluting microrobots in the bloodstream offers great promise in improving the selectivity ...