Gastroenterology

NucVerse3D: generalizable 3D nuclear instance segmentation across heterogeneous microscopy modalities.

Accurate three-dimensional (3D) nuclear instance segmentation is a prerequisite for quantitative phenotyping in volumetric microscopy, yet remains challenging in densely packed tissues, irregular nuclear morphologies, and across heterogeneous imaging modalities. Here we present NucVerse3D, a deep-learning framework for generalized 3D nuclei instance segmentation that combines a residual attention ...

Artificial intelligence iterative imaging reconstruction with low-dose CT scans for hepatic metastases.

Contrast-enhanced CT is commonly used in the evaluation of hepatic metastatic lesions. This prospective study aimed to assess the capability of artificial intelligence iterative reconstruction (AIIR) in low-dose CT for detection of hepatic metastases. Thirty-two patients with hepatic metastases were enrolled and underwent dual-phase CT scans in the venous phase. Each patient received a standard-do...

Construction of a predictive model for the risk of non-alcoholic fatty liver disease in patients with sleep apnea syndrome based on multiple machine learning algorithms: a multicenter study.

BACKGROUND: Sleep apnea syndrome (SAS) is closely related to an increased risk of non-alcoholic fatty liver disease (NAFLD), but current clinical tool...

The dynamics of hepatitis B transmission with treatment and vaccination: A case study in the Ili Region of Xinjiang, China, by a physics-informed neural networks approach.

Hepatitis B virus (HBV) infection remains a leading cause of cirrhosis and hepatocellular carcinoma worldwide, and long-term control relies on sustain...

The role of MRCP in characterizing ductal communication and high-risk features in pancreatic cystic lesions.

Pancreatic cystic lesions are increasingly detected due to the widespread use of high-resolution cross-sectional imaging, particularly MRI and CT. The...

Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.

Small tissue biopsies, including renal core biopsies, bone marrow trephines, gastrointestinal endoscopic samples, prostate needle cores, liver biopsie...

Use of artificial intelligence in analysis of endoscopic images to detect residual disease or regrowth in rectal patients with complete clinical response to neoadjuvant chemoradiotherapy.

BACKGROUND: Patients with rectal cancer who have a complete clinical response (cCR) to neoadjuvant chemo/radiotherapy (nCRT) may opt for organ preserv...

Laparoscopic Roux-en-Y Gastric Bypass with the Maestro™ System: a Novel Collaborative Robotic Platform.

BACKGROUND: Laparoscopic Roux-en-Y gastric bypass (LRYGB) is a cornerstone of bariatric surgery, yet its complexity poses challenges in visualization ...

Multi-omics-based machine learning model predicts response and guides treatment in Crohn's disease: a case study in nutritional therapy-response to editorial.

A Natural Language Processing Framework for Structuring and Visualizing Clinical Trial Eligibility Criteria at Scale: Protocol for a Quantitative Study.

BACKGROUND: Eligibility criteria are essential to clinical trial design, guiding recruitment, and ensuring patient safety and scientific rigor. Howeve...

Development and validation of multivariable prognostic machine learning models to identify patients at risk for inadequate bowel preparation when undergoing colonoscopy: a prospective, multicentre study.

OBJECTIVE: Inadequate bowel preparation impairs the accuracy of colonoscopy and increases the burden on patients and healthcare systems. Consequently,...

Dual-Phase Computed Tomography-Based Deep Learning Architecture for Three-Year Survival Prediction in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a major global health burden, ranking as the sixth most common cancer and the third leading cause of cancer-related ...

The role of artificial intelligence in adenoma detection during colonoscopy: a systematic review and meta-analysis of randomized controlled trials-Artificial intelligence and adenoma detection.

BACKGROUND: Colonoscopy is the gold standard for colorectal cancer screening; however, it is limited by a substantial adenoma miss rate. We aimed to p...

Single-cell and machine learning reveal ROS-associated heterogeneity in hepatocellular carcinoma for precision medicine.

BACKGROUND: Reactive oxygen species (ROS) are critical regulators of tumor development and progression, yet their clinical significance and cellular m...

Multi-omics integration identifies ribosome biogenesis-active macrophage subpopulation and its key gene GNL2 in driving liver hepatocellular carcinoma progression and mechanisms.

BACKGROUND: Liver hepatocellular carcinoma (LIHC) is a common malignancy, yet the core genes driving its progression and potential therapeutic targets...

Application of artificial intelligence based on contrast-enhanced CT imaging for predicting peritoneal metastasis in patients with T3/T4 stage gastric cancer.

Gastric cancer, prevalent in East Asia, often presents with peritoneal metastasis at diagnosis, limiting surgical options and reducing survival rates....

Squid-Inspired Multifunctional Retainable Magnetic Soft Robot: Foreign Body Transport and Transmucosal Drug Delivery in Complex Gastrointestinal Environments.

Magnetically controlled soft microrobots, by virtue of their exceptional flexibility and noninvasiveness, exhibit immense potential in the field of mi...

Development and internal validation of a machine learning-based model for predicting postoperative complications after primary liver cancer resection.

OBJECTIVE: To identify risk factors for postoperative major complications after resection of primary liver cancer and to develop machine learning-base...

The Combination of Machine Learning and Metabolomics for the Clinical Diagnosis of Hepatocellular Carcinoma.

The early diagnosis of hepatocellular carcinoma (HCC) lacks biomarkers with a high sensitivity and specificity. This study was to find sensitive and s...

Automated quantification of interstitial lung abnormalities and emphysema on computed tomography: a predictive marker for postoperative pulmonary complications after esophagectomy.

BACKGROUND: Postoperative pulmonary complications (PPCs), including pneumonia, acute lung injury, and acute respiratory distress syndrome, are common ...