Gastroenterology

Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease.

Advanced metabolic-dysfunction-associated steatotic liver disease (MASLD) fibrosis (F3-4) predicts liver-related outcomes. Serum and elastography-based non-invasive tests (NIT) cannot yet reliably predict MASLD outcomes. The role of B-mode ultrasound (US) for outcome prediction is not yet known. We aimed to evaluate machine learning (ML) algorithms based on simple NIT and US for prediction of adve...

Deep learning-based allergic rhinitis diagnosis using nasal endoscopy images.

Allergic rhinitis typically has edematous and pale turbinates or erythematous and inflamed turbinates. While traditional approaches include using skin prick tests (SPT) to determine the presence of AR, It is often not related to actual symptoms, and it is an invasive test. We use deep learning to analyze nasal endoscopy images to investigate a quantitative method for diagnosing allergic rhinitis. ...

Corrigendum to 'CT-Based Deep Learning Predicts Prognosis in Esophageal Squamous Cell Cancer Patients Receiving Immunotherapy Combined with Chemotherapy' [Acad Radiol 32/6 (2025) 3397-3409].

Retraction: Unleashing the potential of exosome ncRNAs for early gastric cancer detection-a critical appraisal of machine learning approaches.

Microfluidic Biochip-Based Multiplexed Profiling of Small Extracellular Vesicles Proteins Integrated with Machine Learning for Early Disease Diagnosis.

Accurate early diagnosis is essential for preventing diseases and improving cure and survival rates. There are no reliable early-diagnosis biomarkers ...

AQP9 and IFITM1 as drivers of immune infiltration and tumor progression in IBD-associated colorectal cancer: from computational insights to experimental validation.

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract associated with an increased risk of colorectal canc...

An enhanced fusion of transfer learning models with optimization based clinical diagnosis of lung and colon cancer using biomedical imaging.

Lung and colon cancers (LCC) are among the foremost reasons for human death and disease. Early analysis of this disorder contains various tests, namel...

Deep learning-based video analysis for automatically detecting penetration and aspiration in videofluoroscopic swallowing study.

The videofluoroscopic swallowing study (VFSS) is the gold standard for diagnosing dysphagia, but its interpretation is time-consuming and requires exp...

Multilayer perceptron deep learning radiomics model based on Gd-BOPTA MRI to identify vessels encapsulating tumor clusters in hepatocellular carcinoma: a multi-center study.

OBJECTIVES: The purpose of this study is to mainly develop a predictive model based on clinicoradiological and radiomics features from preoperative ga...

A comparative study of machine learning models predicting post-hepatectomy liver failure: enhancing risk estimation in over 25,000 National Surgical Quality Improvement Program patients.

BACKGROUNDS/AIMS: Post-hepatectomy liver failure (PHLF) is a significant complication with an incidence rate between 8% and 12%. Machine learning (ML)...

A CT-Based Deep Learning Radiomics Nomogram for Early Recurrence Prediction in Pancreatic Cancer: A Multicenter Study.

BACKGROUND: Early recurrence (ER) following curative-intent surgery remains a major obstacle to improving long-term outcomes in patients with pancreat...

Psoas muscle CT radiomics-based machine learning models to predict response to infliximab in patients with Crohn's disease.

BACKGROUND: Crohn's disease (CD) is a chronic inflammatory bowel disease, with infliximab (IFX) commonly used for treatment. However, no clinically ap...

Multi-kingdom microbiota analysis reveals bacteria-viral interplay in IBS with depression and anxiety.

Irritable Bowel Syndrome (IBS) is a common gastrointestinal disorder frequently accompanied by psychological symptoms. Bacterial microbiota plays a cr...

Targeting PFKFB3 to restore glucose metabolism in acute pancreatitis via nanovesicle delivery.

BACKGROUND: Acute pancreatitis (AP) is a severe inflammatory disease frequently accompanied by disturbances in glucose metabolism, which further compl...

Leveraging machine learning and single-cell RNA sequencing strategies to develop a risk prognosis scoring based on liquid-liquid phase separation feature genes in pediatric hepatoblastoma.

BACKGROUND: Considerable evidence highlights the intricate association between liquid-liquid phase separation (LLPS) and tumorigenesis, progression, a...

Predicting acute diarrhoea in rectal cancer chemoradiotherapy: Secondary analysis of the phase III ARISTOTLE trial.

BACKGROUND: Neoadjuvant chemoradiotherapy is a standard treatment for locally advanced rectal cancer, but acute diarrhoea remains a significant side e...

Artificial intelligence, yes - But what do we need?

In our field, AI is already producing significant changes through the development of expert systems and devices based on machine learning and deep neu...

Evolving Role of Artificial Intelligence in Endoscopic Management of Inflammatory Bowel Disease: Diagnosis, Surveillance, and Assessment.

Inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis, presents substantial diagnostic and management challenges because ...

Predictive analysis of pediatric gastroenteritis risk factors and seasonal variations using VGG Dense HybridNetClassifier a novel deep learning approach.

Pediatric gastroenteritis is a major reason for sickness and death among children worldwide, especially in places where healthcare and clean sanitatio...

Multi-omics analysis identifies SNP-associated immune-related signatures by integrating Mendelian randomization and machine learning in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death globally, characterized by high morbidity and poor prognosis. The complex mo...