Gastroenterology

Should We Use Behavioural Predictions in Organ Allocation?

Medical predictions, for example, concerning a patient's likelihood of survival, can be used to efficiently allocate scarce resources. Predictions of patient behaviour can also be used-for example, patients on the liver transplant waiting list could receive lower priority based on a high likelihood of non-adherence to their immunosuppressant medication regimen or of drinking excessively. But is th...

Early prediction of adverse outcomes in liver cirrhosis using a CT-based multimodal deep learning model.

PURPOSE: Early-stage cirrhosis frequently presents without symptoms, making timely identification of high-risk patients challenging. We aimed to develop a deep learning-based triple-modal fusion liver cirrhosis network (TMF-LCNet) for the prediction of adverse outcomes, offering a promising tool to enhance early risk assessment and improve clinical management strategies.

Deep learning predicts onset acceleration of 38 age-associated diseases from blood and body composition biomarkers in the UK Biobank.

A major challenge in multimorbid aging is understanding how diseases co-occur and identifying high-risk groups for accelerated disease development, bu...

Impact of Artificial Intelligence on the Timing of Recurrent Laryngeal Nerve Recognition during Robot-Assisted Minimally Invasive Esophagectomy.

BACKGROUND: As a first step to prevent recurrent laryngeal nerve (RLN) palsy, we have developed an artificial intelligence (AI)-based anatomical recog...

Unraveling the role of perineural invasion in cancer progression across multiple tumor types.

Perineural invasion (PNI) refers to the infiltration of tumor cells into the connective tissue of nerves and is increasingly recognized as a pathologi...

Predictive modeling for early detection of refractory esophageal stricture following esophageal atresia surgery: insight from a machine learning study.

BACKGROUND: Refractory esophageal stricture (RES) presents a challenging complication after esophageal atresia (EA) repair. Earlier identification of ...

A machine learning model integrating clinical-radiomics-deep learning features accurately predicts postoperative recurrence and metastasis of primary gastrointestinal stromal tumors.

OBJECTIVES: Post-surgical prediction of recurrence or metastasis for primary gastrointestinal stromal tumors (GISTs) remains challenging. We aim to de...

AI-based CT assessment of sarcopenia in borderline resectable pancreatic Cancer: A narrative review of clinical and technical perspectives.

BACKGROUND: Sarcopenia, defined as the progressive loss of skeletal muscle mass and function, has been associated with poor prognosis in patients with...

Diagnostic Performance of Radiomics for Differentiating Intrahepatic Cholangiocarcinoma from Hepatocellular Carcinoma: A Systematic Review and Meta-analysis.

BACKGROUND: Differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC) is essential for selecting the most effective tr...

Corrigendum to "SHapley Additive exPlanations (SHAP)-based, multivariate machine-learning techniques with external validation: Construction of a preoperative interpretable predictive model for intestinal resection of incarcerated inguinal hernia" [Surgery 184 (2025) 109406].

The impact of artificial intelligence on the adenoma detection rate : Comparison between experienced, intermediate and trainee endoscopists' adenoma detection rate.

BACKGROUND: Artificial intelligence (AI) is a promising tool to achieve a high adenoma detection rate (ADR). The aim of this study is to evaluate the ...

Artificial Intelligence-Powered Spatial Analysis of Immune Phenotypes in Resected Pancreatic Cancer.

IMPORTANCE: Although tumor-infiltrating lymphocytes (TILs) have been implicated as prognostic biomarkers across various malignancies, the clinical app...

A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images.

OBJECTIVE: To investigate the potential of a hybrid multi-instance learning model (TGMIL) combining Transformer and graph attention networks for class...

How appropriately can generative artificial intelligence platforms, including GPT-4, Gemini, Bing, and Wrtn, answer questions about colon cancer in the Korean language?

PURPOSE: This study aims to assess the performance of 4 generative artificial intelligence (AI) platforms-Gemini (formerly Bard), Bing, GPT-4, and Wrt...

[Current situation and development trends of minimally invasive surgical procedures in the treatment of pancreatic tumors].

With the rapid development of modern medical technology,minimally invasive surgical procedures are playing an increasingly important role in the field...

[The application of artificial intelligence in enhancing the safety of pancreatic surgeries].

Pancreatic surgeries have long been considered as challenging procedures due to its complex surgical characteristics. Establishing a surgical safety s...

Machine learning prediction of intestinal α-glucosidase inhibitors using a diverse set of ligands: a drug repurposing effort with drugBank database screening.

UNLABELLED: The global rise in diabetes mellitus (DM) poses a significant health challenge, necessitating effective therapeutic interventions. α-Gluco...

Neural network prediction model based on Levy flight and natural biomimetic technology for its application in cancer prediction.

Precise forecasting of cancer outcomes is essential for medical professionals to assess the well-being of patients and develop customized therapeutic ...

DeepSeek-assisted LI-RADS classification: AI-driven precision in hepatocellular carcinoma diagnosis.

BACKGROUND: The clinical utility of the DeepSeek-V3 (DSV3) model in enhancing the accuracy of Liver Imaging Reporting and Data System (LI-RADS, LR) cl...

Validation of a Pretrained Artificial Intelligence Model for Pancreatic Cancer Detection on Diagnosis and Prediagnosis Computed Tomography Scans.

PURPOSE: To evaluate PANCANAI, a previously developed AI model for pancreatic cancer (PC) detection, on a longitudinal cohort of patients. In particul...