OBJECTIVES: To evaluate the value of employing artificial intelligence (AI)-assisted CT pulmonary angiography (CTPA) for patients with chronic thromboembolic pulmonary hypertension (CTEPH) and chronic thromboembolic disease (CTED).
OBJECTIVES: This study aims to develop a deep learning algorithm for differentiating aneurysmal subarachnoid hemorrhage (aSAH) from non-aneurysmal subarachnoid hemorrhage (naSAH) using non-contrast computed tomography (NCCT) scans.
OBJECTIVES: Artifacts in clinical MRI can compromise the performance of AI models. This study evaluates how different data augmentation strategies affect an AI model's segmentation performance under variable artifact severity.
OBJECTIVES: To evaluate how different test set sampling strategies-random selection and balanced sampling-affect the performance of artificial intelligence (AI) models in pediatric wrist fracture detection using radiographs, aiming to highlight the n...
BACKGROUND: Recent evidence has shown that machine learning (ML) techniques can accurately forecast adverse cardiovascular and limb events in patients with intermittent claudication. This is the first study to compare the predictive performance of ML...
Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
Nov 1, 2025
BACKGROUND & AIMS: Diagnosis of bile acid diarrhea (BAD) has been based on 48-hour fecal BA excretion; serum 7αC4 (C4) has been used to screen for BAD. Optimal diagnostic cutoffs for C4 and biochemical measurements in a single stool sample are unknow...
OBJECTIVES: Clinical assessment of lymphedema, particularly for lymphedema severity and fluid-fibrotic lesions, remains challenging with traditional methods. We aimed to develop and validate a deep learning segmentation tool for automated tissue comp...
ObjectiveWe aimed to characterize the impact of startle and surprise, both independently and in combination, on subjective feelings, behavior (task performance and gaze behavior), and several physiological parameters.BackgroundThe effects of startle ...
To develop and validate an explainable machine learning (ML) tool to help clinicians predict the risk of propofol-associated hypertriglyceridemia in critically ill patients receiving propofol sedation. Patients from 11 intensive care units (ICUs) a...
PURPOSE: The aim of this study was to develop and validate CT venous phase image-based radiomics to predict BRAF gene mutation status in preoperative colorectal cancer patients.
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