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Deep Learning for Classification of Inflammatory Bowel Disease Activity in Whole Slide Images of Colonic Histopathology.
The American journal of pathology
Jan 10, 2025
Grading activity of inflammatory bowel disease (IBD) using standardized histopathological scoring systems remains challenging due to limited availability of pathologists with IBD expertise and interobserver variability. In this study, a deep learning...
Multimodal deep-learning model using pre-treatment endoscopic images and clinical information to predict efficacy of neoadjuvant chemotherapy in esophageal squamous cell carcinoma.
Esophagus : official journal of the Japan Esophageal Society
Jan 10, 2025
BACKGROUND: Neoadjuvant chemotherapy is standard for advanced esophageal squamous cell carcinoma, though often ineffective. Therefore, predicting the response to chemotherapy before treatment is desirable. However, there is currently no established m...
Utilizing artificial intelligence and cellular population data for timely identification of bacteremia in hospitalized patients.
International journal of medical informatics
Jan 10, 2025
BACKGROUND: Bacteremia is a critical condition with high mortality that requires prompt detection to prevent progression to life-threatening sepsis. Traditional diagnostic approaches, such as blood cultures, are time-consuming. This limitation has en...
Bioinspired Smart Triboelectric Soft Pneumatic Actuator-Enabled Hand Rehabilitation Robot.
Advanced materials (Deerfield Beach, Fla.)
Jan 10, 2025
Quantitative assessment for post-stroke spasticity remains a significant challenge due to the encountered variable resistance during passive stretching, which can lead to the widely used modified Ashworth scale (MAS) for spasticity assessment dependi...
Artificial Intelligence for Predicting HER2 Status of Gastric Cancer Based on Whole-Slide Histopathology Images: A Retrospective Multicenter Study.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Jan 10, 2025
Human epidermal growth factor receptor 2 (HER2) positive gastric cancer (GC) shows a robust response to the combined therapy based HER2-targeted therapy. The application of these therapies is highly dependent on the evaluation of tumor HER2 status. H...
Patient- and clinician-based evaluation of large language models for patient education in prostate cancer radiotherapy.
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
Jan 10, 2025
BACKGROUND: This study aims to evaluate the capabilities and limitations of large language models (LLMs) for providing patient education for men undergoing radiotherapy for localized prostate cancer, incorporating assessments from both clinicians and...
Automated classification of coronary LEsions fRom coronary computed Tomography angiography scans with an updated deep learning model: ALERT study.
European radiology
Jan 10, 2025
OBJECTIVES: The use of deep learning models for quantitative measurements on coronary computed tomography angiography (CCTA) may reduce inter-reader variability and increase efficiency in clinical reporting. This study aimed to investigate the diagno...
Development and routine implementation of deep learning algorithm for automatic brain metastases segmentation on MRI for RANO-BM criteria follow-up.
NeuroImage
Jan 10, 2025
RATIONALE AND OBJECTIVES: The RANO-BM criteria, which employ a one-dimensional measurement of the largest diameter, are imperfect due to the fact that the lesion volume is neither isotropic nor homogeneous. Furthermore, this approach is inherently ti...
Machine learning in public health informatics: Evidence that complex sampling structures may not be needed for prediction models with imbalanced outcomes.
Annals of epidemiology
Jan 10, 2025
PURPOSE: The objective of this study is to investigate the predictive ability of machine learning models for imbalanced outcomes from national survey data without the use of sampling weights.
Predicting cognitive decline from neuropsychiatric symptoms and Alzheimer's disease biomarkers: A machine learning approach to a population-based data.
Journal of Alzheimer's disease : JAD
Jan 10, 2025
BACKGROUND: The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) models, along with demographic features and Alzheimer's disease (AD) biomarkers, to predict decline or no...
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