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Machine learning model for predicting DIBH non-eligibility in left-sided breast cancer radiotherapy: Development, validation and clinical impact analysis.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Jan 31, 2025
OBJECTIVE: Multi-day assessments accurately identify patients with left-sided breast cancer who are ineligible for irradiation in Deep Inspiration Breath Hold (DIBH) and minimise on-couch treatment time in those who are eligible. The challenge of imp...
Multiscale deep learning radiomics for predicting recurrence-free survival in pancreatic cancer: A multicenter study.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Jan 31, 2025
PURPOSE: This multicenter study aimed to develop and validate a multiscale deep learning radiomics nomogram for predicting recurrence-free survival (RFS) in patients with pancreatic ductal adenocarcinoma (PDAC).
Integrating neuroscience and artificial intelligence: EEG analysis using ensemble learning for diagnosis Alzheimer's disease and frontotemporal dementia.
Journal of neuroscience methods
Jan 31, 2025
BACKGROUND: Alzheimer's disease (AD) and frontotemporal dementia (FTD) are both progressive neurological disorders that affect the elderly. Distinguishing between individuals suffering from these two diseases in the early stages can be quite challeng...
Differential diagnosis of multiple system atrophy with predominant parkinsonism and Parkinson's disease using neural networks (part II).
Journal of the neurological sciences
Jan 31, 2025
Neural networks (NNs) possess the capability to learn complex data relationships, recognize inherent patterns by emulating human brain functions, and generate predictions based on novel data. We conducted deep learning utilizing an NN to differentiat...
Developing an interpretable machine learning model for diagnosing gout using clinical and ultrasound features.
European journal of radiology
Jan 31, 2025
OBJECTIVE: To develop a machine learning (ML) model using clinical data and ultrasound features for gout prediction, and apply SHapley Additive exPlanations (SHAP) for model interpretation.
The potential use of deep learning in performing autocorrection of setup errors in patients receiving radiotherapy.
Radiography (London, England : 1995)
Jan 31, 2025
INTRODUCTION: Modern radiotherapy practice relies on multiple approaches for verification of patient positioning. All of these techniques require experienced radiotherapists who understand the anatomical landmarks and the limitations of the used veri...
Personalized Cognitive Support via Social Robots.
Sensors (Basel, Switzerland)
Jan 31, 2025
This paper explores the use of personalized cognitive support through social robots to assist the elderly in maintaining cognitive health and emotional well-being. As aging populations grow, the demand for innovative solutions to address issues like ...
Exploring the assessment of post-cardiac valve surgery pulmonary complication risks through the integration of wearable continuous physiological and clinical data.
BMC medical informatics and decision making
Jan 31, 2025
BACKGROUND: Postoperative pulmonary complications (PPCs) following cardiac valvular surgery are characterized by high morbidity, mortality, and economic cost. This study leverages wearable technology and machine learning algorithms to preoperatively ...
Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches.
BMC medical informatics and decision making
Jan 31, 2025
BACKGROUND: The aim of this study was to evaluate the potential models to determine the most important anthropometric factors associated with type 2 diabetes mellitus (T2DM).
Using machine learning to predict outcomes following transcarotid artery revascularization.
Scientific reports
Jan 31, 2025
Transcarotid artery revascularization (TCAR) is a relatively new and technically challenging procedure that carries a non-negligible risk of complications. Risk prediction tools may help guide clinical decision-making but remain limited. We developed...
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