Long-term analysis of animal behavior has been limited by reliance on real-time sensors or manual scoring. Existing machine learning tools can automate analysis but often fail under variable conditions or ignore temporal dynamics. We developed a scal...
PURPOSE: Accurate pre-treatment dose prediction is essential for efficient radiotherapy planning. Although deep learning models have advanced automated dose distribution, comprehensive multi-tumor analyses remain scarce. This study assesses deep lear...
OBJECTIVES: To establish and validate deep learning (DL) models based on pre-treatment multiparametric magnetic resonance imaging (MRI) images of primary rectal cancer and basic clinical data for the prediction of synchronous liver metastases (SLM) i...
INTRODUCTION: AI has the potential to enhance diagnostics, optimize treatment planning, and improve patient care. However, concerns remain regarding professional autonomy, ethical considerations, and the need for adequate training. This research aims...
BACKGROUND: This retrospective observational research evaluates the potential applicability of artificial intelligence models to predict the length of hospital stay for patients with pleural empyema who underwent uniportal video-assisted thoracoscopi...
BMC medical informatics and decision making
May 19, 2025
BACKGROUND: Despite the global commitment to ending AIDS by 2030, the loss of follow-up (LTFU) in HIV care remains a significant challenge. To address this issue, a data-driven clinical decision tool is crucial for identifying patients at greater ris...
OBJECTIVES: This study aims to develop and validate a novel radiomics model utilizing magnetic resonance imaging (MRI) to predict progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) who are receiving a combin...
OBJECTIVE: Development of a deep learning model for accurate preoperative identification of glioblastoma and solitary brain metastases by combining multi-centre and multi-sequence magnetic resonance images and comparison of the performance of differe...
Identifying likely placebo responders can help design more efficient clinical trials by stratifying participants, reducing sample size requirements, and enhancing the detection of true drug effects. In response to this need, we developed a deep convo...
Dementia spectrum disorders, characterized by progressive cognitive decline, pose a significant global health burden. Early screening and diagnosis are essential for timely and accurate treatment, improving patient outcomes and quality of life. This ...
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