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Machine learning-based prognostic prediction for acute ischemic stroke using whole-brain and infarct multi-PLD ASL radiomics.

BMC medical imaging
INTRODUCTION: Accurate early prognostic prediction for acute ischemic stroke (AIS) is essential for guiding personalized treatment. This study aimed to assess the predictive value of radiomics features from whole-brain and infarct cerebral blood flow...

Readiness to use artificial intelligence: a comparative study among dental faculty members and students.

BMC medical education
BACKGROUND: Artificial intelligence (AI) is prone to become a key element in dentistry, especially education and practice. Understanding the dental students' perspectives, who will be the next generation of practitioners, is crucial for effective tec...

Turkish medical oncologists' perspectives on integrating artificial intelligence: knowledge, attitudes, and ethical considerations.

BMC medical ethics
BACKGROUND: Integrating artificial intelligence (AI), especially large language models (LLM) into oncology has potential benefits, yet medical oncologists' knowledge, attitudes, and ethical concerns remain unclear. Understanding these perspectives is...

Gut microbiome alterations and hepatic encephalopathy post-TIPS in liver cirrhosis patients.

Journal of translational medicine
BACKGROUND: The transjugular intrahepatic portosystemic shunt (TIPS), a crucial tool for treating complications related to portal hypertension in patients with liver cirrhosis, is often associated with an increased risk of postoperative complications...

Prediction of three-year all-cause mortality in patients with heart failure and atrial fibrillation using the CatBoost model.

BMC cardiovascular disorders
BACKGROUND: Heart failure and atrial fibrillation (HF-AF) frequently coexist, resulting in complex interactions that substantially elevate mortality risk. This study aimed to develop and validate a machine learning (ML) model predicting the 3-year al...

Multi-modality radiomics diagnosis of breast cancer based on MRI, ultrasound and mammography.

BMC medical imaging
OBJECTIVE: To develop a multi-modality machine learning-based radiomics model utilizing Magnetic Resonance Imaging (MRI), Ultrasound (US), and Mammography (MMG) for the differentiation of benign and malignant breast nodules.

Identification and single-cell analysis of prognostic genes related to mitochondrial and neutrophil extracellular traps in bladder cancer.

Scientific reports
The development of bladder cancer (BLCA) is associated with mitochondrial dysfunction and neutrophil extracellular traps (NETs); however, the relationship between mitochondrial function and NET formation in BLCA remains poorly understood. In this stu...

Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis.

Scientific reports
This study developed an immune-related long non-coding RNAs (lncRNAs)-based prognostic signature by integrating multi-omics data and machine learning algorithms to predict survival and therapeutic responses in breast cancer patients. Utilizing transc...

Prior knowledge of anatomical relationships supports automatic delineation of clinical target volume for cervical cancer.

Scientific reports
Deep learning has been used for automatic planning of radiotherapy targets, such as inferring the clinical target volume (CTV) for a given new patient. However, previous deep learning methods mainly focus on predicting CTV from CT images without cons...