BACKGROUND: Delineating the internal gross tumor volume (IGTV) is crucial for the treatment of non-small cell lung cancer (NSCLC). Deep learning (DL) enables the automation of this process; however, current studies focus mainly on multiple phases of ...
BACKGROUND: In sub-Saharan Africa, Plasmodium falciparum is the most prevalent species of malaria parasites. In endemic areas, malaria is mainly diagnosed using microscopy or rapid diagnostic tests (RDTs), which have limited sensitivity, and microsco...
BMC medical informatics and decision making
Apr 18, 2025
This study aimed to identify key predictors for the severity of High Altitude Pulmonary Edema (HAPE) to assist clinicians in promptly recognizing severely affected patients in the emergency department, thereby reducing associated mortality rates. Mul...
BACKGROUND: Dental anxiety is a pervasive problem worldwide, leading to avoidance of dental care, resulting in oral health problems and impacting daily life through social withdrawal and work absenteeism. Addressing this fear is an important public h...
BACKGROUND AND AIM: Colorectal cancer is among the most prevalent and deadliest cancers. Early prediction of metastasis in patients with colorectal cancer is crucial in preventing it from the advanced stages and enhancing the prognosis among these pa...
Heart failure (HF) remains a leading global cause of cardiovascular deaths, with its prevalence expected to rise in the upcoming decade. Measuring the heart ejection fraction (EF) is crucial for diagnosing and monitoring HF. Although echocardiography...
A segmentation-free 3D Convolutional Neural Network (3DCNN) model was adopted to estimate Visual Field (VF) in glaucoma cases using Optical Coherence Tomography (OCT) images. This study, conducted at a university hospital, included 6335 participants ...
This study aimed to identify distinct clusters of diabetic macular edema (DME) patients with differential anti-vascular endothelial growth factor (VEGF) treatment outcomes using an unsupervised machine learning (ML) approach based on radiomic feature...
The purpose of this study is to examine and interpret machine learning models that predict dry eye (DE)-related clinical signs, subjective symptoms, and clinician diagnoses by heavily weighting lifestyle factors in the predictions. Machine learning m...
Sepsis related acute respiratory distress syndrome (ARDS) is a common and serious disease in clinic. Accurate prediction of in-hospital mortality of patients is crucial to optimize treatment and improve prognosis under the new global definition of AR...
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