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Abstract Background Tuberculous pericardial effusion (TPE) presents significant diagnostic challenges due to its nonspecific clinical presentation and similarities with other types of pericardial effusion. The available ...

Abstract Background Surgical site infections (SSIs) continue to exert a substantial burden on healthcare systems, particularly in resource-limited settings where they contribute to prolonged hospitalizations, escalated c...

Abstract Objective Machine learning-based approaches were applied to explore prognostic risk factors for patients with tuberculous meningitis (TBM) and develop a risk prediction model for their 1-year post-treatment poor...

Abstract Background Enchondromas (EC) present cartilaginous tumors that are difficult to differentiate from their intermediate counterpart, atypical cartilaginous tumors (ACT). Histologically, tumor distinction of these ...

Abstract Purpose This study aimed to identify the effectiveness of free water MRI (FW-MRI) features for predicting amyloid-beta (Aβ) statuses in Alzheimer’s disease (AD) by constructing diagnostic models using machine...

Abstract Background Interstitial lung disease (ILD) is characterized by marked heterogeneity and an overall poor prognosis, with many patients experiencing rapid progression and high short-term mortality. Accurate early ...

Abstract Background Carotid atherosclerosis is a kind of systemic atherosclerosis in the carotid arteries. It remains one of the leading causes of ischemic stroke. However, the efficiency of treatment is insufficient. Th...

Abstract Background Accurate prediction of response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) is essential for optimizing treatment decisions. This study aimed to deve...

Abstract Backgrounds Acute primary angle-closure glaucoma (APACG) and chronic primary angle-closure glaucoma (CPACG) exhibit distinct clinical features, yet the molecular mechanisms underlying their differing progression...

Abstract Background Machine learning methods are widely used to detect behavioural data patterns. Although these new mathematical methods are useful tools, the interpretation of the results are often ambivalent unless ...

Abstract Background Understanding how the respiratory microbiota matures with age is key to improving poultry health and pathogen surveillance, yet the ecological processes shaping this transition remain elusive. We aime...

Abstract Background Dyspnea is one of the most common symptoms in the post-acute phase of COVID-19 pneumonia. Conventional pulmonary function tests and computed tomography (CT) scores often fail to show correlation with ...

Abstract Background This study aims to explore spatial heterogeneity within tumors, establish and validate an interpretable machine learning model combining habitat radiomics and deep learning, and investigate its predic...

Abstract Background The prognosis and progression mechanisms of bladder cancer (BLCA) are highly heterogeneous, driven by complex genetic and epigenetic alterations. This study aimed to construct a robust prognostic sign...

Abstract Background Postoperative complications of mediastinal tumors significantly impact patients’ quality of life and long-term outcomes; however, a notable gap in the development of predictive tools for their occur...

Abstract Background Knee osteoarthritis (OA) is a leading global cause of disability, yet conventional tools lack sensitivity for early detection and precise prognostication. Artificial intelligence (AI) and machine lear...

Abstract Background Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder that is linked to cardiovascular, metabolic, and neurocognitive complications. However, its diagnosis relies on polysomnograp...