Hospital-acquired infections (HAIs) are serious complication for patients with acute ischemic stroke (AIS), often resulting in poor functional outcomes. However, no existing model can specifically predict HAI in AIS patients. Therefore, we employed t...
Cerebral microbleeds (CMB) represent a feature of cerebral small vessel disease (cSVD), a prominent vascular contributor to age-related cognitive decline, dementia, and stroke. They are visible as spherical hypointense signals on T2*- or susceptibili...
In the current scenario, decision-making models are essential for analyzing real-world problems. To address the dynamic nature of these problems, fuzzy decision-making models have been proposed by various researchers. However, an advanced technique i...
Alzheimer's disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the nervous system, affecting the cognitive ability of the human brain. Over the past two decades, neuroimaging data from Magnetic Resonance Imaging (MRI)...
Chronic wounds are a syndrome that affects around 4% of the world population due to several pathologies. The COV-19 pandemic has enforced the need of developing new techniques and technologies that can help clinicians to monitor the affected patients...
Identifying which patients should undergo serologic screening for celiac disease (CD) may help diagnose patients who otherwise often experience diagnostic delays or remain undiagnosed. Using anonymized outpatient data from the electronic medical reco...
OBJECTIVE: To comprehensively investigate the predictive value of thyroid hormone sensitivity parameters for cervical lymph node metastasis in patients diagnosed with differentiated thyroid cancer (DTC) undergoing total thyroidectomy and neck lymph n...
RATIONALE AND OBJECTIVES: To evaluate and compare image quality of different energy levels of virtual monochromatic images (VMIs) using standard versus strong deep learning spectral reconstruction (DLSR) on dual-energy CT pulmonary angiogram (DECT-PA...
RATIONALE AND OBJECTIVES: To develop and validate a multimodal deep learning (DL) model based on computed tomography (CT) images and clinical knowledge to predict lymph node metastasis (LNM) in early lung adenocarcinoma.
BACKGROUND: Duodenal stump leakage is one of the most critical complications following gastrectomy surgery, with a high mortality rate. The present study aimed to establish a predictive model based on machine learning for forecasting the occurrence o...
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