OBJECTIVE: To assess the clinical value of the deep learning image reconstruction (DLIR) algorithm compared with conventional adaptive statistical iterative reconstruction-Veo (ASiR-V) in image quality, diagnostic confidence, and intestinal lesion de...
PURPOSE: This study aims to develop and validate a multiregional radiomics model to predict pathological complete response (pCR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC), and further evaluate the performance of the mode...
OBJECTIVE: Condylar remodeling is a key prognostic indicator in maxillofacial surgery for skeletal class II patients. This study aimed to develop and validate a fully automated method leveraging landmark-guided segmentation and registration for effic...
BACKGROUND AND AIMS: Accurate dietary intake assessment is essential for nutritional care in hospitals, yet it is time-consuming for caregivers and therefore not routinely performed. Recent advancements in artificial intelligence (AI) offer promising...
OBJECTIVE: We aimed to prospectively investigate whether bladder volume measured using deep learning artificial intelligence (AI) algorithms (AI-BV) is more accurate than that measured using conventional methods (C-BV) if using a portable ultrasound ...
BACKGROUND: Endometrial cancer (EC) is the 6th most common cancer among women worldwide. No effective non-invasive screening methods or approved blood biomarkers for EC exist. Previous research explored Attenuated Total Reflection-Fourier Transform I...
RATIONALE AND OBJECTIVES: To investigate lung changes in patients with polymyositis/dermatomyositis-associated interstitial lung disease (PM/DM-ILD) using quantitative CT and to construct a diagnostic model to evaluate the application of quantitative...
OBJECTIVES: To develop a convolutional neural network (CNN) model to diagnose thyroid cartilage invasion by laryngeal and hypopharyngeal cancers observed on computed tomography (CT) images and evaluate the model's diagnostic performance.
BACKGROUND: Traditional Chinese culture makes death a sensitive and taboo topic, leading patients and family members to refuse to choose palliative care.
PURPOSE: To investigate the factors influencing the length of percutaneous nephrolithotomy (PCNL) procedures and identify predictive variables for operation time using machine learning models.
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