BACKGROUND: It was essential to identify individuals at high risk of fragility fracture and prevented them due to the significant morbidity, mortality, and economic burden associated with fragility fracture. The quantitative ultrasound (QUS) showed p...
OBJECTIVE: This study aimed to develop and evaluate machine-learning models for predicting the onset of overweight in adolescents aged 14‒17, utilizing easily collectible personal information.
PURPOSE: This study aims to develop a non-invasive diagnosis model using machine learning (ML) for identifying high-risk IgG4 Hashimoto's thyroiditis (HT) patients.
BACKGROUND: The TNFRSF9 molecule is pivotal in thyroid carcinoma (THCA) development. This study utilizes Pathomics techniques to predict TNFRSF9 expression in THCA tissue and explore its molecular mechanisms.
PURPOSE: This study aims to develop a deep learning-based computer-aided diagnosis (CAD) system for the automatic detection and classification of lateral cervical lymph nodes (LNs) on original ultrasound images of papillary thyroid carcinoma (PTC) pa...
OBJECTIVE: To construct a risk prediction model for assisted diagnosis of Diabetic Nephropathy (DN) using machine learning algorithms, and to validate it internally and externally.
PURPOSE: Thyroid-associated ophthalmopathy (TAO) may result in increased metabolism and abnormalities in microcirculation. The fractal dimension (Df) of retinal vessels has been shown to be related to the pathology of a number of ophthalmic disorders...
OBJECTIVE: Distant metastasis of thyroid cancer often indicates poor prognosis, and it is important to identify patients who have developed distant metastasis or are at high risk as early as possible. This paper aimed to predict distant metastasis of...
BACKGROUND: Machine learning is increasingly recognized as a viable approach for identifying risk factors associated with diabetic kidney disease (DKD). However, the current state of real-world research lacks a comprehensive systematic analysis of th...
BACKGROUND: The identification of associated overweight risk factors is crucial to future health risk predictions and behavioral interventions. Several consensus problems remain in machine learning, such as cross-validation, and the resulting model m...