BACKGROUND: There is no standard practice for intensive care admission after non-small cell lung cancer surgery. In this study, we aimed to determine the need for intensive care admission after non-small cell lung cancer surgery with deep learning mo...
OBJECTIVES: This study aims to develop a reliable and interpretable predictive model for long-term survival in Type A aortic dissection (TAAD) patients, utilizing machine learning (ML) algorithms.
A common shoulder problem that significantly detracts from patients' quality of life is shoulder instability (SI). Abnormal scapular positioning and movement are closely associated with rotator cuff injuries and SI, as shown by several studies. The a...
OBJECTIVE: The incidence of thyroid cancer (TC) is increasing in China, largely due to overdiagnosis from widespread screening and improved ultrasound technology. Identifying precise TC biomarkers is crucial for accurate diagnosis and effective treat...
BACKGROUND: Male infertility contributes to 20-30% of infertility cases, yet traditional diagnostic and treatment methods face limitations in accuracy and consistency. Artificial intelligence (AI) promises to transform male infertility management wit...
BACKGROUND: Studies on the epidemiological characteristics, treatment strategies and prognosis of nasal and paranasal sinus cancer are still relatively limited.
BACKGROUND: Arteriovenous fistula stenosis is a common complication in hemodialysis patients, yet effective predictive tools are lacking. This study aims to develop an interpretable machine learning model for stenosis risk prediction.
BACKGROUND: Ischemic stroke (IS) is one of the most common causes of disability in adults worldwide. This study aimed to identify key genes related to the inflammatory response to provide insights into the mechanisms and management of IS.
OBJECTIVES: This study aimed to identify risk factors for diabetic retinopathy (DR) and develop machine learning (ML)-based predictive models using routine laboratory data in patients with type 2 diabetes mellitus (T2DM).
OBJECTIVES: This study aimed to develop a model for predicting peripheral lymph node metastasis (LNM) in thyroid cancer patients by combining enhanced CT radiomic features with machine learning algorithms. It increased the clinical utility and interp...