AIMC Topic: Follow-Up Studies
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AI-enabled opportunistic measurement of liver steatosis in coronary artery calcium scans predicts cardiovascular events and all-cause mortality: an AI-CVD study within the Multi-Ethnic Study of Atherosclerosis (MESA).
BMJ open diabetes research & care
Apr 12, 2025
INTRODUCTION: About one-third of adults in the USA have some grade of hepatic steatosis. Coronary artery calcium (CAC) scans contain more information than currently reported. We previously reported new artificial intelligence (AI) algorithms applied ...
Preoperative assessment in lymph node metastasis of pancreatic ductal adenocarcinoma: a transformer model based on dual-energy CT.
World journal of surgical oncology
Apr 9, 2025
BACKGROUND: Deep learning(DL) models can improve significantly discrimination of lymph node metastasis(LNM) of pancreatic ductal adenocarcinoma(PDAC), but have not been systematically assessed.
Randomized Trial on Electroacupuncture for Recovery of Postoperative Gastrointestinal Function Based on Long-Term Monitoring Device.
Annals of surgical oncology
Apr 4, 2025
BACKGROUND: This research aimed to explore the efficacy and safety of electroacupuncture in promoting the recovery of postoperative gastrointestinal function and to discuss the potential mechanism on the basis of heart rate variability (HRV).
Cardiometabolic index predicts cardiovascular events in aging population: a machine learning-based risk prediction framework from a large-scale longitudinal study.
Frontiers in endocrinology
Apr 1, 2025
BACKGROUND: While the Cardiometabolic Index (CMI) serves as a novel marker for assessing adipose tissue distribution and metabolic function, its prognostic utility for cardiovascular disease (CVD) events remains incompletely understood. This investig...
Application of machine learning models to identify predictors of good outcome after laparoscopic fundoplication.
Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
Mar 21, 2025
BACKGROUND: Laparoscopic fundoplication remains the gold standard treatment for gastroesophageal reflux disease. However, 10% to 20% of patients experience new, persistent, or recurrent symptoms warranting further treatment. Potential predictors for ...
Development of a natural language processing algorithm to extract social determinants of health from clinician notes.
American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
Mar 6, 2025
Disparities in access to the organ transplant waitlist are well-documented, but research into modifiable factors has been limited due to a lack of access to organized prewaitlisting data. This study aimed to develop a natural language processing (NLP...
Leveraging Deep Learning in Real-Time Intelligent Bladder Tumor Detection During Cystoscopy: A Diagnostic Study.
Annals of surgical oncology
Mar 6, 2025
BACKGROUND: Accurate detection of bladder lesions during cystoscopy is crucial for early tumor diagnosis and recurrence monitoring. However, conventional visual inspection methods have low and inconsistent detection rates. This study aimed to evaluat...
Retinal Vascularization Rate Predicts Retinopathy of Prematurity and Remains Unaffected by Low-Dose Bevacizumab Treatment.
American journal of ophthalmology
Mar 5, 2025
PURPOSE: To assess the rate of retinal vascularization derived from ultra-widefield (UWF) imaging-based retinopathy of prematurity (ROP) screening as predictor of type 1 ROP and characterize the effect of anti-vascular endothelial growth factor (anti...
Texture-based probability mapping for automatic assessment of myocardial injury in late gadolinium enhancement images after revascularized STEMI.
International journal of cardiology
Mar 2, 2025
BACKGROUND: Late Gadolinium-enhancement in cardiac magnetic resonance imaging (LGE-CMR) is the gold standard for assessing myocardial infarction (MI) size. Texture-based probability mapping (TPM) is a novel machine learning-based analysis of LGE imag...
Enhancing Personalized Chemotherapy for Ovarian Cancer: Integrating Gene Expression Data with Machine Learning.
Asian Pacific journal of cancer prevention : APJCP
Mar 1, 2025
OBJECTIVE: Ovarian cancer's complexity and heterogeneity pose significant challenges in treatment, often resulting in suboptimal chemotherapy outcomes. This study aimed to leverage machine learning algorithms, gene selection, and gene expression dat...
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