AIMC Topic: Predictive Value of Tests

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AI-Driven segmentation and morphogeometric profiling of epicardial adipose tissue in type 2 diabetes.

Cardiovascular diabetology
BACKGROUND: Epicardial adipose tissue (EAT) is associated with cardiometabolic risk in type 2 diabetes (T2D), but its spatial distribution and structural alterations remain understudied. We aim to develop a shape-aware, AI-based method for automated ...

The application of super-resolution ultrasound radiomics models in predicting the failure of conservative treatment for ectopic pregnancy.

Reproductive biology and endocrinology : RB&E
BACKGROUND: Conservative treatment remains a viable option for selected patients with ectopic pregnancy (EP), but failure may lead to rupture and serious complications. Currently, serum β-hCG is the main predictor for treatment outcomes, yet its accu...

Predicting Morbidity and Mortality After Transjugular Intrahepatic Portosystemic Shunt Placement: A Review of Existing Models and Future Directions.

Techniques in vascular and interventional radiology
Transjugular intrahepatic portosystemic shunt (TIPS) is a key therapeutic intervention in the management of portal hypertension and its complications, such as variceal bleeding, hepatic hydrothorax, and refractory ascites. TIPS has historically been ...

A radiomics-clinical predictive model for difficult laparoscopic cholecystectomy based on preoperative CT imaging: a retrospective single center study.

World journal of emergency surgery : WJES
BACKGROUND: Accurately identifying difficult laparoscopic cholecystectomy (DLC) preoperatively remains a clinical challenge. Previous studies utilizing clinical variables or morphological imaging markers have demonstrated suboptimal predictive perfor...

Machine learning-based prediction of stone-free rate after retrograde intrarenal surgery for lower pole renal stones.

World journal of urology
BACKGROUND: Lower pole renal stones (LPS) present unique challenges for retrograde intrarenal surgery (RIRS) due to unfavorable anatomical features, often resulting in suboptimal stone-free rates (SFR). Recent advancements in machine learning (ML) of...

Clinical predictors of treatment resistant depression.

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology
Despite advances in the treatment of major depressive disorder (MDD) yet a substantial proportion of patients fail to achieve remission and instead develop treatment-resistant depression (TRD). Identifying robust clinical predictors of response is es...

Machine learning-based prognostic prediction for acute ischemic stroke using whole-brain and infarct multi-PLD ASL radiomics.

BMC medical imaging
INTRODUCTION: Accurate early prognostic prediction for acute ischemic stroke (AIS) is essential for guiding personalized treatment. This study aimed to assess the predictive value of radiomics features from whole-brain and infarct cerebral blood flow...

Prediction of three-year all-cause mortality in patients with heart failure and atrial fibrillation using the CatBoost model.

BMC cardiovascular disorders
BACKGROUND: Heart failure and atrial fibrillation (HF-AF) frequently coexist, resulting in complex interactions that substantially elevate mortality risk. This study aimed to develop and validate a machine learning (ML) model predicting the 3-year al...

Interpretable machine learning analysis of immunoinflammatory biomarkers for predicting CHD among NAFLD patients.

Cardiovascular diabetology
BACKGROUND: Coronary Heart Disease (CHD) and Non-Alcoholic Fatty Liver Disease (NAFLD) share overlapping pathogenic mechanisms including adipose tissue dysfunction, insulin resistance, and systemic inflammation mediated by adipokines. However, the sp...