AIMC Topic: Retrospective Studies

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Preoperative plasma ceramide profiling coupled with machine learning accurately predicts recurrence of hepatocellular carcinoma after resection.

Lipids in health and disease
BACKGROUND: Accurate stratification of recurrence risk after curative resection remains a critical challenge in the management of hepatocellular carcinoma (HCC). Dysregulated ceramide (CER) metabolism has been implicated in HCC progression and relaps...

Mortality risk prediction in NSTE-ACS following PCI: Insights from a real-world cohort.

PloS one
BACKGROUND: Non-ST-segment elevation acute coronary syndrome (NSTE-ACS) is a major contributor to cardiovascular mortality, yet reliable tools for individualized mortality prediction remain limited. Machine learning offers the potential to enhance pr...

Evaluation of model performance in predicting sepsis after intestinal obstruction surgery: a multicenter retrospective study.

Annals of medicine
PURPOSE: Intestinal obstruction surgery is a high-risk procedure associated with postoperative sepsis. In this multicenter retrospective study, we aimed to employ machine-learning methods to predict sepsis after intestinal obstruction surgery and vis...

Predictive variables analysis for the tongue crib treatment of anterior crossbite in mixed dentition.

BMC oral health
OBJECTIVE: This study aimed to identify key prognostic variables and to develop and validate a clinical prediction model for pre-treatment assessment of tongue crib applicability.

A methodology for developing dermatological datasets: lessons from retrospective data collection for AI-based applications.

BMC medical research methodology
PURPOSE: The integration of artificial intelligence into dermatological research has underscored the need for robust and well-structured dermatological datasets. However, these datasets vary widely in their development processes, and there is current...

Deep learning-based automated detection of supernumerary teeth in pediatric panoramic radiographs.

PloS one
INTRODUCTION: Supernumerary teeth are a common developmental anomaly in pediatric patients, potentially leading to complications such as impaction, crowding, and delayed eruption. Accurate and early detection is critical to prevent these sequelae and...

Differentiation of light chain cardiac amyloidosis and hypertrophic cardiomyopathy by ensemble machine learning-based radiomic analysis of cardiac magnetic resonance.

Orphanet journal of rare diseases
BACKGROUND: We aim to assess the diagnosis performance of an ensemble machine learning (ML) based radiomic analysis of multiparametric cardiac magnetic resonance (CMR) to differentiate light chain cardiac amyloidosis (AL-CA) and hypertrophic cardiomy...

Optimizing myocardial infarction detection: a hybrid CNN-GRU deep learning approach.

BMC medical informatics and decision making
BACKGROUND: Myocardial infarction (MI) is a life-threatening condition caused by sudden interruption of blood supply to the heart. Electrocardiogram (ECG) is the primary tool for MI diagnosis, but interpretation challenges exist. This study aimed to ...

Development and validation of a multidimensional and interpretable artificial intelligence model to predict gout recurrence in hospitalised patients: a real-world, ambispective multicentre cohort study in China.

BMC medicine
BACKGROUND: Gout is the most common inflammatory arthritis. Recurrent flares are common among hospitalised patients and contribute to substantial clinical and economic burden. However, the accurate prediction of inpatient recurrence remains challengi...