AIMC Topic: Retrospective Studies

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Adaptive Breast MRI Scanning Using AI.

Radiology
Background MRI protocols typically involve many imaging sequences and often require too much time. Purpose To simulate artificial intelligence (AI)-directed stratified scanning for screening breast MRI with various triage thresholds and evaluate its ...

Detection of emergency department patients at risk of dementia through artificial intelligence.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: The study aimed to develop and validate the Emergency Department Dementia Algorithm (EDDA) to detect dementia among older adults (65+) and support clinical decision-making in the emergency department (ED).

Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia.

Critical care explorations
OBJECTIVES: Hospitalized community-acquired pneumonia (CAP) patients are admitted for ventilation, vasopressors, and renal replacement therapy (RRT). This study aimed to develop a machine learning (ML) model that predicts the need for such interventi...

Differences across various ideal lumbar lordosis measurement formulas for patient-specific sagittal alignment goals.

Neurosurgical focus
OBJECTIVE: Multiple studies in the past have developed equations to determine the ideal lumbar lordosis (ILL) in the sagittal plane. These equations differ but all look to accomplish the same goal of providing the surgeon with specific alignment targ...

Early outcomes with virtual surgical planning software and patient-specific instrumentation in adult spinal deformity.

Neurosurgical focus
OBJECTIVE: Software engineering innovations have led to the development of virtual surgical planning software (VSPS) for deformity correction. VSPS uses calibrated radiographs and machine learning predictive models to simulate postoperative spinopelv...

Optimizing predictive model performance in adult spinal deformity surgery: a comparative head-to-head analysis of learning models for perioperative complications.

Neurosurgical focus
OBJECTIVE: The aim of this study was to develop and compare 4 predictive algorithms, including logistic regression (LR), random forest (RF), gradient boosting machine (GBM), and neural network (NN), for perioperative outcomes in adult spinal deformit...

Machine-learning models to predict iron recovery after blood donation: a model development and external validation study.

The Lancet. Haematology
BACKGROUND: Machine-learning models directly predicting iron biomarkers after blood donation could help to manage donation-associated iron deficiency and avoid low haemoglobin deferrals. No such models have been externally validated internationally. ...

Using Machine Learning to Identify Predictors of Maternal and Infant Hair Cortisol Concentration Before and During the COVID-19 Pandemic.

Stress and health : journal of the International Society for the Investigation of Stress
Hair cortisol concentration (HCC) has been theorized to reflect chronic stress, and maternal and infant HCC may be correlated due to shared genetic, physiological, behavioural, and environmental factors, such as stressful life circumstances. The curr...

Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia.

Critical care explorations
OBJECTIVES: Hospitalized community-acquired pneumonia (CAP) patients are admitted for ventilation, vasopressors, and renal replacement therapy (RRT). This study aimed to develop a machine learning (ML) model that predicts the need for such interventi...