AIMC Topic: Retrospective Studies

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Machine Learning for In-hospital Mortality Prediction in Critically Ill Patients With Acute Heart Failure: A Retrospective Analysis Based on the MIMIC-IV Database.

Journal of cardiothoracic and vascular anesthesia Dec 16, 2024
BACKGROUND: The incidence, mortality, and readmission rates for acute heart failure (AHF) are high, and the in-hospital mortality for AHF patients in the intensive care unit (ICU) is higher. However, there is currently no method to accurately predict...
Critical Illness Intensive Care Units Female Aged Aged, 80 and over Humans Machine Learning Databases, Factual Retrospective Studies Heart Failure Male Middle Aged Hospital Mortality Predictive Value of Tests Acute Disease
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Noncontrast MRI-based machine learning and radiomics signature can predict the severity of primary lower limb lymphedema.

Journal of vascular surgery. Venous and lymphatic disorders Dec 16, 2024
OBJECTIVE: According to International Lymphology Society guidelines, the severity of lymphedema is determined by the difference in volume between the affected limb and the healthy side divided by the volume of the healthy side. However, this method o...
Severity of Illness Index Magnetic Resonance Imaging Lower Extremity Predictive Value of Tests Image Interpretation, Computer-Assisted Aged Lymphedema Humans Adult Machine Learning Radiomics Retrospective Studies Male Female Middle Aged
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Exploring phenotypes to improve long-term mortality risk stratification in obstructive sleep apnea through a machine learning approach: an observational cohort study.

European journal of internal medicine Dec 16, 2024
BACKGROUND: Obstructive sleep apnea (OSA) is a heterogeneous sleep disorder for which the identification of phenotypes might help for risk stratification for long-term mortality. Thus, the aim of the study was to identify distinct phenotypes of OSA a...
Sleep Apnea, Obstructive Hypoxia Female Proportional Hazards Models Middle Aged Continuous Positive Airway Pressure Risk Factors Risk Assessment Aged Cluster Analysis Humans Adult Obesity Machine Learning Male Phenotype Retrospective Studies
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Novel dilation-erosion labeling technique allows for rapid, accurate and adjustable alignment measurements in primary TKA.

Computers in biology and medicine Dec 16, 2024
BACKGROUND: Optimal implant position and alignment remains a controversial, yet critical topic in primary total knee arthroplasty (TKA). Future study of ideal implant position will require the ability to efficiently measure component positions at sca...
Neural Networks, Computer Machine Learning Algorithms Retrospective Studies Female Aged Male Tibia Middle Aged Knee Joint Femur Arthroplasty, Replacement, Knee Knee Prosthesis Humans
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Automated Segmentation of Graft Material in 1-Stage Sinus Lift Based on Artificial Intelligence: A Retrospective Study.

Clinical implant dentistry and related research Dec 16, 2024
OBJECTIVES: Accurate assessment of postoperative bone graft material changes after the 1-stage sinus lift is crucial for evaluating long-term implant survival. However, traditional manual labeling and segmentation of cone-beam computed tomography (CB...
Sinus Floor Augmentation Retrospective Studies Humans Cone-Beam Computed Tomography Artificial Intelligence Image Processing, Computer-Assisted Bone Transplantation Bone Substitutes
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Artificial intelligence-derived coronary artery calcium scoring saves time and achieves close to radiologist-level accuracy accuracy on routine ECG-gated CT.

The international journal of cardiovascular imaging Dec 16, 2024
Artificial Intelligence (AI) has been proposed to improve workflow for coronary artery calcium scoring (CACS), but simultaneous demonstration of improved efficiency, accuracy, and clinical stability have not been demonstrated. 148 sequential patients...
Observer Variation Coronary Vessels Multidetector Computed Tomography Vascular Calcification Computed Tomography Angiography Coronary Angiography Radiologists Radiographic Image Interpretation, Computer-Assisted Coronary Artery Disease Workflow Cardiac-Gated Imaging Techniques Retrospective Studies Female Reproducibility of Results Aged Male Middle Aged Severity of Illness Index Time Factors Electrocardiography Predictive Value of Tests Humans Artificial Intelligence
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Development of a machine learning model for prediction of intraventricular hemorrhage in premature neonates.

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery Dec 16, 2024
PURPOSE: Intraventricular hemorrhage (IVH) is a common and severe complication in premature neonates, leading to long-term neurological impairments. Early prediction and identification of risk factors for IVH in premature neonates are crucial for imp...
Female Infant, Newborn Risk Factors Male Cerebral Hemorrhage Humans Machine Learning Algorithms Retrospective Studies Infant, Premature Gestational Age Infant, Premature, Diseases Cerebral Intraventricular Hemorrhage
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Predicting molecular subtypes of breast cancer based on multi-parametric MRI dataset using deep learning method.

Magnetic resonance imaging Dec 14, 2024
PURPOSE: To develop a multi-parametric MRI model for the prediction of molecular subtypes of breast cancer using five types of breast cancer preoperative MRI images.
Image Interpretation, Computer-Assisted Receptor, ErbB-2 ROC Curve Humans Multiparametric Magnetic Resonance Imaging Contrast Media Retrospective Studies Magnetic Resonance Imaging Female Breast Aged Adult Sensitivity and Specificity Breast Neoplasms Middle Aged Deep Learning
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A Machine Learning Approach for Predicting In-Hospital Cardiac Arrest Using Single-Day Vital Signs, Laboratory Test Results, and International Classification of Disease-10 Block for Diagnosis.

Annals of laboratory medicine Dec 13, 2024
BACKGROUND: Predicting in-hospital cardiac arrest (IHCA) is crucial for potentially reducing mortality and improving patient outcomes. However, most models, which rely solely on vital signs, may not comprehensively capture the patients' risk profiles...
Aged Aged, 80 and over Machine Learning Adult Retrospective Studies ROC Curve Male Intensive Care Units Humans International Classification of Diseases Middle Aged Female Vital Signs Hospitalization Heart Arrest
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Individual risk assessment for rupture of abdominal aortic aneurysm using artificial intelligence.

Journal of vascular surgery Dec 13, 2024
OBJECTIVE: This study aimed to develop a prediction tool to identify abdominal aortic aneurysms (AAAs) at increased risk of rupture incorporating demographic, clinical, imaging, and medication data using artificial intelligence (AI).
Reproducibility of Results Risk Factors Aged Aged, 80 and over Male Artificial Intelligence Decision Support Techniques Prognosis Retrospective Studies Female Middle Aged Humans Predictive Value of Tests Risk Assessment Case-Control Studies Aortic Aneurysm, Abdominal Decision Trees Computed Tomography Angiography Denmark Aortography Aortic Rupture
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