AIMC Topic: Predictive Value of Tests

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Deep learning-based 3D quantitative total tumor burden predicts early recurrence of BCLC A and B HCC after resection.

European radiology Jul 19, 2024
OBJECTIVES: This study aimed to evaluate the potential of deep learning (DL)-assisted automated three-dimensional quantitative tumor burden at MRI to predict postoperative early recurrence (ER) of hepatocellular carcinoma (HCC).
Tumor Burden Hepatectomy Humans Prognosis Retrospective Studies Female Aged Imaging, Three-Dimensional Contrast Media Male Magnetic Resonance Imaging Middle Aged Predictive Value of Tests Deep Learning Neoplasm Recurrence, Local Liver Neoplasms Carcinoma, Hepatocellular
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Performance of a Machine Learning Algorithm on Lesions with a High Preoperative Suspicion of Invasive Melanoma.

Acta dermato-venereologica Jul 18, 2024
Male Middle Aged Neoplasm Invasiveness Predictive Value of Tests Retrospective Studies Female Melanoma Skin Neoplasms Aged Humans Machine Learning Algorithms
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Prediction of Left Ventricle Pressure Indices Via a Machine Learning Approach Combining ECG, Pulse Oximetry, and Cardiac Sounds: a Preclinical Feasibility Study.

Journal of cardiovascular translational research Jul 17, 2024
Heart failure (HF) is defined as the inability of the heart to meet body oxygen demand requiring an elevation in left ventricular filling pressures (LVP) to compensate. LVP increase can be assessed in the cardiac catheterization laboratory, but this ...
Machine Learning Animals Ventricular Function, Left Stroke Volume Swine Electrocardiography Ventricular Pressure Disease Models, Animal Heart Rate Predictive Value of Tests Oximetry Signal Processing, Computer-Assisted Swine, Miniature Feasibility Studies Heart Sounds Heart Failure Phonocardiography
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Identifying high-risk Fontan phenotypes using K-means clustering of cardiac magnetic resonance-based dyssynchrony metrics.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance Jul 14, 2024
BACKGROUND: Individuals with a Fontan circulation encompass a heterogeneous group with adverse outcomes linked to ventricular dilation, dysfunction, and dyssynchrony. The purpose of this study was to assess if unsupervised machine learning cluster an...
Heart Transplantation Fontan Procedure Heart Defects, Congenital Magnetic Resonance Imaging, Cine Myocardial Contraction Risk Assessment Young Adult Adolescent Adult Humans Time Factors Biomechanical Phenomena Phenotype Cluster Analysis Child Male Retrospective Studies Unsupervised Machine Learning Treatment Outcome Ventricular Function, Left Female Predictive Value of Tests Risk Factors Ventricular Dysfunction, Left Image Interpretation, Computer-Assisted
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State-of-the-art sleep arousal detection evaluated on a comprehensive clinical dataset.

Scientific reports Jul 14, 2024
Aiming to apply automatic arousal detection to support sleep laboratories, we evaluated an optimized, state-of-the-art approach using data from daily work in our university hospital sleep laboratory. Therefore, a machine learning algorithm was traine...
Polysomnography Sleep Wake Disorders Arousal Predictive Value of Tests Area Under Curve Humans Sleep Machine Learning Datasets as Topic Female Statistics, Nonparametric Aged Male Aged, 80 and over Middle Aged Young Adult Adolescent Adult
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Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer.

Scientific reports Jul 13, 2024
To retrospectively assess the effectiveness of deep learning (DL) model, based on breast magnetic resonance imaging (MRI), in predicting preoperative lymphovascular invasion (LVI) status in patients diagnosed with invasive breast cancer who have nega...
Predictive Value of Tests Female Aged Adult Humans Breast Neoplasms Retrospective Studies Middle Aged Deep Learning Lymph Nodes Lymphatic Metastasis Magnetic Resonance Imaging Neoplasm Invasiveness
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Predictors of left atrial appendage thrombus in atrial fibrillation patients undergoing cardioversion.

Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing Jul 12, 2024
BACKGROUND: Atrial fibrillation and atrial flutter represent the most prevalent clinically significant cardiac arrhythmias. While the CHA2DS2-VASc score is commonly used to inform anticoagulation therapy decisions for patients with these conditions, ...
Humans Machine Learning Algorithms Electric Countershock Echocardiography, Transesophageal Atrial Appendage Atrial Flutter Middle Aged Retrospective Studies Risk Assessment Female Predictive Value of Tests Risk Factors Thrombosis Aged Atrial Fibrillation Male
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2-Dimensional Echocardiographic Global Longitudinal Strain With Artificial Intelligence Using Open Data From a UK-Wide Collaborative.

JACC. Cardiovascular imaging Jul 10, 2024
BACKGROUND: Global longitudinal strain (GLS) is reported to be more reproducible and prognostic than ejection fraction. Automated, transparent methods may increase trust and uptake.
Biomechanical Phenomena Ventricular Function, Left Echocardiography Consensus Predictive Value of Tests Humans Neural Networks, Computer Machine Learning Reproducibility of Results Myocardial Contraction Global Longitudinal Strain Ventricular Dysfunction, Left Image Interpretation, Computer-Assisted Datasets as Topic Observer Variation United Kingdom
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Detection of Endoleak after Endovascular Aortic Repair through Deep Learning Based on Non-contrast CT.

Cardiovascular and interventional radiology Jul 8, 2024
OBJECTIVES: To develop and validate a deep learning model for detecting post-endovascular aortic repair (EVAR) endoleak from non-contrast CT.
Computed Tomography Angiography Endoleak Endovascular Aneurysm Repair Deep Learning Predictive Value of Tests Aortic Aneurysm, Abdominal Blood Vessel Prosthesis Implantation Endovascular Procedures Humans Retrospective Studies Female Aged Aged, 80 and over Male
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Machine learning models to predict systemic inflammatory response syndrome after percutaneous nephrolithotomy.

BMC urology Jul 8, 2024
OBJECTIVE: The objective of this study was to develop and evaluate the performance of machine learning models for predicting the possibility of systemic inflammatory response syndrome (SIRS) following percutaneous nephrolithotomy (PCNL).
Humans Machine Learning Middle Aged Postoperative Complications Systemic Inflammatory Response Syndrome Predictive Value of Tests Kidney Calculi Nephrolithotomy, Percutaneous Retrospective Studies Female Aged Adult Male
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