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Typical Atrial Flutter: A Practical Review.
J Cardiovasc Electrophysiol
January 2025
Cardiology Division, Geneva University Hospitals, Geneva, Switzerland.
Typical atrial flutter (AFL), defined as cavotricuspid isthmus (CTI)-dependent macro-re-entrant atrial tachycardia, often causes debilitating symptoms, and is associated with increased incidence of atrial fibrillation, stroke, heart failure, and death. Typical AFL occurs in patients with atrial remodeling and shares risk factors with atrial fibrillation. It is also common in patients with a history of prior heart surgery or catheter ablation.
View Article and Find Full Text PDFAnalysis of goal, feedback and rewards on sustained attention via machine learning.
Front Behav Neurosci
December 2024
Department of Mathematics, University of Texas at Arlington, Arlington, TX, United States.
Introduction: Sustaining attention is a notoriously difficult task as shown in a recent experiment where reaction times (RTs) and pupillometry data were recorded from 350 subjects in a 30-min vigilance task. Subjects were also presented with different types of goal, feedback, and reward.
Methods: In this study, we revisit this experimental data and solve three families of machine learning problems: (i) RT-regression problems, to predict subjects' RTs using all available data, (ii) RT-classification problems, to classify responses more broadly as attentive, semi-attentive, and inattentive, and (iii) to predict the subjects' experimental conditions from physiological data.
Two-Step Transfer Learning Improves Deep Learning-Based Drug Response Prediction in Small Datasets: A Case Study of Glioblastoma.
Bioinform Biol Insights
January 2025
Department of Pathology & Clinical Bioinformatics, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.
While deep learning (DL) is used in patients' outcome predictions, the insufficiency of patient samples limits the accuracy. In this study, we investigated how transfer learning (TL) alleviates the small sample size problem. A 2-step TL framework was constructed for a difficult task: predicting the response of the drug temozolomide (TMZ) in glioblastoma (GBM) cell cultures.
View Article and Find Full Text PDFArtificial Intelligence to Predict Chronic Kidney Disease Progression to Kidney Failure: A Narrative Review.
Nephrology (Carlton)
January 2025
Faculty of Medicine, Dentistry & Health Sciences Melbourne, The University of Melbourne, Melbourne, Victoria, Australia.
Chronic kidney disease is characterised by the progressive loss of kidney function. However, predicting who will progress to kidney failure is difficult. Artificial Intelligence, including Machine Learning, shows promise in this area.
View Article and Find Full Text PDFBackground: Stone impaction is an obstacle to successful laparoscopic common bile duct exploration (LCBDE). This study aims to identify the incidence, operative difficulties and techniques used to disimpact and remove impacted stones during LCBDE.
Methods: Prospectively collected data from a large series of LCBDE.
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