Acute Coronary Syndrome

Deep learning-based classification of adequate sonographic images for self-diagnosing deep vein thrombosis.

BACKGROUND: Pulmonary thromboembolism is a serious disease that often occurs in disaster victims evacuated to shelters. Deep vein thrombosis is the most common reason for pulmonary thromboembolism, and early prevention is important. Medical technicians often perform ultrasonography as part of mobile medical screenings of disaster victims but reaching all isolated and scattered shelters is difficul...

Deep learning augmented ECG analysis to identify biomarker-defined myocardial injury.

Chest pain is a common clinical complaint for which myocardial injury is the primary concern and is associated with significant morbidity and mortality. To aid providers' decision-making, we aimed to analyze the electrocardiogram (ECG) using a deep convolutional neural network (CNN) to predict serum troponin I (TnI) from ECGs. We developed a CNN using 64,728 ECGs from 32,479 patients who underwent...

Do artificial neural networks love sex? How the combination of artificial neural networks with evolutionary algorithms may help to identify gender influence in rheumatic diseases.

Although medical research has been performed predominantly on men both in preclinical and clinical studies, continuous efforts have been made to overc...

Deep Learning Analysis of Chest Radiographs to Triage Patients with Acute Chest Pain Syndrome.

Background Patients presenting to the emergency department (ED) with acute chest pain (ACP) syndrome undergo additional testing to exclude acute coron...

Identification of Coronary Culprit Lesion in ST Elevation Myocardial Infarction by Using Deep Learning.

OBJECTIVE: Early revascularization of the occluded coronary artery in patients with ST elevation myocardial infarction (STEMI) has been demonstrated t...

The clinical feasibility of artificial intelligence-assisted compressed sensing single-shot fluid-attenuated inversion recovery (ACS-SS-FLAIR) for evaluation of uncooperative patients with brain diseases: comparison with the conventional T2-FLAIR with parallel imaging.

BACKGROUND: Satisfactory magnetic resonance imaging (MRI) of those patients with involuntary head motion due to brain diseases is essential in avoidin...

Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients.

Myocardial infarction diagnosis is a common challenge in the emergency department. In managed settings, deep learning-based models and especially conv...

Kenichi Harumi Plenary Address at Annual Meeting of the International Society of Computers in Electrocardiology: "What Should ECG Deep Learning Focus on? The diagnosis of acute coronary occlusion!".

According to the STEMI paradigm, only patients whose ECGs meet STEMI criteria require immediate reperfusion. This leads to reperfusion delays and sign...

How do I/we forecast tomorrow's transfusion? A focus on recipients' profiles.

Red blood cell (RBC) transfusion is a life-saving medical intervention and has an essential role in the management of surgical patients. However, bloo...

A machine learning approach to predicting early and late postoperative reintubation.

Accurate estimation of surgical risks is important for informing the process of shared decision making and informed consent. Postoperative reintubatio...

Development and validation of a predictive model for peripherally inserted central catheter-related thrombosis in breast cancer patients based on artificial neural network: A prospective cohort study.

BACKGROUND: Peripherally inserted central catheters have been extensively applied in clinical practices. However, they are associated with an increase...

Artificial intelligence fully automated myocardial strain quantification for risk stratification following acute myocardial infarction.

Feasibility of automated volume-derived cardiac functional evaluation has successfully been demonstrated using cardiovascular magnetic resonance (CMR)...

Application of deep neural network and gamma-ray scattering in eccentric scale calculation regardless of the fluids volume fraction inside a pipeline.

Scale formation is one of the major problems in the oil industry as it can accumulate on the surface of the pipelines, which could even fully block th...

Intelligent and novel multi-type cancer prediction model using optimized ensemble learning.

Cancer is known to be highly severe disease and gets incurable even when the treatment has started at the time of diagnosis owing to the occurrence of...

RETRACTED: Chemistry-Informed Neural Networks modelling of lignocellulosic biomass pyrolysis.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has ...

Machine learning model prediction of 6-month functional outcome in elderly patients with intracerebral hemorrhage.

Spontaneous intracerebral hemorrhage (ICH) has an increasing incidence and a worse outcome in elderly patients. The ability to predict the functional ...

Exploiting exercise electrocardiography to improve early diagnosis of atrial fibrillation with deep learning neural networks.

Atrial fibrillation (AF) is the most common type of sustained arrhythmia. It results from abnormal irregularities in the electrical performance of the...

Combining mathematical modeling and deep learning to make rapid and explainable predictions of the patient-specific response to anticoagulant therapy under venous flow.

Anticoagulant drugs are commonly prescribed to prevent hypercoagulable states in patients with venous thromboembolism. The choice of the most efficien...

Effect of an artificial intelligence-assisted tool on non-valvular atrial fibrillation anticoagulation management in primary care: protocol for a cluster randomized controlled trial.

BACKGROUND: Atrial fibrillation (AF) is one of the most common cardiac arrhythmia diseases. Thromboembolic prophylaxis plays an essential role in AF t...

Fully automated platelet differential interference contrast image analysis via deep learning.

Platelets mediate arterial thrombosis, a leading cause of myocardial infarction and stroke. During injury, platelets adhere and spread over exposed su...