Congestive Heart Failure

The Effectiveness of a Deep Learning Model to Detect Left Ventricular Systolic Dysfunction from Electrocardiograms.

Deep learning models can be applied to electrocardiograms (ECGs) to detect left ventricular (LV) dysfunction. We hypothesized that applying a deep learning model may improve the diagnostic accuracy of cardiologists in predicting LV dysfunction from ECGs. We acquired 37,103 paired ECG and echocardiography data records of patients who underwent echocardiography between January 2015 and December 2019...

End-stage renal disease at dialysis initiation: Epidemiology and mortality risks during the first year of hemodialysis.

Chronic kidney disease (CKD) treated by hemodialysis (HD) is a worldwide major public health problem. Its incidence is getting higher and higher, leading to an alarming social and economic impact. The survival of these patients is significantly low, especially during the first year of treatment. The purpose of our study was to identify the epidemiological and clinico-biological characteristics of ...

Smartphone-based diabetic macula edema screening with an offline artificial intelligence.

BACKGROUND: Diabetic macular edema (DME) is a sight-threatening condition that needs regular examinations and remedies. Optical coherence tomography (...

A Case of Dapsone-induced Mild Methemoglobinemia with Dyspnea and Cyanosis.

Dear Editor, Dapsone is a dual-function drug with antimicrobial and antiprotozoal effects and anti-inflammatory features (1). In dermatology, it is a ...

Machine learning based congestive heart failure detection using feature importance ranking of multimodal features.

In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized ...

Optical coherence tomography-based diabetic macula edema screening with artificial intelligence.

BACKGROUND: Optical coherence tomography (OCT) is considered as a sensitive and noninvasive tool to evaluate the macular lesions. In patients with dia...

A novelty route for smartphone-based artificial intelligence approach to ophthalmic screening.

Artificial intelligence (AI) has been widely applied in the medical field and achieved enormous milestones in helping specialists to make diagnosis an...

Measuring Anatomical Outcomes of Anti-Vascular Endothelial Growth Factor Treatment of Diabetic Macular Edema With Artificial Intelligence-A Step Toward Individualized Medicine.

Quantification of Fluid Resolution and Visual Acuity Gain in Patients With Diabetic Macular Edema Using Deep Learning: A Post Hoc Analysis of a Randomized Clinical Trial.

IMPORTANCE: Large amounts of optical coherence tomographic (OCT) data of diabetic macular edema (DME) are acquired, but many morphologic features have...

Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features.

BACKGROUND: Left ventricular (LV) diastolic dysfunction is recognized as playing a major role in the pathophysiology of heart failure; however, clinic...

Constructing Inpatient Pressure Injury Prediction Models Using Machine Learning Techniques.

The incidence rate of pressure injury is a critical nursing quality indicator in clinic care; consequently, factors causing pressure injury are divers...

A Brain Tumor Segmentation Framework Based on Outlier Detection Using One-Class Support Vector Machine.

Accurate segmentation of brain tumors is a challenging task and also a crucial step in diagnosis and treatment planning for cancer patients. Magnetic ...

A Preliminary Study of Predicting Effectiveness of Anti-VEGF Injection Using OCT Images Based on Deep Learning.

Deep learning based radiomics have made great progress such as CNN based diagnosis and U-Net based segmentation. However, the prediction of drug effec...

Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is an uncommon but important cause of sudden cardiac death.

A Machine Learning-Based Model to Predict Acute Traumatic Coagulopathy in Trauma Patients Upon Emergency Hospitalization.

Acute traumatic coagulopathy (ATC) is an extremely common but silent murderer; this condition presents early after trauma and impacts approximately 30...

Diastolic Function Evaluation: What Can We Learn From Machine Learning?

Evaluating the Effect of Dexmedetomidine on Hemodynamic Status of Patients with Septic Shock Admitted to Intensive Care Unit: A Single-Blind Randomized Controlled Trial.

Septic shock, known as the most severe complication of sepsis, is a serious medical condition that can lead to death. Clinical symptoms of sepsis incl...

OPTICAL COHERENCE TOMOGRAPHY BIOMARKERS TO DISTINGUISH DIABETIC MACULAR EDEMA FROM PSEUDOPHAKIC CYSTOID MACULAR EDEMA USING MACHINE LEARNING ALGORITHMS.

PURPOSE: In diabetic patients presenting with macular edema (ME) shortly after cataract surgery, identifying the underlying pathology can be challengi...

Heterogeneity of perception of symptoms in patients with asthma.

BACKGROUND: Cough-dominant or cough-variant asthma is common in Japan. However, it is unclear whether cough and dyspnea, the cardinal symptoms of bron...

Predicting post-experiment fatigue among healthy young adults: Random forest regression analysis.

The current study utilized a random forest regression analysis to predict post-experiment fatigue in a sample of 212 healthy participants (mean age = ...