Accurate segmentation of retinal blood vessels from retinal images is crucial for detecting and diagnosing a wide range of ophthalmic diseases. Our retinal blood vessel segmentation algorithm enhances microfine vessel extraction, improves edge textur...
We developed a deep learning-based extraction of electrocardiographic (ECG) waves from ballistocardiographic (BCG) signals and explored their use in R-R interval (RRI) estimation. Preprocessed BCG and reference ECG signals were inputted into the bidi...
Objective Endometrial lesions are a frequent complication following breast cancer, and current diagnostic tools have limitations. This study aims to develop a machine learning-based nomogram model for predicting the early detection of endometrial les...
Model optimization is a problem of great concern and challenge for developing an image classification model. In image classification, selecting the appropriate hyperparameters can substantially boost the model's ability to learn intricate patterns an...
Alzheimer's disease (AD), a progressive neurodegenerative condition, notably impacts cognitive functions and daily activity. One method of detecting dementia involves a task where participants describe a given picture, and extensive research has been...
Diabetes is a long-term condition characterized by elevated blood sugar levels. It can lead to a variety of complex disorders such as stroke, renal failure, and heart attack. Diabetes requires the most machine learning help to diagnose diabetes illne...
OBJECTIVE: Artificial intelligence (AI) tools for histological diagnosis offer great potential to healthcare, yet failure to understand their clinical context is delaying adoption. IGUANA (Interpretable Gland-Graphs using a Neural Aggregator) is an A...
BACKGROUND: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces ...
BACKGROUND: The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to th...
OBJECTIVES: Approximal caries diagnosis in children is difficult, and artificial intelligence-based research in pediatric dentistry is scarce. To create a convolutional neural network (CNN)-based diagnostic system for the prompt and efficient identif...
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