AIMC Topic: Databases, Factual

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Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair.

Journal of the American Heart Association
BACKGROUND: Thoracic endovascular aortic repair (TEVAR) and complex endovascular aneurysm repair (EVAR) are complex procedures that carry a significant risk of complications. While risk prediction tools can aid in clinical decision making, they remai...

MLFusion: Multilevel Data Fusion using CNNs for atrial fibrillation detection.

Computers in biology and medicine
Data fusion, involving the simultaneous integration of signals from multiple sensors, is an emerging field that facilitates more accurate inferences in instrumentation applications. This paper presents a novel fusion methodology for multi-sensor mult...

A Generative Shape Compositional Framework to Synthesize Populations of Virtual Chimeras.

IEEE transactions on neural networks and learning systems
Generating virtual organ populations that capture sufficient variability while remaining plausible is essential to conduct in silico trials (ISTs) of medical devices. However, not all anatomical shapes of interest are always available for each indivi...

Clinical Application of Artificial Intelligence Preoperative Planning System Combined with Expert Database Retrieval in Complex Revision Hip Surgery.

Journal of visualized experiments : JoVE
Accurate preoperative planning in revision hip arthroplasty is crucial for achieving successful outcomes. To enhance the intuitive evaluation of acetabular bone defect severity and leverage previous successful experience in revision hip arthroplasty,...

Enhancing atrial fibrillation detection in PPG analysis with sparse labels through contrastive learning.

Computer methods and programs in biomedicine
BACKGROUND: With the advancements in wearable technology, photoplethysmography (PPG) has emerged as a promising technique for detecting atrial fibrillation (AF) due to its ability to capture cardiovascular information. However, current deep learning-...

Investigation of Inter-Patient, Intra-Patient, and Patient-Specific Based Training in Deep Learning for Classification of Heartbeat Arrhythmia.

Cardiovascular engineering and technology
Effective diagnosis of electrocardiogram (ECG) is one of the simplest and fastest ways to assess the heart's function. In the recent decade, various attempts have been made to automate the classification of electrocardiogram signals to detect heartbe...

Enhancing biomedical named entity recognition with parallel boundary detection and category classification.

BMC bioinformatics
BACKGROUND: Named entity recognition is a fundamental task in natural language processing. Recognizing entities in biomedical text, known as the BioNER, is particularly crucial for cutting-edge applications. However, BioNER poses greater challenges c...