Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 11,471 to 11,480 of 209,934 articles

A Deep Bidirectional LSTM Model Enhanced by Transfer-Learning for the Classification of Peripheral Arterial Blood Pressure Waveforms.

IEEE transactions on bio-medical engineering May 25, 2026
OBJECTIVE: Arterial blood pressure waveform (BPW) morphology provides critical insight into cardiovascular status and can serve as an early marker of pathological changes, particularly in critically ill patients where waveform alterations may occur o... read more 

TVRN: Invertible Neural Networks for Compression-Aware Temporal Video Rescaling.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society May 25, 2026
To fit diverse display and bandwidth constraints, high-frame-rate videos are temporally downscaled to low-frame-rate (LFR) and later upscaled, requiring joint optimization for effective frame-rate rescaling. However, existing methods typically link t... read more 

DSA-NRP: No-Reflow Prediction from Angiographic Perfusion Dynamics in Stroke EVT.

IEEE transactions on medical imaging May 25, 2026
Following successful large-vessel recanalization via endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), some patients experience a complication known as no-reflow, defined by persistent microvascular hypoperfusion that undermines tissue... read more 

Learning From Crowds With Multiple Feature Dynamic Fusion-Based Annotation Generation.

IEEE transactions on pattern analysis and machine intelligence May 25, 2026
As the scale of data grows for machine learning, annotating data accurately is extremely time-consuming and with high economic costs. To alleviate this dilemma, crowdsourcing has been widely used for data collection and annotation. Learning from crow... read more 

CTransFuse: A Hybrid Transformer-CNN Framework for Precise Meniscus Segmentation and Lesion Identification.

IEEE journal of biomedical and health informatics May 25, 2026
Meniscal tears and degenerative changes are the most common pathologies affecting the knee joint. In magnetic resonance imaging (MRI), these lesions often manifest at small spatial scales with indistinct boundaries and heterogeneous intensity pattern... read more 

Consistency-based Semi-supervised Evidential Active Learning Framework for Robust Classification of Radiology Images.

IEEE journal of biomedical and health informatics May 25, 2026
Deep learning offers high performance for radiology image classification, but relies on large, expert annotated datasets. Semi-supervised learning and active learning approaches can leverage unlabelled samples and mitigate the annotation burden. Comb... read more 

TwinRL-Onco: A World Model-Empowered Digital Twin Framework with Hierarchical Reinforcement Learning for Venetoclax Resistance Trajectory Prediction and Adaptive Therapy Optimization in Chronic Lymphocytic Leukemia.

IEEE journal of biomedical and health informatics May 25, 2026
Chronic lymphocytic leukaemia (CLL) presents considerable therapeutic obstacles due to the development of treatment-resistant disease, especially with BCL-2 inhibitors like venetoclax, and is among the most challenging haematological malignancies to ... read more 

User-Guided Visual Analytics of Genome-wide DNA Methylation Data Based on Self-Organizing Maps.

IEEE transactions on computational biology and bioinformatics May 25, 2026
DNA methylation is a key epigenetic modification with diagnostic and prognostic relevance across a wide range of diseases, particularly cancer. Modern array-based technologies enable high-throughput quantification of methylation states at hundreds of... read more 

BiasField: Interactive Bias Probing of Machine Learning Datasets.

IEEE transactions on visualization and computer graphics May 25, 2026
Bias in machine learning datasets occurs when certain attributes are unfairly associated, e.g., serious males being mostly linked with law enforcement officers in job-related image datasets. Training models on biased datasets will degrade model perfo... read more 

Ethics and Fairness Considerations in AI-Based Deception Detection Technologies for Mental Health Applications: Focus Group Study.

JMIR AI May 25, 2026
BACKGROUND: Artificial intelligence (AI) technologies are increasingly being integrated into mental health settings to support tasks such as clinical documentation and decision-making. In parallel, AI-enabled deception detection, which leverages mult... read more 
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