AIMC Topic: Deep Learning
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Applying multimodal AI to physiological waveforms improves genetic prediction of cardiovascular traits.
American journal of human genetics
Jun 20, 2025
Electronic health records, biobanks, and wearable biosensors enable the collection of multiple health modalities from many individuals. Access to multimodal health data provides a unique opportunity for genetic studies of complex traits because diffe...
3D cardiac shape analysis with variational point cloud autoencoders for myocardial infarction prediction and virtual heart synthesis.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 19, 2025
Cardiac anatomy and physiology vary considerably across the human population. Understanding and taking into account this variability is crucial for both accurate clinical decision-making and realistic in silico modeling of cardiac function. In this w...
Expert-level differentiation of incomplete Kawasaki disease and pneumonia from echocardiography via multiple large receptive attention mechanisms.
Computers in biology and medicine
Jun 19, 2025
BACKGROUND: Incomplete Kawasaki disease (KD) is challenging to diagnose due to its lack of classic clinical features, yet it has a higher incidence of coronary artery lesions, making early detection crucial. Echocardiography plays a vital role in ide...
Deep learning detects retropharyngeal edema on MRI in patients with acute neck infections.
European radiology experimental
Jun 19, 2025
BACKGROUND: In acute neck infections, magnetic resonance imaging (MRI) shows retropharyngeal edema (RPE), which is a prognostic imaging biomarker for a severe course of illness. This study aimed to develop a deep learning-based algorithm for the auto...
Deep generative models for Bayesian inference on high-rate sensor data: applications in automotive radar and medical imaging.
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Jun 19, 2025
Deep generative models (DGMs) have been studied and developed primarily in the context of natural images and computer vision. This has spurred the development of (Bayesian) methods that use these generative models for inverse problems in image restor...
Deep learning for differential diagnosis of parotid tumors based on 2.5D magnetic resonance imaging.
Annals of medicine
Jun 18, 2025
PURPOSE: Accurate preoperative diagnosis of parotid gland tumors (PGTs) is crucial for surgical planning since malignant tumors require more extensive excision. Though fine-needle aspiration biopsy is the diagnostic gold standard, its sensitivity in ...
Your turn: At home turning angle estimation for Parkinson's disease severity assessment.
Artificial intelligence in medicine
Jun 18, 2025
People with Parkinson's Disease (PD) often experience progressively worsening gait, including changes in how they turn around, as the disease progresses. Existing clinical rating tools are not capable of capturing hour-by-hour variations of PD sympto...
Research on the potential of the deep learning-based "decomposition-optimization-reconstruction" method in runoff prediction for typical climate- and human-regulated basins in northern China.
Journal of contaminant hydrology
Jun 18, 2025
River runoff may be affected mainly by the natural climate or human activities, and runoff series present complex characteristics, such as non-stationarity, which makes accurate prediction of runoff challenging. To address the problem that the predic...
Deep learning model using CT images for longitudinal prediction of benign and malignant ground-glass nodules.
European journal of radiology
Jun 18, 2025
OBJECTIVES: To develop and validate a CT image-based multiple time-series deep learning model for the longitudinal prediction of benign and malignant pulmonary ground-glass nodules (GGNs).
Multimodal deep learning for predicting unsuccessful recanalization in refractory large vessel occlusion.
European journal of radiology
Jun 18, 2025
PURPOSE: This study explores a multi-modal deep learning approach that integrates pre-intervention neuroimaging and clinical data to predict endovascular therapy (EVT) outcomes in acute ischemic stroke patients. To this end, consecutive stroke patien...
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