AIMC Topic: Deep Learning

Clear Filters Showing 5931 to 5940 of 28423 articles

Two-step deep learning models for detection and identification of the manufacturers and types of dental implants on panoramic radiographs.

Odontology
The purpose of this study is to develop two-step deep learning models that can automatically detect implant regions on panoramic radiographs and identify several types of implants. A total of 1,574 panoramic radiographs containing 3675 implants were ...

Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives.

Ophthalmology. Glaucoma
CLINICAL RELEVANCE: Glaucoma is a complex eye condition with varied morphological and clinical presentations, making diagnosis and management challenging. The lack of a consensus definition for glaucoma or glaucomatous optic neuropathy further compli...

Deep learning to predict risk of lateral skull base cerebrospinal fluid leak or encephalocele.

International journal of computer assisted radiology and surgery
PURPOSE: Skull base features, including increased foramen ovale (FO) cross-sectional area, are associated with lateral skull base spontaneous cerebrospinal fluid (sCSF) leak and encephalocele. Manual measurement requires skill in interpreting imaging...

Comparative study of machine-and deep-learning based classification algorithms for biomedical Raman spectroscopy (RS): case study of RS based pathogenic microbe identification.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry
One key aspect pushing the frontiers of biomedical RS is dedicated machine- or deep- learning (ML or DL) algorithms. Yet, systematic comparative study between ML and DL algorithms has not been conducted for biomedical RS, largely due to the limited a...

Deep-learning-based image reconstruction with limited data: generating synthetic raw data using deep learning.

Magma (New York, N.Y.)
OBJECT: Deep learning has shown great promise for fast reconstruction of accelerated MRI acquisitions by learning from large amounts of raw data. However, raw data is not always available in sufficient quantities. This study investigates synthetic da...

Deciphering the Language of Protein-DNA Interactions: A Deep Learning Approach Combining Contextual Embeddings and Multi-Scale Sequence Modeling.

Journal of molecular biology
Deciphering the mechanisms governing protein-DNA interactions is crucial for understanding key cellular processes and disease pathways. In this work, we present a powerful deep learning approach that significantly advances the computational predictio...

A deep learning approach for cervical cord injury severity determination through axial and sagittal magnetic resonance imaging segmentation and classification.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
STUDY DESIGN: Cross-sectional Database Study.

Economical hybrid novelty detection leveraging global aleatoric semantic uncertainty for enhanced MRI-based ACL tear diagnosis.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
This study presents an innovative hybrid deep learning (DL) framework that reformulates the sagittal MRI-based anterior cruciate ligament (ACL) tear classification task as a novelty detection problem to tackle class imbalance. We introduce a highly d...

Challenges for augmenting intelligence in cardiac imaging.

The Lancet. Digital health
Artificial Intelligence (AI), through deep learning, has brought automation and predictive capabilities to cardiac imaging. However, despite considerable investment, tangible health-care cost reductions remain unproven. Although AI holds promise, the...

From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only ta...