AIMC Topic: Deep Learning

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Deep-Learning Model Prediction of Radiation Pneumonitis Using Pretreatment Chest Computed Tomography and Clinical Factors.

Technology in cancer research & treatment
This study aimed to build a comprehensive deep-learning model for the prediction of radiation pneumonitis using chest computed tomography (CT), clinical, dosimetric, and laboratory data. Radiation therapy is an effective tool for treating patients ...

Deep Trans-Omic Network Fusion for Molecular Mechanism of Alzheimer's Disease.

Journal of Alzheimer's disease : JAD
BACKGROUND: There are various molecular hypotheses regarding Alzheimer's disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and synaptic dysfunction. However, detailed molecular mechanism underlying AD remains elusive. In addit...

Application of Deep Learning for Studying NMDA Receptors.

Methods in molecular biology (Clifton, N.J.)
Artificial intelligence underwent remarkable advancement in the past decade, revolutionizing our way of thinking and unlocking unprecedented opportunities across various fields, including drug development. The emergence of large pretrained models, su...

A Systematic Review on Deep Learning Model in Computer-aided Diagnosis for Anterior Cruciate Ligament Injury.

Current medical imaging
INTRODUCTION: In developing Computer-Aided Diagnosis (CAD), a Convolutional Neural Network (CNN) has been commonly used as a Deep Learning (DL) model. Although it is still early, DL has excellent potential in implementing computers in medical diagnos...

An Artificial Intelligence Driven Approach for Classification of Ophthalmic Images using Convolutional Neural Network: An Experimental Study.

Current medical imaging
BACKGROUND: Early disease detection is emphasized within ophthalmology now more than ever, and as a result, clinicians and innovators turn to deep learning to expedite accurate diagnosis and mitigate treatment delay. Efforts concentrate on the creati...

Automatic Optic Nerve Assessment From Transorbital Ultrasound Images: A Deep Learning-based Approach.

Current medical imaging
BACKGROUND: Transorbital Ultrasonography (TOS) is a promising imaging technology that can be used to characterize the structures of the optic nerve and the potential alterations that may occur in those structures as a result of an increase in intracr...

Deep Learning-Based Cell Tracking in Deforming Organs and Moving Animals.

Methods in molecular biology (Clifton, N.J.)
Cell tracking is an essential step in extracting cellular signals from moving cells, which is vital for understanding the mechanisms underlying various biological functions and processes, particularly in organs such as the brain and heart. However, c...

Revolutionizing tumor detection and classification in multimodality imaging based on deep learning approaches: Methods, applications and limitations.

Journal of X-ray science and technology
BACKGROUND: The emergence of deep learning (DL) techniques has revolutionized tumor detection and classification in medical imaging, with multimodal medical imaging (MMI) gaining recognition for its precision in diagnosis, treatment, and progression ...

Classification of DNA Sequence Based on a Non-gradient Algorithm: Pseudoinverse Learners.

Methods in molecular biology (Clifton, N.J.)
This chapter proposes a prototype-based classification approach for analyzing DNA barcodes that uses a spectral representation of DNA sequences and a non-gradient neural network. Biological sequences can be viewed as data components with higher non-f...

Convolutional Neural Networks for Glioma Segmentation and Prognosis: A Systematic Review.

Critical reviews in oncogenesis
Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extractio...