AIMC Topic: Deep Learning

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A deep learning-based algorithm for automatic detection of perilunate dislocation in frontal wrist radiographs.

Hand surgery & rehabilitation
This study proposes a Deep Learning algorithm to automatically detect perilunate dislocation in anteroposterior wrist radiographs. A total of 374 annotated radiographs, 345 normal and 29 pathological, of skeletally mature adolescents and adults aged ...

A Forward Learning Algorithm for Neural Memory Ordinary Differential Equations.

International journal of neural systems
The deep neural network, based on the backpropagation learning algorithm, has achieved tremendous success. However, the backpropagation algorithm is consistently considered biologically implausible. Many efforts have recently been made to address the...

Deep learning based decoding of single local field potential events.

NeuroImage
How is information processed in the cerebral cortex? In most cases, recorded brain activity is averaged over many (stimulus) repetitions, which erases the fine-structure of the neural signal. However, the brain is obviously a single-trial processor. ...

Insights into traditional Large Deformation Diffeomorphic Metric Mapping and unsupervised deep-learning for diffeomorphic registration and their evaluation.

Computers in biology and medicine
This paper explores the connections between traditional Large Deformation Diffeomorphic Metric Mapping methods and unsupervised deep-learning approaches for non-rigid registration, particularly emphasizing diffeomorphic registration. The study provid...

Deep learning reconstruction for lumbar spine MRI acceleration: a prospective study.

European radiology experimental
BACKGROUND: We compared magnetic resonance imaging (MRI) turbo spin-echo images reconstructed using a deep learning technique (TSE-DL) with standard turbo spin-echo (TSE-SD) images of the lumbar spine regarding image quality and detection performance...

AlphaFold2 structures guide prospective ligand discovery.

Science (New York, N.Y.)
AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ and serotonin 2A (5-HT2A) receptors, testing hundreds of new molecules a...

Using machine learning or deep learning models in a hospital setting to detect inappropriate prescriptions: a systematic review.

European journal of hospital pharmacy : science and practice
OBJECTIVES: The emergence of artificial intelligence (AI) is catching the interest of hospital pharmacists. A massive collection of health data is now available to train AI models and hold the promise of disrupting codes and practices. The objective ...

Artificial intelligence in dermatopathology: Updates, strengths, and challenges.

Clinics in dermatology
Artificial intelligence (AI) has evolved to become a significant force in various domains, including medicine. We explore the role of AI in pathology, with a specific focus on dermatopathology and neoplastic dermatopathology. AI, encompassing machine...

Research on multi-defects classification detection method for solar cells based on deep learning.

PloS one
Solar cells are playing a significant role in aerospace equipment. In view of the surface defect characteristics in the manufacturing process of solar cells, the common surface defects are divided into three categories, which include difficult-detect...

A hybrid human recognition framework using machine learning and deep neural networks.

PloS one
Faces are a crucial environmental trigger. They communicate information about several key features, including identity. However, the 2019 coronavirus pandemic (COVID-19) significantly affected how we process faces. To prevent viral spread, many gover...