AIMC Topic: Deep Learning

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DeepSP: A Deep Learning Framework for Spatial Proteomics.

Journal of proteome research
The study of protein subcellular localization (PSL) is a fundamental step toward understanding the mechanism of protein function. The recent development of mass spectrometry (MS)-based spatial proteomics to quantify the distribution of proteins acros...

Deep Learning Versus Neurologists: Functional Outcome Prediction in LVO Stroke Patients Undergoing Mechanical Thrombectomy.

Stroke
BACKGROUND: Despite evolving treatments, functional recovery in patients with large vessel occlusion stroke remains variable and outcome prediction challenging. Can we improve estimation of functional outcome with interpretable deep learning models u...

PeakDetective: A Semisupervised Deep Learning-Based Approach for Peak Curation in Untargeted Metabolomics.

Analytical chemistry
Peak-detection algorithms currently used to process untargeted metabolomics data were designed to maximize sensitivity at the sacrifice of selectively. Peak lists returned by conventional software tools therefore contain a high density of artifacts t...

Detecting stress caused by nitrogen deficit using deep learning techniques applied on plant electrophysiological data.

Scientific reports
Plant electrophysiology carries a strong potential for assessing the health of a plant. Current literature for the classification of plant electrophysiology generally comprises classical methods based on signal features that portray a simplification ...

Prediction of osteoporosis using MRI and CT scans with unimodal and multimodal deep-learning models.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: Osteoporosis is the systematic degeneration of the human skeleton, with consequences ranging from a reduced quality of life to mortality. Therefore, the prediction of osteoporosis reduces risks and supports patients in taking precautions. De...

Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMD.

Eye (London, England)
PURPOSE: To evaluate the reliability of automated fluid detection in identifying retinal fluid activity in OCT scans of patients treated with anti-VEGF therapy for neovascular age-related macular degeneration by correlating human expert and automated...

Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography.

Medical physics
BACKGROUND: Invasive coronary angiography (ICA) is a primary imaging modality that visualizes the lumen area of coronary arteries for diagnosis and interventional guidance. In the current practice of quantitative coronary analysis (QCA), semi-automat...

Phenotyping urban built and natural environments with high-resolution satellite images and unsupervised deep learning.

The Science of the total environment
Cities in the developing world are expanding rapidly, and undergoing changes to their roads, buildings, vegetation, and other land use characteristics. Timely data are needed to ensure that urban change enhances health, wellbeing and sustainability. ...

A deep learning-based dynamic model for predicting acute kidney injury risk severity in postoperative patients.

Surgery
BACKGROUND: Acute kidney injury is a common postoperative complication affecting between 10% and 30% of surgical patients. Acute kidney injury is associated with increased resource usage and chronic kidney disease development, with more severe acute ...

A Pilot Study: Detrusor Overactivity Diagnosis Method Based on Deep Learning.

Urology
OBJECTIVE: To develop two intelligent diagnosis models of detrusor overactivity (DO) based on deep learning to assist doctors no longer heavily rely on visual observation of urodynamic study (UDS) curves.