AIMC Topic: Deep Learning

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A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography.

Scientific reports
Since its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radi...

A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs.

Nature communications
Pelvic radiograph (PXR) is essential for detecting proximal femur and pelvis injuries in trauma patients, which is also the key component for trauma survey. None of the currently available algorithms can accurately detect all kinds of trauma-related ...

A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.

The international journal of cardiovascular imaging
Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in non-gated images as existing methods to acquire gated or to retrospectively gate IVUS images have f...

Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment.

Environmental science and pollution research international
One of the most critical parameters in concrete design is compressive strength. As the compressive strength of concrete is correctly measured, time and cost can be decreased. Concrete strength is relatively resilient to impacts on the environment. Th...

DON: Deep Learning and Optimization-Based Framework for Detection of Novel Coronavirus Disease Using X-ray Images.

Interdisciplinary sciences, computational life sciences
In the hospital, a limited number of COVID-19 test kits are available due to the spike in cases every day. For this reason, a rapid alternative diagnostic option should be introduced as an automated detection method to prevent COVID-19 spreading amon...

Deep Learning Corpus Callosum Segmentation as a Neurodegenerative Marker in Multiple Sclerosis.

Journal of neuroimaging : official journal of the American Society of Neuroimaging
BACKGROUND AND PURPOSE: Corpus callosum atrophy is a sensitive biomarker of multiple sclerosis (MS) neurodegeneration but typically requires manual 2D or volumetric 3D-based segmentations. We developed a supervised machine learning algorithm, DeepnCC...

Potential applications of deep learning in single-cell RNA sequencing analysis for cell therapy and regenerative medicine.

Stem cells (Dayton, Ohio)
When used in cell therapy and regenerative medicine strategies, stem cells have potential to treat many previously incurable diseases. However, current application methods using stem cells are underdeveloped, as these cells are used directly regardle...

Sensing morphogenesis of bone cells under microfluidic shear stress by holographic microscopy and automatic aberration compensation with deep learning.

Lab on a chip
We present sensing time-lapse morphogenesis of living bone cells under micro-fluidic shear stress (FSS) by digital holographic (DH) microscopy. To remove the effect of aberrations on quantitative measurements, we propose a numerical and automatic met...

Artificial Intelligence-Based Prediction of Covid-19 Severity on the Results of Protein Profiling.

Computer methods and programs in biomedicine
BACKGROUND: COVID-19 progresses slowly and negatively affects many people. However, mild to moderate symptoms develop in most infected people, who recover without hospitalization. Therefore, the development of early diagnosis and treatment strategies...