AIMC Topic: Deep Learning

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Detecting hip osteoarthritis on clinical CT: a deep learning application based on 2-D summation images derived from CT.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
UNLABELLED: We developed and compared deep learning models to detect hip osteoarthritis on clinical CT. The CT-based summation images, CT-AP, that resemble X-ray radiographs can detect radiographic hip osteoarthritis and in the absence of large train...

Deep Learning Brings New Era in Echocardiography.

Circulation journal : official journal of the Japanese Circulation Society

Automated segmentation of biventricular contours in tissue phase mapping using deep learning.

NMR in biomedicine
Tissue phase mapping (TPM) is an MRI technique for quantification of regional biventricular myocardial velocities. Despite its potential, clinical use is limited due to the requisite labor-intensive manual segmentation of cardiac contours for all tim...

Deep learning-based Hounsfield unit value measurement method for bolus tracking images in cerebral computed tomography angiography.

Computers in biology and medicine
BACKGROUND: Patient movement during bolus tracking (BT) impairs the accuracy of Hounsfield unit (HU) measurements. This study assesses the accuracy of measuring HU values in the internal carotid artery (ICA) using an original deep learning (DL)-based...

Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy.

Medical physics
PURPOSE: The aim of this study is to improve the performance of machine learning (ML) models in predicting response of non-small cell lung cancer (NSCLC) to stereotactic body radiation therapy (SBRT) by integrating image features from pre-treatment c...

Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients.

Chinese medical journal
BACKGROUND: A deep learning model (DLM) that enables non-invasive hypokalemia screening from an electrocardiogram (ECG) may improve the detection of this life-threatening condition. This study aimed to develop and evaluate the performance of a DLM fo...

5G-enabled contactless multi-user presence and activity detection for independent assisted living.

Scientific reports
Wireless sensing is the state-of-the-art technique for next generation health activity monitoring. Smart homes and healthcare centres have a demand for multi-subject health activity monitoring to cater for future requirements. 5G-sensing coupled with...

Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins.

PloS one
Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simula...

Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping.

PloS one
Cosegmentation is a newly emerging computer vision technique used to segment an object from the background by processing multiple images at the same time. Traditional plant phenotyping analysis uses thresholding segmentation methods which result in h...

Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches.

Skeletal radiology
Deep learning-based MRI diagnosis of internal joint derangement is an emerging field of artificial intelligence, which offers many exciting possibilities for musculoskeletal radiology. A variety of investigational deep learning algorithms have been d...