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Deep Learning CT-based Quantitative Visualization Tool for Liver Volume Estimation: Defining Normal and Hepatomegaly.

Radiology
Background Imaging assessment for hepatomegaly is not well defined and currently uses suboptimal, unidimensional measures. Liver volume provides a more direct measure for organ enlargement. Purpose To determine organ volume and to establish threshold...

Empowering study of breast cancer data with application of artificial intelligence technology: promises, challenges, and use cases.

Clinical & experimental metastasis
In healthcare, artificial intelligence (AI) technologies have the potential to create significant value by improving time-sensitive outcomes while lowering error rates for each patient. Diagnostic images, clinical notes, and reports are increasingly ...

Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence.

La Radiologia medica
Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis for breast MRI, but ultrafast images, T2-weighted images, and diffusi...

Deep Learning for Prediction of N2 Metastasis and Survival for Clinical Stage I Non-Small Cell Lung Cancer.

Radiology
Background Preoperative mediastinal staging is crucial for the optimal management of clinical stage I non-small cell lung cancer (NSCLC). Purpose To develop a deep learning signature for N2 metastasis prediction and prognosis stratification in clinic...

The Route of Motor Recovery in Stroke Patients Driven by Exoskeleton-Robot-Assisted Therapy: A Path-Analysis.

Medical sciences (Basel, Switzerland)
: Exoskeleton-robot-assisted therapy is known to positively affect the recovery of arm functions in stroke patients. However, there is a lack of evidence regarding which variables might favor a better outcome and how this can be modulated by other fa...

Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children.

Nutrients
INTRODUCTION: Accurate assessment of resting energy expenditure (REE) can guide optimal nutritional prescription in critically ill children. Indirect calorimetry (IC) is the gold standard for REE measurement, but its use is limited. Alternatively, RE...

Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network.

Biosensors
Masses are one of the early signs of breast cancer, and the survival rate of women suffering from breast cancer can be improved if masses can be correctly identified as benign or malignant. However, their classification is challenging due to the simi...

Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors.

International journal of molecular sciences
Olfactory receptors (ORs) constitute the largest superfamily of G protein-coupled receptors (GPCRs). ORs are involved in sensing odorants as well as in other ectopic roles in non-nasal tissues. Matching of an enormous number of the olfactory stimulat...

Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images.

Computational and mathematical methods in medicine
Diagnosis of different breast cancer stages using histopathology whole slide images (WSI) is the gold standard in determining the grade of tissue metastasis. Computer-aided diagnosis (CAD) assists medical experts as a second opinion tool in early det...

Effect of pedestrian physique differences on head injury prediction in car-to-pedestrian accidents using deep learning.

Traffic injury prevention
OBJECTIVE: The aim of this study is to identify the effects of pedestrian physique differences on head injury prediction in car-to-pedestrian accidents via deep learning.