Menopause

An Artificial Neural Network-Based Approach to Optimizing Energy Efficiency in Residential Buildings in Hot Summer and Cold Winter Regions.

Resource depletion and ecological crisis have prompted human beings to reflect on the behavior patterns based on industrial civilization so as to seek ways of sustainable development of human society, economy, technology, and environment. The energy consumed in the construction process, commonly known as building energy consumption, accounts for more and more of the total social energy consumption...

Nuclear morphology is a deep learning biomarker of cellular senescence.

Cellular senescence is an important factor in aging and many age-related diseases, but understanding its role in health is challenging due to the lack of exclusive or universal markers. Using neural networks, we predict senescence from the nuclear morphology of human fibroblasts with up to 95% accuracy, and investigate murine astrocytes, murine neurons, and fibroblasts with premature aging in cult...

Deep-learning image reconstruction for image quality evaluation and accurate bone mineral density measurement on quantitative CT: A phantom-patient study.

BACKGROUND AND PURPOSE: To investigate the image quality and accurate bone mineral density (BMD) on quantitative CT (QCT) for osteoporosis screening b...

TSDLPP: A Novel Two-Stage Deep Learning Framework For Prognosis Prediction Based on Whole Slide Histopathological Images.

Recently, digital pathology image-based prognosis prediction has become a hot topic in healthcare research to make early decisions on therapy and impr...

Osteoporosis screening support system from panoramic radiographs using deep learning by convolutional neural network.

OBJECTIVES: This study was performed to develop computer-aided screening systems that could predict osteoporosis. The systems were constructed using p...

Deep-Learning-Based Ultrasound Sound-Speed Tomography Reconstruction with Tikhonov Pseudo-Inverse Priori.

Ultrasound sound-speed tomography (USST) is a promising technology for breast imaging and breast cancer detection. Its reconstruction is a complex non...

Can machine learning predict pharmacotherapy outcomes? An application study in osteoporosis.

BACKGROUND AND OBJECTIVE: The specific aim of this study is to develop machine learning models as a clinical approach for personalized treatment of os...

Short and long-term effects of robot-assisted therapy on upper limb motor function and activity of daily living in patients post-stroke: a meta-analysis of randomized controlled trials.

OBJECTIVE: To investigate the effect of robot-assisted therapy (RAT) on upper limb motor control and activity function in poststroke patients compared...

Deep learning applications in telerehabilitation speech therapy scenarios.

Nowadays, many application scenarios benefit from automatic speech recognition (ASR) technology. Within the field of speech therapy, in some cases ASR...

Cardiotocography in Obstetrics: New Solutions for "Routine" Technology.

This work is devoted to the problems of one of the most common screening examinations used in medical practice: fetal cardiotocography (CTG). The tech...

Accurate contactless sleep apnea detection framework with signal processing and machine learning methods.

The detection of sleep apnea is critical for assessing sleep quality. It is also a proven biometric in diagnosing cardiovascular and other diseases. R...

Automation of dry eye disease quantitative assessment: A review.

Dry eye disease (DED) is a common eye condition worldwide and a primary reason for visits to the ophthalmologist. DED diagnosis is performed through a...

Smart Sensorization Using Propositional Dynamic Logic.

The current high energy prices pose a serious challenge, especially in the domestic economy. In this respect, one of the main problems is obtaining do...

Effects of Robot-Assisted Gait Training with Body Weight Support on Gait and Balance in Stroke Patients.

This study investigated the effects of robot-assisted gait training with body weight support on gait and balance in stroke patients. The study partici...

A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients.

In a growing number of social and clinical scenarios, machine learning (ML) is emerging as a promising tool for implementing complex multi-parametric ...

iProm-Zea: A two-layer model to identify plant promoters and their types using convolutional neural network.

A promoter is a short DNA sequence near the start codon, responsible for initiating the transcription of a specific gene in the genome. The accurate r...

Classification of non-coding variants with high pathogenic impact.

Whole genome sequencing is increasingly used to diagnose medical conditions of genetic origin. While both coding and non-coding DNA variants contribut...

Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning.

Parallel imaging is the most clinically used acceleration technique for magnetic resonance imaging (MRI) in part due to its easy inclusion into routin...

Identification of osteoporosis using ensemble deep learning model with panoramic radiographs and clinical covariates.

Osteoporosis is becoming a global health issue due to increased life expectancy. However, it is difficult to detect in its early stages owing to a lac...

Prediction of femoral strength of elderly men based on quantitative computed tomography images using machine learning.

Hip fracture is the most common complication of osteoporosis, and its major contributor is compromised femoral strength. This study aimed to develop p...