AIMC Topic: Deep Learning

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FlyVISTA, an integrated machine learning platform for deep phenotyping of sleep in .

Science advances
There is great interest in using genetically tractable organisms such as to gain insights into the regulation and function of sleep. However, sleep phenotyping in has largely relied on simple measures of locomotor inactivity. Here, we present FlyVI...

Real-Time Acoustic Scene Recognition for Elderly Daily Routines Using Edge-Based Deep Learning.

Sensors (Basel, Switzerland)
The demand for intelligent monitoring systems tailored to elderly living environments is rapidly increasing worldwide with population aging. Traditional acoustic scene monitoring systems that rely on cloud computing are limited by data transmission d...

Deep learning radiomics for the prediction of epidermal growth factor receptor mutation status based on MRI in brain metastasis from lung adenocarcinoma patients.

BMC cancer
BACKGROUND: Early and accurate identification of epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients with brain metastases is critical for guiding targeted therapy. This study aimed to develop a deep...

Deep learning-based evaluation of panoramic radiographs for osteoporosis screening: a systematic review and meta-analysis.

BMC medical imaging
BACKGROUND: Osteoporosis is a complex condition that drives research into its causes, diagnosis, treatment, and prevention, significantly affecting patients and healthcare providers in various aspects of life. Research is exploring orthopantomogram (...

Lung cancer detection with machine learning classifiers with multi-attribute decision-making system and deep learning model.

Scientific reports
Diseases of the airways and the other parts of the lung cause chronic respiratory diseases. The major cause of lung disease is tobacco smoke, along with risk factors such as dust, air pollution, chemicals, and frequent lower respiratory infections du...

Mapping variants in thyroid hormone transporter MCT8 to disease severity by genomic, phenotypic, functional, structural and deep learning integration.

Nature communications
Predicting and quantifying phenotypic consequences of genetic variants in rare disorders is a major challenge, particularly pertinent for 'actionable' genes such as thyroid hormone transporter MCT8 (encoded by the X-linked SLC16A2 gene), where loss-o...

Disease detection on exterior surfaces of buildings using deep learning in China.

Scientific reports
Urban infrastructure, particularly in ageing cities, faces significant challenges in maintaining building aesthetics and structural integrity. Traditional methods for detecting diseases on building exteriors, such as manual inspections, are often ine...

Zebrafish identification with deep CNN and ViT architectures using a rolling training window.

Scientific reports
Zebrafish are widely used in vertebrate studies, yet minimally invasive individual tracking and identification in the lab setting remain challenging due to complex and time-variable conditions. Advancements in machine learning, particularly neural ne...

Deep learning based automatic quantification of aortic valve calcification on contrast enhanced coronary CT angiography.

Scientific reports
Quantifying aortic valve calcification is critical for assessing the severity of aortic stenosis, predicting cardiovascular risk, and guiding treatment decisions. This study evaluated the feasibility of a deep learning-based automatic quantification ...

Artificial Intelligence in Gas Sensing: A Review.

ACS sensors
The role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in enhancing and automating gas sensing methods and the implications of these technologies for emergent gas sensor systems is reviewed. Applications of AI-based i...