AIMC Topic: Humans

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Artificial intelligence in radiography: Where are we now and what does the future hold?

Radiography (London, England : 1995)
OBJECTIVES: This paper will outline the status and basic principles of artificial intelligence (AI) in radiography along with some thoughts and suggestions on what the future might hold. While the authors are not always able to separate the current s...

Machine Learning for personalised stress detection: Inter-individual variability of EEG-ECG markers for acute-stress response.

Computer methods and programs in biomedicine
Stress appears as a response for a broad variety of physiological stimuli. It does vary among individuals in amplitude, phase and frequency. Thus, the necessity for personalised diagnosis is key to prevent stress-related diseases. In order to evaluat...

Deep learning and lung ultrasound for Covid-19 pneumonia detection and severity classification.

Computers in biology and medicine
The Covid-19 European outbreak in February 2020 has challenged the world's health systems, eliciting an urgent need for effective and highly reliable diagnostic instruments to help medical personnel. Deep learning (DL) has been demonstrated to be use...

Personalized brachytherapy dose reconstruction using deep learning.

Computers in biology and medicine
BACKGROUND AND PURPOSE: Accurate calculation of the absorbed dose delivered to the tumor and normal tissues improves treatment gain factor, which is the major advantage of brachytherapy over external radiation therapy. To address the simplifications ...

Machine learning-based identification and characterization of 15 novel pathogenic SUOX missense mutations.

Molecular genetics and metabolism
Isolated sulfite oxidase deficiency (ISOD) is a rare hereditary metabolic disease caused by absence of functional sulfite oxidase (SO) due to mutations of the SUOX gene. SO oxidizes toxic sulfite and sulfite accumulation is associated with neurologic...

Transparent Machine Learning Models to Diagnose Suspicious Thoracic Lesions Leveraging CT Guided Biopsy Data.

Academic radiology
RATIONALE AND OBJECTIVES: To train and validate machine learning models capable of classifying suspicious thoracic lesions as benign or malignant and to further classify malignant lesions by pathologic subtype while quantifying feature importance for...

Diagnostic performance of deep learning models for detecting bone metastasis on whole-body bone scan in prostate cancer.

European journal of nuclear medicine and molecular imaging
PURPOSE: We evaluated the performance of deep learning classifiers for bone scans of prostate cancer patients.

The effect of principal component analysis in the diagnosis of congestive heart failure via heart rate variability analysis.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
In this study, we investigated the effect of principal component analysis (PCA) in congestive heart failure (CHF) diagnosis using various machine learning algorithms from 5-min HRV data. The extracted 59 heart rate variability (HRV) features consist ...

MSST-RT: Multi-Stream Spatial-Temporal Relative Transformer for Skeleton-Based Action Recognition.

Sensors (Basel, Switzerland)
Skeleton-based human action recognition has made great progress, especially with the development of a graph convolution network (GCN). The most important work is ST-GCN, which automatically learns both spatial and temporal patterns from skeleton sequ...

GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations.

International journal of molecular sciences
Circular RNAs (circRNAs) are a new class of endogenous non-coding RNAs with covalent closed loop structure. Researchers have revealed that circRNAs play an important role in human diseases. As experimental identification of interactions between circR...