AI diagnoses heart arrhythmia.
Doctors at Kobe University Hospital (Japan) have developed an AI that combines data from ECG and X-ray results to predict the locations of defects in the heart which cause an irregular heartbeat. Combining the two completely different test data types lead to improved diagnostic accuracy of the AI.
Their work can be found in Scientific Reports: https://www.nature.com/articles/s41598-021-87631-y
#sciencenews #AI #healthcare #medicine
Doctors at Kobe University Hospital (Japan) have developed an AI that combines data from ECG and X-ray results to predict the locations of defects in the heart which cause an irregular heartbeat. Combining the two completely different test data types lead to improved diagnostic accuracy of the AI.
Their work can be found in Scientific Reports: https://www.nature.com/articles/s41598-021-87631-y
#sciencenews #AI #healthcare #medicine
Nature
Accessory pathway analysis using a multimodal deep learning model
Scientific Reports - Accessory pathway analysis using a multimodal deep learning model
Unremarkable AI.
Carnegie Mellon University researchers say clinical AI tools should be designed to take tough life-and-death clinical decisions out of the hands of physicians. They suggest that AI might guide decisions best if it were seamlessly embedded in the decision-making routines already used by the clinical team, providing predictions and evaluations on the go.
Read more about their ideas in Proceedings of the CHI Conference on Human Factors in Computing Systems: https://doi.org/10.1145/3290605.3300468
#sciencenews #AI #healthcare #medicine
Carnegie Mellon University researchers say clinical AI tools should be designed to take tough life-and-death clinical decisions out of the hands of physicians. They suggest that AI might guide decisions best if it were seamlessly embedded in the decision-making routines already used by the clinical team, providing predictions and evaluations on the go.
Read more about their ideas in Proceedings of the CHI Conference on Human Factors in Computing Systems: https://doi.org/10.1145/3290605.3300468
#sciencenews #AI #healthcare #medicine
AI fighting breast cancer.
According to the WHO, breast cancer has recently overtaken lung cancer to become the most common cancer globally. The BreastPathQ Challenge was launched at the SPIE Medical Imaging 2019 conference to support the development of computer-aided diagnosis for assessing breast cancer pathology. 39 teams from 12 countries participated, with 100 new algorithms developed.
The exciting results and ideas produced by the challenge have recently been reported in the Journal of Medical Imaging: https://www.spiedigitallibrary.org/journals/journal-of-medical-imaging/volume-8/issue-03/034501/SPIE-AAPM-NCI-BreastPathQ-challenge--an-image-analysis-challenge/10.1117/1.JMI.8.3.034501.full?SSO=1
#sciencenews #AI #ML #healthcare #medicine
According to the WHO, breast cancer has recently overtaken lung cancer to become the most common cancer globally. The BreastPathQ Challenge was launched at the SPIE Medical Imaging 2019 conference to support the development of computer-aided diagnosis for assessing breast cancer pathology. 39 teams from 12 countries participated, with 100 new algorithms developed.
The exciting results and ideas produced by the challenge have recently been reported in the Journal of Medical Imaging: https://www.spiedigitallibrary.org/journals/journal-of-medical-imaging/volume-8/issue-03/034501/SPIE-AAPM-NCI-BreastPathQ-challenge--an-image-analysis-challenge/10.1117/1.JMI.8.3.034501.full?SSO=1
#sciencenews #AI #ML #healthcare #medicine
www.spiedigitallibrary.org
SPIE-AAPM-NCI BreastPathQ Challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer…
The <i>Journal of Medical Imaging</i> allows for the peer-reviewed communication and archiving of fundamental and translational research, as well as applications, focused on medical imaging, a field that continues to benefit from technological improvements…
Deep Neural Networks in medical imaging.
Scientists at the University of California are investigating how neural networks can be used to efficiently and accurately analyse associations between gene expression and features of biological tissues. They consider how the neural networks could lead to improvements in lung cancer diagnosis.
The results are published in the Journal of Medical Imaging: https://www.spiedigitallibrary.org/journals/journal-of-medical-imaging/volume-8/issue-03/031906/Using-deep-neural-networks-and-interpretability-methods-to-identify-gene/10.1117/1.JMI.8.3.031906.full
#sciencenews #AI #ML #healthcare #medicine
Scientists at the University of California are investigating how neural networks can be used to efficiently and accurately analyse associations between gene expression and features of biological tissues. They consider how the neural networks could lead to improvements in lung cancer diagnosis.
The results are published in the Journal of Medical Imaging: https://www.spiedigitallibrary.org/journals/journal-of-medical-imaging/volume-8/issue-03/031906/Using-deep-neural-networks-and-interpretability-methods-to-identify-gene/10.1117/1.JMI.8.3.031906.full
#sciencenews #AI #ML #healthcare #medicine
www.spiedigitallibrary.org
Using deep neural networks and interpretability methods to identify gene expression patterns that predict radiomic features and…
The <i>Journal of Medical Imaging</i> allows for the peer-reviewed communication and archiving of fundamental and translational research, as well as applications, focused on medical imaging, a field that continues to benefit from technological improvements…
Self-learning robots.
Researchers from AMOLF's Soft Robotic Matter group have shown small, autonomous, self-learning robots can adapt easily to changing circumstances. They connected a group of simple robots in a line, after which each individual robot taught itself to move forward as quickly as possible.
The results are available in PNAS: https://www.pnas.org/content/118/21/e2017015118/tab-article-info
#sciencenews #AI #robots
Researchers from AMOLF's Soft Robotic Matter group have shown small, autonomous, self-learning robots can adapt easily to changing circumstances. They connected a group of simple robots in a line, after which each individual robot taught itself to move forward as quickly as possible.
The results are available in PNAS: https://www.pnas.org/content/118/21/e2017015118/tab-article-info
#sciencenews #AI #robots
PNAS
Continuous learning of emergent behavior in robotic matter
In the last century, robots have been revolutionizing our lives, augmenting human actions with greater precision and repeatability. Unfortunately, most robotic systems can only operate in controlled environments. While increasing the complexity of the centralized…
Artificial muscles.
Universidad Carlos III de Madrid researchers offer guidance on the design of magneto-active structural systems that can be applied to stimulate wound healing and artificially replicate muscle tissues. They describe their method as creating an ‘athletic track for cells’.
Two articles on their work have been published recently in Composites Part B: Engineering and International Journal of Solids and Structures: https://doi.org/10.1016/j.compositesb.2021.108796 https://doi.org/10.1016/j.ijsolstr.2020.10.028
#sciencenews #AI #bioengineering
Universidad Carlos III de Madrid researchers offer guidance on the design of magneto-active structural systems that can be applied to stimulate wound healing and artificially replicate muscle tissues. They describe their method as creating an ‘athletic track for cells’.
Two articles on their work have been published recently in Composites Part B: Engineering and International Journal of Solids and Structures: https://doi.org/10.1016/j.compositesb.2021.108796 https://doi.org/10.1016/j.ijsolstr.2020.10.028
#sciencenews #AI #bioengineering
Sciencedirect
Influence of elastomeric matrix and particle volume fraction on the mechanical response of magneto-active polymers
Magneto-active polymers (MAPs) are revolutionising the fields of material science and solid mechanics as well as having an important presence in the b…
A graphene key for computing.
Current silicon technology exploits microscopic differences between computing components to create secure keys, but AI techniques can be used to predict defects and gain access to data. Penn State researchers have designed a way to make the encrypted keys harder to crack using graphene.
The results are presented in Nature Electronics: https://www.nature.com/articles/s41928-021-00569-x
#sciencenews #AI #computing #graphene
Current silicon technology exploits microscopic differences between computing components to create secure keys, but AI techniques can be used to predict defects and gain access to data. Penn State researchers have designed a way to make the encrypted keys harder to crack using graphene.
The results are presented in Nature Electronics: https://www.nature.com/articles/s41928-021-00569-x
#sciencenews #AI #computing #graphene
Nature
Graphene-based physically unclonable functions that are reconfigurable and resilient to machine learning attacks
Nature Electronics - Disorder in the charge carrier transport of graphene-based field-effect transistors can be used to construct physically unclonable functions that are secure and can withstand...
AI-powered microscopes.
Light field microscopy allows the neuronal signals in the brain to be imaged in real time, but the images are often lacking quality and take a long time to process for visualisation. European Molecular Biology Laboratory scientists are using artificial intelligence to boost the image processing speeds from days to seconds.
Learn about their technique in Nature Methods:
https://www.nature.com/articles/s41592-021-01136-0
#sciencenews #AI #science #microscopy
Light field microscopy allows the neuronal signals in the brain to be imaged in real time, but the images are often lacking quality and take a long time to process for visualisation. European Molecular Biology Laboratory scientists are using artificial intelligence to boost the image processing speeds from days to seconds.
Learn about their technique in Nature Methods:
https://www.nature.com/articles/s41592-021-01136-0
#sciencenews #AI #science #microscopy
Nature Methods
Deep learning-enhanced light-field imaging with continuous validation
A deep learning–based algorithm enables efficient reconstruction of light-field microscopy data at video rate. In addition, concurrently acquired light-sheet microscopy data provide ground truth data for training, validation and refinement of the algorithm.
On the brink of chaos.
Scientists at the University of Sydney and Japan's National Institute for Material Science have discovered that an artificial network of nanowires can be tuned to respond in a brain-like way to electrical stimuli. By keeping the network of nanowires in a chaotic, brain-like state optimized its performance.
Their insights are published in Nature Communications: http://dx.doi.org/10.1038/s41467-021-24260-z
#sciencenews #nano #AI
Scientists at the University of Sydney and Japan's National Institute for Material Science have discovered that an artificial network of nanowires can be tuned to respond in a brain-like way to electrical stimuli. By keeping the network of nanowires in a chaotic, brain-like state optimized its performance.
Their insights are published in Nature Communications: http://dx.doi.org/10.1038/s41467-021-24260-z
#sciencenews #nano #AI
Nature Communications
Avalanches and edge-of-chaos learning in neuromorphic nanowire networks
Neuromorphic nanowire networks are found to exhibit neural-like dynamics, including phase transitions and avalanche criticality. Hochstetter and Kuncic et al. show that the dynamical state at the...
AI in 3D printing.
Additive manufacturing allows on-demand production. However, the performance of the final object is hard to predict. A team at the University of Texas has shown that neural networks can be used to better understand the processes.
The study is published in the journal Computational Methods: https://www.sciencedirect.com/science/article/abs/pii/S0045782521002474?via%3Dihub
#sciencenews #AI #3dprinting
Additive manufacturing allows on-demand production. However, the performance of the final object is hard to predict. A team at the University of Texas has shown that neural networks can be used to better understand the processes.
The study is published in the journal Computational Methods: https://www.sciencedirect.com/science/article/abs/pii/S0045782521002474?via%3Dihub
#sciencenews #AI #3dprinting
Sciencedirect
A mixed interface-capturing/interface-tracking formulation for thermal multi-phase flows with emphasis on metal additive manufacturing…
High fidelity thermal multi-phase flow simulations are in much demand to reveal the multi-scale and multi-physics phenomena in metal additive manufact…
AI for mental health.
Researchers from the University of Tsukuba have found that AI can be used to detect signs of depression. They used machine learning to predict psychological distress among study participants without the need for subjective data inputs.
The process is described in BMJ Open: http://dx.doi.org/10.1136/bmjopen-2020-046265
#sciencenews #AI
Researchers from the University of Tsukuba have found that AI can be used to detect signs of depression. They used machine learning to predict psychological distress among study participants without the need for subjective data inputs.
The process is described in BMJ Open: http://dx.doi.org/10.1136/bmjopen-2020-046265
#sciencenews #AI
BMJ Open
Comparison of predicted psychological distress among workers between artificial intelligence and psychiatrists: a cross-sectional…
Objectives Psychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic…