Articles tagged with Artificial Intelligence
An AI model developed by researchers identified a subset of individuals with a 25-fold risk of developing pancreatic cancer within three to 36 months. The model used sequence of medical diagnoses from patient records to predict risk, which was validated in two independent datasets.
Researchers have gained unprecedented insights into the heart's dynamic ultrastructure using high-resolution electron microscopy. This knowledge is crucial for developing new therapeutic concepts for heart attacks and cardiac arrhythmias.
Researchers used AI to analyze videos of over 2,500 ASL signs and found that challenging signs are made closer to the signer's face, making them easier for perceivers to recognize. This suggests that ASL has evolved to be more recognizable, improving communication.
The UPV study uses AI to investigate subphenotypes in line with clinical characteristics, helping clinicians assess patients and plan resource allocation. The team develops predictive models for early mortality prediction and severity assessment, offering robust and reliable AI in data quality issues.
The UNC Charlotte team developed a universal AI algorithm called AutoClass to clean noisy single-cell RNA sequencing (scRNA-Seq) data. The algorithm effectively removes noise and enhances downstream analysis in multiple aspects, demonstrating its robustness and scalability.
Researchers have developed a new method called Shared Interest that enables users to aggregate, sort, and rank individual explanations of a machine-learning model's reasoning. This technique uses quantifiable metrics to compare how well the model's reasoning matches human thinking, helping to uncover concerning trends in decision-making.
A new e-nose prototype, NOS.E, can distinguish between six whiskies by brand names, regions, and styles in under four minutes, with 100% accuracy for region detection and 96.15% for brand name identification. The technology has applications beyond whisky, including counterfeiting detection in perfume and wine.
Researchers at the University of Bristol created a 3D-printed artificial fingertip that produces nerve signals similar to those from human tactile nerves. The innovation could improve robot dexterity and prosthetic hand performance by giving them an in-built sense of touch.
A study using AI-powered robot scientist Eve analyzed over 12,000 research papers on breast cancer cell biology and found that less than one third of the results were reproducible. The researchers found that significant evidence for repeatability was found in 43 papers, while 22 papers showed replicable results under different conditions.
Researchers at Carnegie Mellon University developed AI-enhanced museum exhibits that increased learning and engagement for elementary school-aged children. The intelligent exhibits featured a virtual assistant, NoRilla, which interacted with visitors, asking questions and guiding them through scientific challenges.
Researchers at MIT developed a framework for robotic manipulation systems that can perform complex tasks using a two-stage learning process. This allows robots to learn abstract ideas about manipulating deformable objects, such as pizza dough, and execute skills to complete tasks.
Researchers integrated biological signals with gold-standard machine learning methods to create emotionally intelligent speech dialog systems. The study found that combining language information with biological signal information improved the AI's performance, making it comparable to human-like emotional recognition.
Researchers at Yale University developed an AI-based model that can diagnose multiple heart rhythm and conduction disorders using electrocardiogram images, regardless of format or layout. The tool has been validated through multiple international data sources with high accuracy for clinical diagnosis from ECGs.
A machine-learning algorithm detected potential signs of colorectal cancer in high-risk patients who missed routine screenings. The algorithm identified patients as high-risk by analyzing age, gender, and recent CBC results, and subsequent screenings showed significant findings in 70% of the flagged group.
A research team from Universidad Complutense de Madrid developed a machine learning-based tool to predict areas with best access to potable groundwater in Africa. The tool achieved a success rate of close to 90% in initial trials, particularly in Mali and Chad.
Researchers at MIT created a process called DualFair that can remove bias from data used to train machine-learning models. The method tackles both label bias and selection bias, significantly reducing discrimination in loan predictions while maintaining high accuracy.
Researchers used AI to detect dialects in zebra finch songs, showing they play a key role in mate choice. Females prefer males who sing the same dialect as their parents, indicating cultural trait importance over physical appearance.
A study by RIFS researcher Silke Niehoff analyzed DAX company sustainability reports and found that most prioritize customer demands and efficiency over environmental concerns. However, a few companies are using digitalization to promote sustainable development and serve as role models.
A Bayes Business School study found that supply chain professionals overestimate technology benefits and underestimate needs, leading to disappointment. The study offers a list of goals, constraints, and benefits to help managers better calibrate expectations.
Researchers developed an AI-driven image analysis pipeline that identified novel cellular hallmarks of Parkinson's disease from images of over a million skin cells. The platform can distinguish between patient cells and healthy controls, revealing new signatures for potential therapeutic targets.
Researchers developed a novel approach to diagnose, monitor, and predict the course of myocarditis using CMR parameters and AI. The goal is to provide personalized counseling for athletes and optimize treatment options.
Scientists from Cardiff University have developed a new artificial intelligence method to automate the extraction of information from large collections of museum specimens. The 'image segmentation' method can accurately locate and bound different visual regions on images, allowing for faster and more efficient digitization processes.
Researchers developed an AI-based computer algorithm that accurately predicted heart health risks based on voice recordings alone, identifying a high biomarker score associated with increased CAD-related events. The study suggests voice technology could be a powerful screening tool in remote healthcare delivery and telehealth.
The Human Brain Project (HBP) has brought together neuroscientists from different disciplines to work collaboratively on common goals. The HBP researchers outline their scientific approach and illustrate the potential of EBRAINS infrastructure for neuroscience research.
Researchers developed an AI algorithm that can measure coronary plaque buildup in five seconds, predicting heart attack risk within five years. The tool was trained on images from 921 people and matched results with invasive tests considered highly accurate.
Researchers have developed a new forecasting approach using machine learning and anonymized datasets from Facebook that significantly outperforms conventional models for projecting COVID trends at the county level. The model captures shifting trends in numbers reflecting lockdowns, waning immunity, or masking policies.
A study found that telemedicine and community screening for diabetic retinopathy are highly cost-effective in rural and urban China. The results suggest that adopting telemedicine screening programs at the national primary care level is economically reasonable.
A pilot study suggests that an artificial intelligence system called CRANE can help cardiac experts diagnose rejection and estimate its severity more accurately. This could lead to faster diagnosis and treatment, as well as reduced variability in expert agreement, ultimately improving heart transplant outcomes.
Researchers used a contrast pattern mining algorithm on publicly available data from 16,000 participants in the T1D Exchange Clinic Registry. The study found individuals with an immediate family history of Type 1 diabetes were more frequently diagnosed with hypertension and other co-occurring conditions.
A new machine learning study analyzed 10 years of weather data to identify three major categories of weather patterns and their effects on thunderstorms. The study aims to isolate the impact of aerosols, tiny particles suspended in the atmosphere, on storm severity.
A new AI method accurately estimates timetable robustness within milliseconds, enabling efficient optimization. This improves balance between passenger needs and economic conditions.
Researchers from the University of Cambridge and Oslo identify a century-old mathematical paradox as the Achilles' heel of modern AI. The paradox limits the existence of stable and accurate neural networks, making many AI systems untrustworthy in high-risk areas.
Researchers created BirdBot, a robotic leg inspired by the ostrich's anatomy, which achieves energy efficiency through a mechanical coupling of muscles and tendons. The robot leg requires fewer motors than other machines, making it suitable for large size applications.
Mayo Clinic researchers used AI to predict antidepressant outcomes in children and adolescents with major depressive disorder. They identified six depressive symptoms and assessed them using the Children's Depression Rating Scale-Revised to predict treatment outcomes.
Scientists developed an AI approach to model and map the Earth's natural features in greater detail and accuracy. The new system can recognise intricate features and aspects of the terrain far beyond traditional methods, generating enhanced-quality environmental maps.
The European Lung Cancer Congress 2022 focused on the latest advancements in lung cancer treatment, including personalized strategies for better patient care. The congress also explored emerging therapies and molecular sub-types to enhance precision medicine.
Researchers at NC State University have developed a 'self-driving lab' that uses artificial intelligence and fluidic systems to advance our understanding of metal halide perovskite nanocrystals. The technology can autonomously dope MHP nanocrystals, adding manganese atoms on demand, allowing for faster control over properties.
Researchers reexamined hundreds of experiments on neural activity and consciousness, finding that experiment parameters determine results. The study used artificial intelligence to predict which theory would be supported by each experiment with 80% success.
Researchers found that social media consumers trust verified accounts less than unverified ones if the content is inconsistent with the influencer's brand. Verified accounts can actually harm trust and may not be more effective at selling products.
A global study found symptoms of COVID-19 differ significantly between countries and individuals with underlying health conditions. Researchers analyzed data from 78,299 individuals in 190 countries, revealing varied symptom profiles that can inform clinical practice, public health messaging, and treatment strategies.
A Bocconi University study used machine learning to analyze data on 2038 couples in Germany, finding that life satisfaction and housework are key predictors of union dissolution. The analysis also revealed complex interactions between variables, including the impact of personal traits like openness and extraversion.
A new study found that music and auditory beat stimulation can significantly reduce state anxiety in people with moderate trait anxiety. The treatment was more effective than music alone or pink noise for reducing somatic anxiety, while music alone was more effective for reducing cognitive state anxiety.
Researchers at the University of Tsukuba created a handheld social robot, OMOY, that can appear to convey emotions by shifting an internal weight while reading out text messages. The robot was tested with 94 people and found to reduce negative emotions such as anger, revenge, and avoidance motivation.
Researchers at Rutgers University have successfully stabilized an enzyme that degrades scar tissue resulting from spinal cord injuries, promoting tissue regeneration. The study used AI-driven liquid handling robotics to synthesize and test copolymers that stabilize the enzyme, offering new hope for patients with spinal cord injuries.
A new study by Anglia Ruskin University found that AI-powered apps reduced business risks during the pandemic by 3.1%. The use of AI apps was also linked to lower profits (2%) and overall business risk (1.2%). Only 26% of small enterprises are currently using these applications.
Researchers at the University of Washington developed an AI-designed protein that can awaken individual dormant genes by disabling chemical 'off switches'. This approach allows for safe upregulation of specific genes to affect cell activity without permanently changing the genome.
Researchers at MIT and Harvard University applied cognitive science theories to human-robot interaction, finding that humans need to see variation in robot behavior to build accurate mental models. Theories suggest that strategic variation can reveal concepts that might be difficult for a person to discern otherwise.
Researchers at Mainz University will examine algorithm decisions on transparency, fairness, and data protection while optimizing resource use. The project aims to create workable trade-offs for applications and integrates young researcher promotion programs.
Biohybrid micro- and nanorobots promise to deliver drugs to body tissues with high precision, enabling tasks such as cancer treatment, cell microsurgery, and tissue engineering. Researchers envision incorporating novel biological components into robots to overcome immune responses and increase efficiency in manufacturing.
Researchers found that ant colonies use an algorithm similar to the internet's data optimization, which senses and stabilizes behavior. This principle is also used in cells and neurons. Nature's algorithms may inspire new cybersecurity strategies or alternative approaches to gene regulation.
Researchers at University of Illinois develop new method to accurately estimate soil organic carbon using airborne and satellite hyperspectral sensing. The study leverages machine learning algorithms with a comprehensive soil spectral library, enabling large-scale monitoring of surface soil organic carbon.
Researchers propose various approaches using AI, deep learning, and machine learning to improve the accuracy and predictive power of biomarkers for cancer and other diseases. The tools have shown promising applications in identifying early-stage cancers, inferring the site of specific cancers, and predicting response to immunotherapy.
Researchers developed a machine-learning technique that can pinpoint anomalies in large datasets, such as power grid failures and traffic bottlenecks. The model uses advanced probability distributions to identify low-density values, allowing for faster and more accurate anomaly detection.
GIST researchers propose a new strategy for crime prevention using artificial intelligence, trained on a large-scale dataset of deviant incident reports and corresponding images. The model, called DevianceNet, can accurately classify and detect deviant places, making it a useful tool in urban safety development.
Researchers warn of substantial risks associated with AI in agriculture, including cyber-attacks and environmental degradation. They suggest involving ecologists in technology design to avoid scenarios like overuse of fertilisers and soil erosion.
A new AI algorithm developed by physician-scientists can effectively identify and distinguish between two life-threatening heart conditions: hypertrophic cardiomyopathy and cardiac amyloidosis. The algorithm uses specific features from cardiac ultrasound videos to flag high-risk patients, enabling earlier diagnosis and treatment.
Pharmaceutical firms are working towards using machine learning to analyze vast stores of data, developing models that evolve and improve as the data are processed. However, experts agree that a fully functional end-to-end approach is still a ways off due to biology's complexity.
Researchers studied how diverse neural network training datasets impact generalization. They found that data diversity is key to overcoming bias, but also degrade performance when neural networks are trained for multiple tasks simultaneously. The study highlights the importance of designing diverse and controlled datasets in machine le...
A three-year project funded by the National Science Foundation will use artificial intelligence (AI) to identify fossil shark teeth, including those of the extinct megalodon. The program aims to increase interest in STEM careers among middle schoolers.
Researchers at the University of Copenhagen have developed an AI method to recognize and detect insect species based on their wingbeats, enabling easier monitoring of biodiversity. The method uses infrared sensors to measure wingbeats and group insects into different species without human input.