Lymph nodes become swollen, there is weight loss and fatigue, as well as fevers and infections - these are typical symptoms of malignant B-cell lymphomas and related leukemias. If such a cancer of the lymphatic system is suspected, the physician takes a blood or bone marrow sample and sends it to specialized laboratories. This is where flow cytometry comes in. Flow cytometry is a method in which the blood cells flow past measurement sensors at high speed. The properties of the cells can be detected depending on their shape, structure or coloring.
An unimaginable amount of data is continually being generated by scientific experiments, longitudinal studies, clinical trials, and hospital records--but what can be done with all this information? Barbara Engelhardt (she/her), PhD, is building machine-learning models and statistical tools to make use of that data and find ways to better understand, and even prevent, disease. She is now joining Gladstone Institutes as a senior investigator. "Barbara is an innovator in computational biology," says Katie Pollard, PhD, director of the Gladstone Institute of Data Science and Biotechnology. "She brings vast expertise in statistical models and will help expand our machine-learning program. We're thrilled she's joining our team."
Bullish predictions suggest that artificial intelligence (AI) could contribute up to $15.7 trillion to the global economy by 2030. From autonomous cars to faster mortgage approvals and automated advertising decisions, AI algorithms promise numerous benefits for businesses and their customers. Unfortunately, these benefits may not be enjoyed equally. Algorithmic bias -- when algorithms produce discriminatory outcomes against certain categories of individuals, typically minorities and women -- may also worsen existing social inequalities, particularly when it comes to race and gender. From the recidivism prediction algorithm used in courts to the medical care prediction algorithm used by hospitals, studies have found evidence of algorithmic biases that make racial disparities worse for those impacted, not better. Many firms have put considerable effort into combating algorithmic bias in their management and services.
The research was sparked by the pandemic and set out to build an AI tool to predict how much extra oxygen a Covid-19 patient may need in the first days of hospital care, using data from across four continents. The technique, known as federated learning, used an algorithm to analyse chest x-rays and electronic health data from hospital patients with Covid symptoms. To maintain strict patient confidentiality, the patient data was fully anonymised and an algorithm was sent to each hospital so no data was shared or left its location. Once the algorithm had'learned' from the data, the analysis was brought together to build an AI tool which could predict the oxygen needs of hospital Covid patients anywhere in the world. Published today in Nature Medicine, the study dubbed EXAM (for EMR CXR AI Model), is one of the largest, most diverse clinical federated learning studies to date.
Addenbrooke's Hospital in Cambridge along with 20 other hospitals from across the world and healthcare technology leader, NVIDIA, have used artificial intelligence (AI) to predict Covid patients' oxygen needs on a global scale. The research was sparked by the pandemic and set out to build an AI tool to predict how much extra oxygen a Covid-19 patient may need in the first days of hospital care, using data from across four continents. The technique, known as federated learning, used an algorithm to analyse chest x-rays and electronic health data from hospital patients with Covid symptoms. To maintain strict patient confidentiality, the patient data was fully anonymised and an algorithm was sent to each hospital so no data was shared or left its location. Once the algorithm had'learned' from the data, the analysis was brought together to build an AI tool which could predict the oxygen needs of hospital Covid patients anywhere in the world.
Vaccine rates are increasing, we're starting to venture out into the world, and dating in real life has finally become an option again. The pandemic may have changed dating forever, but one thing that hasn't is that meeting people can be damn hard. Most online dating sites are a mix of both, and after living with online dating as an increasingly ubiquitous option for the past 20 years, the general public (mostly) sees dating sites as a super normal means to find casual dates or a hookup. But what if you're looking for a serious relationship that lasts? What if you just don't want to be alone on Valentine's Day ever again?
The presence of cancer of the lymphatic system is often determined by analyzing samples from the blood or bone marrow. A team led by Prof. Dr. Peter Krawitz from the University of Bonn had already shown in 2020 that artificial intelligence can help with the diagnosis of such lymphomas and leukemias. The technology fully utilizes the potential of all measurement values and increases the speed as well as the objectivity of the analyses compared to established processes. The method has now been further developed so that even smaller laboratories can benefit from this freely accessible machine learning method – an important step towards clinical practice. The study has now been published in the journal "Patterns".
An artificial intelligence (AI) model can accurately distinguish between active and healed tuberculosis on chest x-rays, according to a study published August 3 in Radiology. A team of researchers trained a deep-learning algorithm on thousands of x-rays from patients with active and healed tuberculosis and found the model accurately differentiated between the two. The model performed better than pulmonologists and as well as radiologists, which suggests it could be beneficial in countries fighting tuberculosis with poor resources and few specialists. "The network may help radiologically triage patients with active tuberculosis by excluding healed tuberculosis in high-burden countries and may assist in monitoring the activity of mycobacterial diseases that require long-term treatment," wrote a team led by Dr. Soon Ho Yoon, PhD, of Seoul National University College of Medicine. Previous studies have shown that AI models can outperform human experts in detecting tuberculosis on chest x-rays, but the networks in those studies were not trained on chest x-rays of treated patients with lung damage, so little is known about how an AI model might perform in countries with a high burden of the disease, according to the authors.
A new study has found that a material can mimic the sea slug's most essential intelligence features. The discovery is a step toward building hardware that could help make AI more efficient and reliable for technology ranging from self-driving cars and surgical robots to social media algorithms. The study, publishing this week in the Proceedings of the National Academy of Sciences, was conducted by a team of researchers from Purdue University, Rutgers University, the University of Georgia and Argonne National Laboratory. "Through studying sea slugs, neuroscientists discovered the hallmarks of intelligence that are fundamental to any organism's survival," said Shriram Ramanathan, a Purdue professor of materials engineering. "We want to take advantage of that mature intelligence in animals to accelerate the development of AI." Two main signs of intelligence that neuroscientists have learned from sea slugs are habituation and sensitization.
People around the globe have suffered the nerve-wracking anxiety of waiting weeks or months to find out if their homes have been damaged by wildfires that scorch with increased intensity. Now, once the smoke has cleared for aerial photography, researchers have found a way to identify building damage within minutes. Through a system they call DamageMap, a team at Stanford University and the California Polytechnic State University (Cal Poly) has brought an artificial intelligence approach to building assessment: Instead of comparing before-and-after photos, they've trained a program using machine learning to rely solely on post-fire images. The findings appear in the International Journal of Disaster Risk Reduction. "We wanted to automate the process and make it much faster for first responders or even for citizens that might want to know what happened to their house after a wildfire," said lead study author Marios Galanis, a graduate student in the Civil and Environmental Engineering Department at Stanford's School of Engineering.