Scientists have been researching how a protein folds into a unique 3D shape for approximately 50 years. Now, thanks to the use of artificial intelligence (AI), U.K.-based AI lab, DeepMind, has helped to solve this scientific mystery, as the organizers of a scientific challenge, CASP (Critical Assessment of protein Structure Prediction) said, which kicked off in the early 1990s, reports Science Alert. Understanding a protein shape could lead to major scientific advancements, as well as environmental ones, per the BBC. The full findings have not yet been published, explains Science Alert, however, the study's abstract can be read over on CASP14, here. SEE ALSO: GOOGLE'S DEEPMIND AI BETTER AT DETECTING BREAST CANCER THAN EXPERTS Proteins are integral as they are present in all living things.
Researchers at Massachusetts General Hospital (MGH) have developed a deep learning model that identifies imaging biomarkers on screening mammograms to predict a patient's risk for developing breast cancer with greater accuracy than traditional risk assessment tools. Results of the study are being presented at the annual meeting of the Radiological Society of North America (RSNA). "Traditional risk assessment models do not leverage the level of detail that is contained within a mammogram," said Leslie Lamb, M.D., M.Sc., breast radiologist at MGH. "Even the best existing traditional risk models may separate sub-groups of patients but are not as precise on the individual level." Currently available risk assessment models incorporate only a small fraction of patient data such as family history, prior breast biopsies, and hormonal and reproductive history. Only one feature from the screening mammogram itself, breast density, is incorporated into traditional models.
Researchers have developed a deep learning model that identifies imaging biomarkers on screening mammograms to predict a patient's risk for developing breast cancer with greater accuracy than traditional risk assessment tools. Traditional risk assessment models do not leverage the level of detail that is contained within a mammogram," said study author Leslie Lamb from the Massachusetts General Hospital (MGH) in the US. "Even the best existing traditional risk models may separate sub-groups of patients but are not as precise on the individual level," Lamb added. Currently available risk assessment models incorporate only a small fraction of patient data such as family history, prior breast biopsies, and hormonal and reproductive history. Only one feature from the screening mammogram itself, breast density, is incorporated into traditional models.
A deep learning computer model developed by researchers at Massachusetts General Hospital (MGH) was able to identify subtle information in breast cancer images that could help better predict a woman's chances of developing the disease. Currently, the main methods of judging individual risk include checking for a family history of cancer, evaluating any biopsied tissue and noting whether they've given birth to a child. Screening mammograms--recommended annually by the American Cancer Society for women between the ages of 45 and 54--are typically used by oncologists to measure the density of the breast. "Why should we limit ourselves to only breast density when there is such rich digital data embedded in every woman's mammogram?" said Constance Lehman, M.D., Ph.D., MGH's division chief of breast imaging and senior author of a paper presented at the annual meeting of the Radiological Society of North America. "Every woman's mammogram is unique to her just like her thumbprint," Lehman said.
This post will show you how you easily apply Stacked Ensemble Models in R using the H2O package. The models can treat both Classification and Regression problems. For this example, we will apply a classification problem using the Breast Cancer Wisconsin dataset, which can be found here. The steps below describe the individual tasks involved in training and testing a Super Learner ensemble. H2O automates most of the steps below so that you can quickly and easily build ensembles of H2O models.
Over 500,000 CT scans for the coronavirus diagnostics have been processed in Moscow using the artificial intelligence (AI) technology, Moscow Mayor Sergei Sobyanin wrote on his official page on the VKontakte social network on Wednesday. "To date, AI helped process over 500,000 CT scans for COVID diagnostics. Artificial intelligence sees the degree of lung damage, increases the quality and speed of diagnostics. This is very important with COVID-19 when the decision on treatment approaches should be made in mere hours," he wrote. The Mayor added that the capital healthcare system actively implements digital technologies that help with diagnostics and perform routine tasks.
Over the last half-decade, the term "Artificial Intelligence" (AI) has become ubiquitous in the field of healthcare technologies, with machine learning applied to clinical tasks such as radiation oncology treatment planning, breast cancer screening diagnoses and triaging patients in primary care settings based on self-reported symptoms. The onset of COVID-19 has sparked a new level of pragmatism, breaking down pre-conceptions over the near-term role of AI and seeing it brought to bear on urgent global challenges by new multi-disciplinary consortia united by a common cause. To prepare us against any future pandemics, we must use and share the experiences and lessons we've learnt from COVID-19. This report answers key questions from data scientists and engineers, features case studies where AI was used to tackle the pandemic and shares the next steps and recommendations needed to improve our health emergency planning. Putting into place new systems, faster methods of data collection and diagnosis, and supporting new product innovations are the steps we need to better equip us for future challenges.
Differential privacy is a data anonymization technique that's used by major technology companies such as Apple and Google. The goal of differential privacy is simple: allow data analysts to build accurate models without sacrificing the privacy of the individual data points. But what does "sacrificing the privacy of the data points" mean? Well, let's think about an example. Suppose I have a dataset that contains information (age, gender, treatment, marriage status, other medical conditions, etc.) about every person who was treated for breast cancer at Hospital X.
A novel material made from rotting fruit and vegetables that absorbs stray UV light from the sun and converts it into renewable energy has landed its designer the first sustainability gong in this year's James Dyson awards. From a record 1,800 entries – despite the challenges of Covid-19 – the award was given to 27-year-old Carvey Ehren Maigue, a student at Mapúa University in the Philippines, for his Aureus system which uses the natural scientific principles behind the northern lights. The other top prize in the international competition has been handed to the inventor of a low-cost biomedical device that can be used at home to detect breast cancer, harnessing artificial intelligence to analyse urine. Aureus is made from crop waste and can be attached in panels to windows and walls. It allows high energy particles derived from fruit and vegetables to be absorbed by luminescent particles, which re-emit them as visible light.
Breast cancer is sometimes found after symptoms appear. However, some women with breast cancer have no symptoms, which is why regular breast cancer screening is encouraged. Breast cancer screening with mammography has been shown to improve prognosis and reduce mortality by detecting disease at an earlier stage. However, many cancers are missed on screening mammography. Researchers now report that artificial intelligence (AI) may potentially improve reading breast cancer screening mammograms.