Breakthroughs, discoveries, and DIY tips sent every weekday. There are plenty of negative stereotypes about games and gamers. And it's true that focusing on gaming to the detriment of all else will have negative effects--there's a reason that the World Health Organization recognizes video game addiction as a mental health condition. In the 50 years since Atari unleashed Pong on the world, there's been plenty of research on the effects of video games on our brains, and it's not all bad. Here are a few of the potential benefits of gaming, according to research. A research review published in American Psychologist in 2013 by Isabela Granic, Adam Lobel, and Rutger C. M. E. Engels at Radboud University in Nijmegen, the Netherlands, looked at decades of research and highlighted the various benefits found in gaming.

Researchers have found that an artificial intelligence system is at least as good as human radiologists at identifying tuberculosis from chest X-rays, opening up its use for low-resource countries. Indeed, the deep learning program was superior in sensitivity and noninferior in specificity in identifying active pulmonary TB in frontal chest radiographs when compared with nine radiologists from India. The system could have particular value in low-income countries where large-scale screening programs are not always feasible due to cost and radiologist availability. Simulations revealed that using the deep learning system to identify likely TB-positive chest radiographs for confirmation using nucleic acid amplification testing (NAAT) reduced costs by between 40 and 80 percent per positive patient detected. "We hope this can be a tool used by non-expert physicians and healthcare workers to screen people en masse and get them to treatment where required without getting specialist doctors, who are in short supply,' said researcher Rory Pilgrim, a product manager at Google Health AI in Mountain View, California. "We believe we can do this with the people on the ground in a low-cost, high-volume way." The research is published in Radiology, a journal of the Radiological Society of North America. The deep-learning system was trained using 165,754 images from 22,284 individuals, nearly all from South Africa, and then tested using data from five countries. The total test set had 1236 images, of which 212 were identified as positive for TB based on microbiological tests or NAAT. These were binary scored by 10 radiologists from India and five from the USA, although one of the Indian radiologists was removed due to their much lower specificity than the others. Among 1236 test individuals assessed, the deep learning system achieved superior sensitivity compared with a prespecified analysis involving the nine radiologists from India, at 88% versus 75%, with noninferior specificity at 79% versus 84%. "What's especially promising in this study is that we looked at a range of different datasets that reflected the breadth of TB presentation, different equipment and different clinical workflows," said co-study author Sahar Kazemzadeh, software engineer at Google Health. The AI system achieved thresholds set by the World Health Organization in 2014 as a reasonable requirement for any TB screening test in most of the data sets, noted Bram van Ginneken, a professor of medical image analysis at Radboud University Medical Center in Nijmegen, The Netherlands, in an editorial accompanying the study. Yet, he added: "It is shown that for difficult data sets, such as a mining population, whose radiographs may contain other signs of lung disease, and a subset of subjects who are HIV positive, where TB may occur without typical radiographic abnormalities, both the AI software and the human readers performed much lower.

Consider the squeak of a mouse and the low rumble of a lion's roar. In the animal kingdom, bigger animals usually produce lower pitch sounds as a result of their larger larynges and longer vocal tracts. But harbour seals seem to break that rule: they can learn how to change their calls. That means they can deliberately move between lower or higher pitch sounds and make themselves sound bigger than they really are. "The information that is in their calls is not necessarily honest," says Koen de Reus at the Max Planck Institute for Psycholinguistics in Nijmegen, Netherlands.

Lung cancer is one of the most common cancers worldwide. According to a study published in Nature called "Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography", researchers got to know that with the help of AI (artificial intelligence), lung cancer and cardiovascular health can be screened through the usage of low-dose computed tomography. This can help to reduce the risk of developing lung and cardiovascular diseases. The study was a result of a combined effort by Rensselaer Polytechnic Institute and Massachusetts General Hospital. Dr Colin Jacobs, Ph.D. assistant professor in the Department of Medical Imaging at Radboud University Medical Center in Nijmegen said "As it does not require manual interpretation of nodule imaging characteristics, the proposed algorithm may reduce the substantial interobserver variability in CT interpretation," .

According to the World Health Organization, there was an estimated 1.8 million deaths in 2020 from lung cancer. It is a leading cause of cancer death among men and women and each year, more people die of lung cancer than of colon, breast, and prostate cancers combined. However, the number of new lung cancer cases continues to decrease, due to fewer people smoking and advances in early detection and treatment. The latest advance in early lung cancer detection involves artificial intelligence (AI). Researchers from Radboud University Medical Center in Nijmegen, the Netherlands, and collaborators reported that an AI program accurately predicted the risk that lung nodules detected on screening CT will become cancerous.

Making simple up and down hand movements while speaking may influence the way people hear what you are saying. We often use meaningless movements, such as flicking or waving our hands, known as beat gestures when speaking face-to-face. These typically align with prominent words in speech. "Politicians use these gestures all the time to get their message across," says Hans Rutger Bosker at the Max Planck Institute for Psycholinguistics in Nijmegen, the Netherlands. Bosker and his colleagues tested how important these movements are in influencing sound recognition.

Deaf people who can hear again, blind people who can see, paralyzed people who can move again: the objectives of the third Nijmegen AI-lab are by no means small. By means of artificial intelligence, machine learning, and implants, the brain would not only be able to pick up the mental control of the body again but could do the same for its senses. Looking, feeling, hearing, smelling, controlled movement: these are essential functions in a person's life. Those who miss them are literally limited in their functioning. Restoring these kinds of functions is the main objective of the Donders ICAI-lab, which was opened in June at the Radboud University in Nijmegen.

Babak Ehteshami Bejnordi, from the Radboud University Medical Center in Nijmegen, Netherlands, and colleagues compared the performance of automated deep learning algorithms for detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer with pathologists' diagnoses in a diagnostic setting. The researchers found that the area under the receiver operating characteristic curve (AUC) ranged from 0.556 to 0.994 for the algorithms. The lesion-level, true-positive fraction achieved for the top-performing algorithm was comparable to that of the pathologist without a time constraint at a mean of 0.0125 false-positives per normal whole-slide image. Daniel Shu Wei Ting, M.D., Ph.D., from the Singapore National Eye Center, and colleagues assessed the performance of a DLS for detecting referable diabetic retinopathy and related eye diseases using 494,661 retinal images. The researchers found that the AUC of the DLS for referable diabetic retinopathy was 0.936, and sensitivity and specificity were 90.5 and 91.6 percent, respectively.

Bayesian methods have been successfully applied to regression and classification problems in multi-layer perceptrons. We present a novel application of Bayesian techniques to Radial Basis Function networks by developing a Gaussian approximation to the posterior distribution which, for fixed basis function widths, is analytic in the parameters. The setting of regularization constants by crossvalidation iswasteful as only a single optimal parameter estimate is retained. We treat this issue by assigning prior distributions to these constants, which are then adapted in light of the data under a simple re-estimation formula. 1 Introduction Radial Basis Function networks are popular regression and classification tools[lO]. For fixed basis function centers, RBFs are linear in their parameters and can therefore betrained with simple one shot linear algebra techniques[lO]. The use of unsupervised techniques to fix the basis function centers is, however, not generally optimal since setting the basis function centers using density estimation on the input data alone takes no account of the target values associated with that data. Ideally, therefore, we should include the target values in the training procedure[7, 3, 9]. Unfortunately, allowingcenters to adapt to the training targets leads to the RBF being a nonlinear function of its parameters, and training becomes more problematic. Most methods that perform supervised training of RBF parameters minimize the ·Present address: SNN, University of Nijmegen, Geert Grooteplein 21, Nijmegen, The Netherlands.

In contrast to the maximum likelihood approach which finds only a single estimate for the regression parameters, the Bayesian approach yields a distribution of weight parameters, p(wID), conditional on the training data D, and predictions are ex- ·Present address: SNN, University of Nijmegen, Geert Grooteplein 21, Nijmegen, The Netherlands.