After severe brain injury, one of the key challenges for medical doctors is to determine the patient’s prognosis. Who will do well? Who will not do well? Physicians need to know this, and families need to do this too, to address choices regarding the continuation of life supporting therapies. However, current prognostication methods are insufficient to provide a reliable prognosis. -/- Functional Magnetic Resonance Imaging (MRI) holds considerable promise for improving the accuracy of prognosis in acute brain (...) injury patients. Nonetheless, research on functional MRI in the intensive care unit context is ethically challenging. These studies raise several ethical issues that have not been addressed so far. In this article, Prof. Charles Weijer and his co-workers provide a framework for researchers and ethics committees to design and review these studies in an ethically sound way. (shrink)

Delicate Tissue Tumors (STT) are a type of sarcoma found in tissues that interface, backing, and encompass body structures. Due to their shallow recurrence in the body and their extraordinary variety, they seem, by all accounts, to be heterogeneous when seen through Magnetic Resonance Imaging (MRI). They are effortlessly mistaken for different infections, for example, fibro adenoma mammae, lymphadenopathy, and struma nodosa, and these indicative blunders have an extensive unfavorable impact on the clinical treatment cycle of patients. (...) Analysts have proposed a few AI models to characterize cancers, however none have sufficiently tended to this misdiagnosis issue. Likewise, comparative investigations that have proposed models for assessment of such cancers generally don't think about the heterogeneity and the size of the information. Thusly, we propose an AI based approach which joins another strategy of pre handling the information for highlights change, resampling methods to dispense with the predisposition and the deviation of precariousness and performing classifier tests in light of the and Deep learning Algorithm as Artificial brain organization. -/- Tumors (STT). (shrink)

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and reacts like humans, some of the computer activities with artificial intelligence are designed to include speech, recognition, learning, planning and problem solving. Deep learning is a collection of algorithms used in machine learning, it is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is used as a (...) technique to produce brain tumor detection and classification models using Magnetic Resonance Imaging (MRI) imaging for rapid and easy detection and identification of brain tumor. In this thesis, some ways and mechanisms will be reviewed to use deep learning techniques to produce a model for brain tumor detection. The goal is to find a good and effective way to detect brain tumor based on MRI to help the brain doctor in making decisions easily, accurately and rapidly. A recent report by the World Health Organization in February 2018 showed that the death rate from brain cancer or central nervous system (CNS) is the highest in the Asian continent. It is important to detect cancer early so that many of these lives can be saved. The model has been designed and implemented, including a dataset which consist of 10,000 images for brain tumor detection through the use of Deep learning algorithms based on neural networks. For testing, we have used our model, Inception, VGG16, MobileNet and ResNet models. The f-score accuracy we got for each model was as follows: Our model was 98.28, VGG16 was 99.86%, ResNet50 was 98.14%, MobileNet was 88,98%, and InceptionV3 was 99.88%. (shrink)

Subjective perception of sleep is not necessarily consistent with electroencephalography (EEG) indications of sleep. The mismatch between subjective reports and objective measures is often referred to as “sleep state misperception.” Previous studies evince that this mismatch is found in both patients with insomnia and in normal sleepers, but the neurophysiological mechanism remains unclear. The aim of the study is to explore the neurophysiological basis of this mechanism, from the perspective of both EEG power and functional magnetic resonance (...) class='Hi'>imaging (fMRI) fluctuations. Thirty-six healthy young adults participated in the study. Simultaneous EEG and fMRI recordings were conducted while the participants were trying to fall asleep in an MRI scanner at approximately 9:00 pm. They were awakened after achieving stable N1 or N2 sleep, or after 90 min without falling into stable sleep. Next they were asked to recall their conscious experiences from the moment immediately prior to awakening. Sixty-one instances of scheduled awakenings were collected: 21 of these after having achieved stable stage N2 sleep; 12, during stage N1 sleep; and, 20 during the waking state. Relative to those awakenings without subjective–objective discrepancy (n = 27), these awakenings with discrepancy (n = 14) were associated with lower θ power, as well as higher α, β, and γ power. Moreover, we found that participants who exhibited the discrepancy, compared with those who did not, evinced a higher amplitude of low-frequency fluctuation levels in the prefrontal cortex. These results lend support to the conjecture that the subjective–objective discrepancy is associated with central nervous system hyperarousal. (shrink)