Modern technology makes it possible to sequence individual cells and to identify which genes are currently being expressed in each cell. These methods are sensitive and consequently error prone. Devices, environment and biology itself can be responsible for failures and differences between measurements. Researchers at Helmholtz Zentrum München joined forces with colleagues from the Technical University of Munich (TUM) and the British Wellcome Sanger Institute and have developed algorithms that make it possible to predict and correct such sources of error. The work was published in'Nature Methods' and'Nature Communications'.
In this profile series, we interview AI innovators on the front-lines - those who have dedicated their life's work to improving the human condition through technology advancements. His background in electrical engineering, biomedical engineering, and computer science, helps him research different methods, including AI, to improve diagnostic imaging in the development of medical devices. In addition to OneProjects, Hennersperger also works with Trinity College in Dublin, Ireland and the Technical University of Munich in Germany. OneProjects is an innovative medical device start-up founded in 2017 in Dublin and Munich. For the past two years, OneProjects has been developing VERAFEYE, a new medical device treating cardiac arrhythmias.
A three-dimensional printout of a human heart is seen at the Heidelberg University Hospital (Universitaetsklinikum Heidelberg) in Heidelberg, Germany, August 14, 2018. If artificial intelligence (AI) has a big say in what we should watch next on YouTube or any other multimedia streaming platform, then perhaps it can also recommend what we should eat or drink next--or more likely not eat or drink next--to stay in the best of health. Already the applications of AI are going beyond tips for maintaining a healthy lifestyle, and AI-powered software is becoming an integral part of some medical diagnosis procedures. Thanks to deep learning algorithms and neural networks, AI solutions have become very good at pattern recognition, which after all, lies at the core of what a human doctor does for figuring out the root cause of a patient's ailment. Doctors, in essence, examine all the symptoms a patient is exhibiting--often with the help of medical imaging, bloodwork, and pathological tests--and then compare the systems with the telltale signs of likely diseases and conditions that they have learned about as medical students or cases that they have come across in the medical literature.
In this article, we propose an approach that can make use of not only labeled EEG signals but also the unlabeled ones which is more accessible. We also suggest the use of data fusion to further improve the seizure prediction accuracy. Data fusion in our vision includes EEG signals, cardiogram signals, body temperature and time. We use the short-time Fourier transform on 28-s EEG windows as a pre-processing step. A generative adversarial network (GAN) is trained in an unsupervised manner where information of seizure onset is disregarded. The trained Discriminator of the GAN is then used as feature extractor. Features generated by the feature extractor are classified by two fully-connected layers (can be replaced by any classifier) for the labeled EEG signals. This semi-supervised seizure prediction method achieves area under the operating characteristic curve (AUC) of 77.68% and 75.47% for the CHBMIT scalp EEG dataset and the Freiburg Hospital intracranial EEG dataset, respectively. Unsupervised training without the need of labeling is important because not only it can be performed in real-time during EEG signal recording, but also it does not require feature engineering effort for each patient.
HEIDELBERG, Germany (Reuters) - Armed with a mouse and computer screen instead of a scalpel and operating theater, cardiologist Benjamin Meder carefully places the electrodes of a pacemaker in a beating, digital heart. Using this "digital twin" that mimics the electrical and physical properties of the cells in patient 7497's heart, Meder runs simulations to see if the pacemaker can keep the congestive heart failure sufferer alive - before he has inserted a knife. The digital heart twin developed by Siemens Healthineers is one example of how medical device makers are using artificial intelligence (AI) to help doctors make more precise diagnoses as medicine enters an increasingly personalized age. The challenge for Siemens Healthineers and rivals such as Philips and GE Healthcare is to keep an edge over tech giants from Alphabet's Google to Alibaba that hope to use big data to grab a slice of healthcare spending. With healthcare budgets under increasing pressure, AI tools such as the digital heart twin could save tens of thousands of dollars by predicting outcomes and avoiding unnecessary surgery.