Collaborating Authors

Deep Learning, Big Data Projects Hone in Diseases


Major medical centers and technology companies recently announced new projects aimed at harnessing artificial learning to improve detection, diagnosis, treatment, and management of diseases. Google's DeepMind Health, a Silicon Valley-based artificial intelligence project, and Moorfields Eye Hospital, a leading center for eye research in London, have teamed up for a five-year project to determine if machine learning can speed up and improve diagnosis of eye diseases by developing machine learning approaches to automatically review eye scans. The deal was announced in July. NVIDIA, the Silicon Valley-based developer of graphics processing unit techniques for use in scientific, engineering, and consumer products, is collaborating with Massachusetts General Hospital in Boston to apply machine learning initially in radiology and pathology, areas rich in images and data. The organizations in April announced they eventually will expand the research into genomics and electronic health records.

Medical Image Computation and the Application


Over the past few decades, medical imaging techniques, such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), mammography, ultrasound, and X-ray, have been used for the early detection, diagnosis, and treatment of diseases. In the clinic, medical image interpretation has been performed mostly by human experts such as radiologists and physicians. However, given wide variations in pathology and the potential fatigue of human experts, researchers and doctors have begun to benefit from the machine learning methods. The process of applying machine learning methods in medical image analysis is called medical image computation. We will introduce our work in medical image synthesis, classification, and segmentation. Complementary imaging modalities are always acquired simultaneously to indicate the disease areas, present the various tissue properties, and help to make an accurate and early diagnosis.

AI in Precision Medicine in 2018


The Precision Medicine World Conference will be one of the most exciting conferences focused on AI in healthcare in 2018. CEOs of cutting edge companies from around the world will come together to discuss how they are using techniques such as computer vision, deep learning and machine learning to make big advances in medicine from drug discovery to patient diagnosis and treatment. The program will traverse innovative technologies and clinical case studies that enable the translation of precision medicine into direct improvements in health care. Attendees will have an opportunity to learn about the latest developments in Precision Medicine and cutting-edge new strategies that are changing how patients are treated.

A survey on artificial intelligence in chest imaging of COVID-19


In this review article the authors Yun Chen, Gongfa Jiang, Yue Li, Yutao Tang, Yanfang Xu, Siqi Ding, Yanqi Xin and Yao Lu from Xiangtan University, Xiangtan, China and Sun Yat-sen University, Guangzhou, China consider the application of artificial intelligence imaging analysis methods for COVID-19 clinical diagnosis. The world is facing a key health threat because of the outbreak of COVID-19. Intelligent medical imaging analysis is urgently needed to make full use of chest images in COVID- 19 diagnosis and its management due to the important role of typical imaging findings in this disease. The authors review artificial intelligence (AI) assisted chest imaging analysis methods for COVID-19 which provide accurate, fast, and safe imaging solutions. In particular, medical images from X-ray and CT scans are used to demonstrate that AI techniques based on deep learning can be applied to COVID-19 diagnosis.

Application of machine learning for hematological diagnosis Machine Learning

Quick and accurate medical diagnosis is crucial for the successful treatment of a disease. Using machine learning algorithms, we have built two models to predict a hematologic disease, based on laboratory blood test results. In one predictive model, we used all available blood test parameters and in the other a reduced set, which is usually measured upon patient admittance. Both models produced good results, with a prediction accuracy of 0.88 and 0.86, when considering the list of five most probable diseases, and 0.59 and 0.57, when considering only the most probable disease. Models did not differ significantly from each other, which indicates that a reduced set of parameters contains a relevant fingerprint of a disease, expanding the utility of the model for general practitioner's use and indicating that there is more information in the blood test results than physicians recognize. In the clinical test we showed that the accuracy of our predictive models was on a par with the ability of hematology specialists. Our study is the first to show that a machine learning predictive model based on blood tests alone, can be successfully applied to predict hematologic diseases and could open up unprecedented possibilities in medical diagnosis.