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Artificial intelligence deep learning

Dipendra Jha, Logan Ward, Arindam Paul, Wei-Keng Liao, Alok Choudhary, Chris Wolverton, Ankit Agrawal
Conventional machine learning approaches for predicting material properties from elemental compositions have emphasized the importance of leveraging domain knowledge when designing model inputs. Here, we demonstrate that by using a deep learning approach, we can bypass such manual feature engineering requiring domain knowledge and achieve much better results, even with only a few thousand training samples. We present the design and implementation of a deep neural network model referred to as ElemNet; it automatically captures the physical and chemical interactions and similarities between different elements using artificial intelligence which allows it to predict the materials properties with better accuracy and speed...
December 4, 2018: Scientific Reports
Andreas Menke
Mental disorders account for around one-third of disability worldwide and cause enormous personal and societal burden. Current pharmacotherapies and nonpharmacotherapies do help many patients, but there are still high rates of partial or no response, delayed effect, and unfavorable adverse effects. The current diagnostic taxonomy of mental disorders by the Diagnostic and Statistical Manual of Mental Disorders and the International Classification of Diseases relies on presenting signs and symptoms, but does not reflect evidence from neurobiological and behavioral systems...
2018: Pharmacogenomics and Personalized Medicine
Youngbae Hwang, Junseok Park, Yun Jeong Lim, Hoon Jai Chun
Unlike wired endoscopy, capsule endoscopy requires additional time for a clinical specialist to review the operation and examine the lesions. To reduce the tedious review time and increase the accuracy of medical examinations, various approaches have been reported based on artificial intelligence for computer-aided diagnosis. Recently, deep learning-based approaches have been applied to many possible areas, showing greatly improved performance, especially for image-based recognition and classification. By reviewing recent deep learning-based approaches for clinical applications, we present the current status and future direction of artificial intelligence for capsule endoscopy...
November 2018: Clinical Endoscopy
Marvin J Fritzler, Laura Martinez-Prat, May Y Choi, Michael Mahler
Precision health (PH) applied to autoimmune disease will need paradigm shifts in the use and application of autoantibodies and other biomarkers. For example, autoantibodies combined with other multi-analyte "omic" profiles will form the basis of disease prediction allowing for earlier intervention linked to disease prevention strategies, as well as earlier, effective and personalized interventions for established disease. As medical intervention moves to disease prediction and a model of "intent to PREVENT," diagnostics will include an early symptom/risk-based, as opposed to a disease-based approach...
2018: Frontiers in Immunology
Koichiro Yasaka, Osamu Abe
No abstract text is available yet for this article.
November 2018: PLoS Medicine
Yoshiko Ariji, Motoki Fukuda, Yoshitaka Kise, Michihito Nozawa, Yudai Yanashita, Hiroshi Fujita, Akitoshi Katsumata, Eiichiro Ariji
OBJECTIVE: Although the deep learning system has been applied to interpretation of medical images, its application to the diagnosis of cervical lymph nodes in patients with oral cancer has not yet been reported. The purpose of this study was to evaluate the performance of deep learning image classification for diagnosis of lymph node metastasis. STUDY DESIGN: The imaging data used for evaluation consisted of computed tomography (CT) images of 127 histologically proven positive cervical lymph nodes and 314 histologically proven negative lymph nodes from 45 patients with oral squamous cell carcinoma...
October 15, 2018: Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology
David Zimmer, Kevin Schneider, Frederik Sommer, Michael Schroda, Timo Mühlhaus
Targeted mass spectrometry has become the method of choice to gain absolute quantification information of high quality, which is essential for a quantitative understanding of biological systems. However, the design of absolute protein quantification assays remains challenging due to variations in peptide observability and incomplete knowledge about factors influencing peptide detectability. Here, we present a deep learning algorithm for peptide detectability prediction, d::pPop, which allows the informed selection of synthetic proteotypic peptides for the successful design of targeted proteomics quantification assays...
2018: Frontiers in Plant Science
Safwan S Halabi, Luciano M Prevedello, Jayashree Kalpathy-Cramer, Artem B Mamonov, Alexander Bilbily, Mark Cicero, Ian Pan, Lucas Araújo Pereira, Rafael Teixeira Sousa, Nitamar Abdala, Felipe Campos Kitamura, Hans H Thodberg, Leon Chen, George Shih, Katherine Andriole, Marc D Kohli, Bradley J Erickson, Adam E Flanders
Purpose The Radiological Society of North America (RSNA) Pediatric Bone Age Machine Learning Challenge was created to show an application of machine learning (ML) and artificial intelligence (AI) in medical imaging, promote collaboration to catalyze AI model creation, and identify innovators in medical imaging. Materials and Methods The goal of this challenge was to solicit individuals and teams to create an algorithm or model using ML techniques that would accurately determine skeletal age in a curated data set of pediatric hand radiographs...
November 27, 2018: Radiology
Scott Weichenthal, Marianne Hatzopoulou, Michael Brauer
BACKGROUND: Artificial intelligence (AI) is revolutionizing our world, with applications ranging from medicine to engineering. OBJECTIVES: Here we discuss the promise, challenges, and probable data sources needed to apply AI in the fields of exposure science and environmental health. In particular, we focus on the use of deep convolutional neural networks to estimate environmental exposures using images and other complementary data sources such as cell phone mobility and social media information...
November 22, 2018: Environment International
Alex Zhavoronkov, Polina Mamoshina, Quentin Vanhaelen, Morten Scheibye-Knudsen, Alexey Moskalev, Alex Aliper
The applications of modern artificial intelligence (AI) algorithms within the field of aging research offer tremendous opportunities. Aging is an almost universal unifying feature possessed by all living organisms, tissues, and cells. Modern deep learning techniques used to develop age predictors offer new possibilities for formerly incompatible dynamic and static data types. AI biomarkers of aging enable a holistic view of biological processes and allow for novel methods for building causal models-extracting the most important features and identifying biological targets and mechanisms...
November 22, 2018: Ageing Research Reviews
Viktor H Koelzer, Korsuk Sirinukunwattana, Jens Rittscher, Kirsten D Mertz
Clinical success of immunotherapy is driving the need for new prognostic and predictive assays to inform patient selection and stratification. This requirement can be met by a combination of computational pathology and artificial intelligence. Here, we critically assess computational approaches supporting the development of a standardized methodology in the assessment of immune-oncology biomarkers, such as PD-L1 and immune cell infiltrates. We examine immunoprofiling through spatial analysis of tumor-immune cell interactions and multiplexing technologies as a predictor of patient response to cancer treatment...
November 23, 2018: Virchows Archiv: An International Journal of Pathology
Shui-Hua Wang, Chaosheng Tang, Junding Sun, Jingyuan Yang, Chenxi Huang, Preetha Phillips, Yu-Dong Zhang
Aim: Multiple sclerosis is a severe brain and/or spinal cord disease. It may lead to a wide range of symptoms. Hence, the early diagnosis and treatment is quite important. Method: This study proposed a 14-layer convolutional neural network, combined with three advanced techniques: batch normalization, dropout, and stochastic pooling. The output of the stochastic pooling was obtained via sampling from a multinomial distribution formed from the activations of each pooling region. In addition, we used data augmentation method to enhance the training set...
2018: Frontiers in Neuroscience
Haik Manukian, Fabio L Traversa, Massimiliano Di Ventra
Restricted Boltzmann machines (RBMs) and their extensions, often called "deep-belief networks", are powerful neural networks that have found applications in the fields of machine learning and artificial intelligence. The standard way to train these models resorts to an iterative unsupervised procedure based on Gibbs sampling, called "contrastive divergence", and additional supervised tuning via back-propagation. However, this procedure has been shown not to follow any gradient and can lead to suboptimal solutions...
November 3, 2018: Neural Networks: the Official Journal of the International Neural Network Society
Huayi Shi, Houyu Wang, Xinyu Meng, Runzhi Chen, Yishu Zhang, Yuanyuan Su, Yao He
The quality of input data in deep learning is tightly associated with the ultimate performance of machine learner. Taking advantages of unique merits of surface-enhanced Raman scattering (SERS) methodology in the collection and construction of database (e.g., abundant intrinsic fingerprint information, noninvasive data acquisition process, strong anti-interfering ability, etc.), herein we set up SERS-based database of deoxyribonucleic acid (DNA), suitable for artificial intelligence (AI)-based sensing applications...
November 20, 2018: Analytical Chemistry
Mika Kortesniemi, Virginia Tsapaki, Annalisa Trianni, Paolo Russo, Ad Maas, Hans-Erik Källman, Marco Brambilla, John Damilakis
Big data and deep learning will profoundly change various areas of professions and research in the future. This will also happen in medicine and medical imaging in particular. As medical physicists, we should pursue beyond the concept of technical quality to extend our methodology and competence towards measuring and optimising the diagnostic value in terms of how it is connected to care outcome. Functional implementation of such methodology requires data processing utilities starting from data collection and management and culminating in the data analysis methods...
November 16, 2018: Physica Medica: PM
Yinqiu Xu, Hequan Yao, Kejiang Lin
Artificial intelligence systems based on neural networks (NNs) find rules for drug discovery according to training molecules, but first, the molecules need to be represented in certain ways. Molecular descriptors and fingerprints have been used as inputs for artificial neural networks (ANNs) for a long time, while other ways for describing molecules are used only for storing and presenting molecules. With the development of deep learning, variants of ANNs are now able to use different kinds of inputs, which provide researchers with more choices for drug discovery...
November 17, 2018: Expert Opinion on Drug Discovery
Soh Nishimoto, Yohei Sotsuka, Kenichiro Kawai, Hisako Ishise, Masao Kakibuchi
BACKGROUND: Cephalometric analysis has long been, and still is one of the most important tools in evaluating craniomaxillofacial skeletal profile. To perform this, manual tracing of x-ray film and plotting landmarks have been required. This procedure is time-consuming and demands expertise. In these days, computerized cephalometric systems have been introduced; however, tracing and plotting still have to be done on the monitor display. Artificial intelligence is developing rapidly. Deep learning is one of the most evolving areas in artificial intelligence...
November 12, 2018: Journal of Craniofacial Surgery
Kai Zhang, Xiyang Liu, Fan Liu, Lin He, Lei Zhang, Yahan Yang, Wangting Li, Shuai Wang, Lin Liu, Zhenzhen Liu, Xiaohang Wu, Haotian Lin
BACKGROUND: Although artificial intelligence performs promisingly in medicine, few automatic disease diagnosis platforms can clearly explain why a specific medical decision is made. OBJECTIVE: We aimed to devise and develop an interpretable and expandable diagnosis framework for automatically diagnosing multiple ocular diseases and providing treatment recommendations for the particular illness of a specific patient. METHODS: As the diagnosis of ocular diseases highly depends on observing medical images, we chose ophthalmic images as research material...
November 14, 2018: Journal of Medical Internet Research
Hamid Shokoohi, Maxine A LeSaux, Yusuf H Roohani, Andrew Liteplo, Calvin Huang, Michael Blaivas
Recent applications of artificial intelligence (AI) and deep learning (DL) in health care include enhanced diagnostic imaging modalities to support clinical decisions and improve patients' outcomes. Focused on using automated DL-based systems to improve point-of-care ultrasound (POCUS), we look at DL-based automation as a key field in expanding and improving POCUS applications in various clinical settings. A promising additional value would be the ability to automate training model selections for teaching POCUS to medical trainees and novice sonologists...
November 13, 2018: Journal of Ultrasound in Medicine: Official Journal of the American Institute of Ultrasound in Medicine
Shuang Wu, Guoqi Li, Lei Deng, Liu Liu, Dong Wu, Yuan Xie, Luping Shi
Batch normalization (BN) has recently become a standard component for accelerating and improving the training of deep neural networks (DNNs). However, BN brings in additional calculations, consumes more memory, and significantly slows down the training iteration. Furthermore, the nonlinear square and sqrt operations in the normalization process impede low bit-width quantization techniques, which draw much attention to the deep learning hardware community. In this paper, we propose an L1-norm BN (L1BN) with only linear operations in both forward and backward propagations during training...
November 9, 2018: IEEE Transactions on Neural Networks and Learning Systems
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