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Artificial intelligence deep learning

Jouha Min, Hyungsoon Im, Matthew Allen, Phillip J McFarland, Ismail Degani, Hojeong Yu, Erica Normandin, Divya Pathania, Jaymin Patel, Cesar M Castro, Ralph Weissleder, Hakho Lee
The global burden of cancer, severe diagnostic bottlenecks in underserved regions, and underfunded health care systems are fueling the need for inexpensive, rapid and treatment-informative diagnostics. Based on advances in computational optics and deep learning, we have developed a low-cost digital system, termed AIDA (artificial intelligence diffraction analysis), for breast cancer diagnosis of fine needle aspirates. Here, we show high accuracy (>90%) in (i) recognizing cells directly from diffraction patterns and (ii) classifying breast cancer types using deep-learning based analysis of sample aspirates...
August 16, 2018: ACS Nano
Faisal Mahmood, Richard Chen, Sandra Sudarsky, Daphne Yu, Nicholas J Durr
Deep learning has emerged as a powerful artificial intelligence tool to interpret medical images for a growing variety of applications. However, the paucity of medical imaging data with high-quality annotations that is necessary for training such methods ultimately limits their performance. Medical data is challenging to acquire due to privacy issues, shortage of experts available for annotation, limited representation of rare conditions and cost. This problem has previously been addressed by using synthetically generated data...
August 16, 2018: Physics in Medicine and Biology
Filippo Pesapane, Caterina Volonté, Marina Codari, Francesco Sardanelli
Worldwide interest in artificial intelligence (AI) applications is growing rapidly. In medicine, devices based on machine/deep learning have proliferated, especially for image analysis, presaging new significant challenges for the utility of AI in healthcare. This inevitably raises numerous legal and ethical questions. In this paper we analyse the state of AI regulation in the context of medical device development, and strategies to make AI applications safe and useful in the future. We analyse the legal framework regulating medical devices and data protection in Europe and in the United States, assessing developments that are currently taking place...
August 15, 2018: Insights Into Imaging
Seonho Kim, Jungjoon Kim, Hong-Woo Chun
Interest in research involving health-medical information analysis based on artificial intelligence, especially for deep learning techniques, has recently been increasing. Most of the research in this field has been focused on searching for new knowledge for predicting and diagnosing disease by revealing the relation between disease and various information features of data. These features are extracted by analyzing various clinical pathology data, such as EHR (electronic health records), and academic literature using the techniques of data analysis, natural language processing, etc...
August 15, 2018: International Journal of Environmental Research and Public Health
Peter Savadjiev, Jaron Chong, Anthony Dohan, Maria Vakalopoulou, Caroline Reinhold, Nikos Paragios, Benoit Gallix
The recent explosion of 'big data' has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship applications has, to some extent, masked decades-long advances in computational technology development for medical image analysis. In this article, we provide an overview of the history of AI methods for radiological image analysis in order to provide a context for the latest developments...
August 13, 2018: European Radiology
Jeffrey De Fauw, Joseph R Ledsam, Bernardino Romera-Paredes, Stanislav Nikolov, Nenad Tomasev, Sam Blackwell, Harry Askham, Xavier Glorot, Brendan O'Donoghue, Daniel Visentin, George van den Driessche, Balaji Lakshminarayanan, Clemens Meyer, Faith Mackinder, Simon Bouton, Kareem Ayoub, Reena Chopra, Dominic King, Alan Karthikesalingam, Cían O Hughes, Rosalind Raine, Julian Hughes, Dawn A Sim, Catherine Egan, Adnan Tufail, Hugh Montgomery, Demis Hassabis, Geraint Rees, Trevor Back, Peng T Khaw, Mustafa Suleyman, Julien Cornebise, Pearse A Keane, Olaf Ronneberger
The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital...
August 13, 2018: Nature Medicine
Yunyi Wu, Guanyu Wang
Toxicity prediction is very important to public health. Among its many applications, toxicity prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials, because a lot of drug evaluations (cellular, animal, and clinical) can be spared due to the predicted toxicity. In the era of Big Data and artificial intelligence, toxicity prediction can benefit from machine learning, which has been widely used in many fields such as natural language processing, speech recognition, image recognition, computational chemistry, and bioinformatics, with excellent performance...
August 10, 2018: International Journal of Molecular Sciences
Hailong Li, Nehal A Parikh, Lili He
Early diagnosis remains a significant challenge for many neurological disorders, especially for rare disorders where studying large cohorts is not possible. A novel solution that investigators have undertaken is combining advanced machine learning algorithms with resting-state functional Magnetic Resonance Imaging to unveil hidden pathological brain connectome patterns to uncover diagnostic and prognostic biomarkers. Recently, state-of-the-art deep learning techniques are outperforming traditional machine learning methods and are hailed as a milestone for artificial intelligence...
2018: Frontiers in Neuroscience
Ursula Schmidt-Erfurth, Amir Sadeghipour, Bianca S Gerendas, Sebastian M Waldstein, Hrvoje Bogunović
Major advances in diagnostic technologies are offering unprecedented insight into the condition of the retina and beyond ocular disease. Digital images providing millions of morphological datasets can fast and non-invasively be analyzed in a comprehensive manner using artificial intelligence (AI). Methods based on machine learning (ML) and particularly deep learning (DL) are able to identify, localize and quantify pathological features in almost every macular and retinal disease. Convolutional neural networks thereby mimic the path of the human brain for object recognition through learning of pathological features from training sets, supervised ML, or even extrapolation from patterns recognized independently, unsupervised ML...
August 1, 2018: Progress in Retinal and Eye Research
Chen Wang, Pan Xu, Luyu Zhang, Jing Huang, Kongkai Zhu, Cheng Luo
Since Human Genome Project (HGP) revealed the heterogeneity of individuals, precision medicine that proposes the customized healthcare has become an intractable and hot research. Meanwhile, as the Precision Medicine Initiative launched, precision drug design which aims at maximizing therapeutic effects while minimizing undesired side effects for an individual patient has entered a new stage. One of the key strategies of precision drug design is target based drug design. Once a key pathogenic target is identified, rational drug design which constitutes the major part of precision drug design can be performed...
2018: Frontiers in Pharmacology
Feisheng Zhong, Jing Xing, Xutong Li, Xiaohong Liu, Zunyun Fu, Zhaoping Xiong, Dong Lu, Xiaolong Wu, Jihui Zhao, Xiaoqin Tan, Fei Li, Xiaomin Luo, Zhaojun Li, Kaixian Chen, Mingyue Zheng, Hualiang Jiang
Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology, the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials. Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence (AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening, activity scoring, quantitative structure-activity relationship (QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity (ADME/T) properties...
July 18, 2018: Science China. Life Sciences
Benjamin Sanchez-Lengeling, Alán Aspuru-Guzik
The discovery of new materials can bring enormous societal and technological progress. In this context, exploring completely the large space of potential materials is computationally intractable. Here, we review methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality. Recent advances from the rapidly growing field of artificial intelligence, mostly from the subfield of machine learning, have resulted in a fertile exchange of ideas, where approaches to inverse molecular design are being proposed and employed at a rapid pace...
July 27, 2018: Science
M Jäger, C Mayer, H Hefter, M Siebler, A Kecskeméthy
The digitalization in medicine has led to almost universal availability of information to different healthcare professionals and accelerated clinical pathways. Fast-track concepts and short hospital stays require intelligent and practicable systems in preventive and rehabilitation medicine. This includes optimization of movement analysis by innovative tools such as detectors sensing skin movements, portable feedback systems for monitoring, robot-assisted devices, and prevention programs based on reliable data...
July 23, 2018: Der Orthopäde
Farnaz Hoseini, Asadollah Shahbahrami, Peyman Bayat
Deep learning is one of the subsets of machine learning that is widely used in artificial intelligence (AI) field such as natural language processing and machine vision. The deep convolution neural network (DCNN) extracts high-level concepts from low-level features and it is appropriate for large volumes of data. In fact, in deep learning, the high-level concepts are defined by low-level features. Previously, in optimization algorithms, the accuracy achieved for network training was less and high-cost function...
July 23, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
Jonathon Stewart, Peter Sprivulis, Girish Dwivedi
Interest in artificial intelligence (AI) research has grown rapidly over the past few years, in part thanks to the numerous successes of modern machine learning techniques such as deep learning, the availability of large datasets and improvements in computing power. AI is proving to be increasingly applicable to healthcare and there is a growing list of tasks where algorithms have matched or surpassed physician performance. Despite the successes there remain significant concerns and challenges surrounding algorithm opacity, trust and patient data security...
July 16, 2018: Emergency Medicine Australasia: EMA
Joeky Tamba Senders, Maya Harary, Brittany Morgan Stopa, Patrick Staples, Marike Lianne Daphne Broekman, Timothy Richard Smith, William Brian Gormley, Omar Arnaout
Glioma constitutes the most common type of primary brain tumor with a dismal survival, often measured in terms of months or years. The thin line between treatment effectiveness and patient harm underpins the importance of tailoring clinical management to the individual patient. Randomized trials have laid the foundation for many neuro-oncological guidelines. Despite this, their findings focus on group-level estimates. Given our current tools, we are limited in our ability to guide patients on what therapy is best for them as individuals, or even how long they should expect to survive...
2018: Computational and Mathematical Methods in Medicine
Qiaokang Liang, Yang Nan, Gianmarc Coppola, Kunlin Zou, Wei Sun, Dan Zhang, Guanzhen Yu
Recent advances in deep learning have produced encouraging results for biomedical image segmentation; however, outcomes rely heavily on comprehensive annotation. In this paper, we propose a neural network architecture and a new algorithm, known as overlapped region forecast, for the automatic segmentation of gastric cancer images. To the best of our knowledge, this report describes the first time that deep learning has been applied to the segmentation of gastric cancer images. Moreover, a reiterative learning framework that achieves superior performance without pre-training or further manual annotation is presented to train a simple network on weakly annotated biomedical images...
June 25, 2018: IEEE Journal of Biomedical and Health Informatics
Omar Costilla Reyes, Ruben Vera-Rodriguez, Patricia Scully, Krikor B Ozanyan
Human footsteps can provide a unique behavioural pattern for robust biometric systems. We propose spatio-temporal footstep representations from floor-only sensor data in advanced computational models for automatic biometric verification. Our models deliver an artificial intelligence capable of effectively differentiating the fine-grained variability of footsteps between legitimate users (clients) and impostor users of the biometric system. The methodology is validated in the largest to date footstep database, containing nearly 20,000 footstep signals from more than 120 users...
January 30, 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Dongbin Zhao, Derong Liu, F L Lewis, Jose C Principe, Stefanoi Squartin
The sixteen papers in this special section focus on deep reinforcement learning and adaptive dynamic programming (deep RL/ADP). Deep RL is able to output control signal directly based on input images, which incorporates both the advantages of the perception of deep learning (DL) and the decision making of RL or adaptive dynamic programming (ADP). This mechanism makes the artificial intelligence much closer to human thinking modes. Deep RL/ADP has achieved remarkable success in terms of theory and applications since it was proposed...
May 3, 2018: IEEE Transactions on Neural Networks and Learning Systems
Hirotaka Nakashima, Hiroshi Kawahira, Hiroshi Kawachi, Nobuhiro Sakaki
Background: Deep learning is a type of artificial intelligence (AI) that imitates the neural network in the brain. We generated an AI to diagnose Helicobacter pylori ( H. pylori ) infection using blue laser imaging (BLI)-bright and linked color imaging (LCI). The aim of this pilot study was to establish an AI diagnosing system that predicts H. pylori infection status using endoscopic images to improve the accuracy and productivity of endoscopic examination. Methods: A total of 222 enrolled subjects (105 H...
July 2018: Annals of Gastroenterology: Quarterly Publication of the Hellenic Society of Gastroenterology
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