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Artificial intelligence deep learning

William F Auffermann, Elliott K Gozansky, Srini Tridandapani
OBJECTIVE: The goal of this article is to examine some of the current cardiothoracic radiology applications of artificial intelligence in general and deep learning in particular. CONCLUSION: Artificial intelligence has been used for the analysis of medical images for decades. Recent advances in computer algorithms and hardware, coupled with the availability of larger labeled datasets, have brought about rapid advances in this field. Many of the more notable recent advances have been in the artificial intelligence subfield of deep learning...
February 19, 2019: AJR. American Journal of Roentgenology
Ryad Zemouri, Christine Devalland, Séverine Valmary-Degano, Noureddine Zerhouni
Artificial Intelligence, in particular deep neural networks are the most used machine learning technics in the biomedical field. Artificial neural networks are inspired by the biological neurons; they are interconnected and follow mathematical models. Two phases are required: a learning and a using phase. The two main applications are classification and regression Computer tools such as GPU computational accelerators or some development tools such as MATLAB libraries are used. Their application field is vast and allows the management of big data in genomics and molecular biology as well as the automated analysis of histological slides...
February 14, 2019: Annales de Pathologie
Daniel Durstewitz, Georgia Koppe, Andreas Meyer-Lindenberg
Machine and deep learning methods, today's core of artificial intelligence, have been applied with increasing success and impact in many commercial and research settings. They are powerful tools for large scale data analysis, prediction and classification, especially in very data-rich environments ("big data"), and have started to find their way into medical applications. Here we will first give an overview of machine learning methods, with a focus on deep and recurrent neural networks, their relation to statistics, and the core principles behind them...
February 15, 2019: Molecular Psychiatry
Maxim Signaevsky, Marcel Prastawa, Kurt Farrell, Nabil Tabish, Elena Baldwin, Natalia Han, Megan A Iida, John Koll, Clare Bryce, Dushyant Purohit, Vahram Haroutunian, Ann C McKee, Thor D Stein, Charles L White, Jamie Walker, Timothy E Richardson, Russell Hanson, Michael J Donovan, Carlos Cordon-Cardo, Jack Zeineh, Gerardo Fernandez, John F Crary
Accumulation of abnormal tau in neurofibrillary tangles (NFT) occurs in Alzheimer disease (AD) and a spectrum of tauopathies. These tauopathies have diverse and overlapping morphological phenotypes that obscure classification and quantitative assessments. Recently, powerful machine learning-based approaches have emerged, allowing the recognition and quantification of pathological changes from digital images. Here, we applied deep learning to the neuropathological assessment of NFT in postmortem human brain tissue to develop a classifier capable of recognizing and quantifying tau burden...
February 15, 2019: Laboratory Investigation; a Journal of Technical Methods and Pathology
Zhe Shi, Evgenii Tsymbalov, Ming Dao, Subra Suresh, Alexander Shapeev, Ju Li
Nanoscale specimens of semiconductor materials as diverse as silicon and diamond are now known to be deformable to large elastic strains without inelastic relaxation. These discoveries harbinger a new age of deep elastic strain engineering of the band structure and device performance of electronic materials. Many possibilities remain to be investigated as to what pure silicon can do as the most versatile electronic material and what an ultrawide bandgap material such as diamond, with many appealing functional figures of merit, can offer after overcoming its present commercial immaturity...
February 15, 2019: Proceedings of the National Academy of Sciences of the United States of America
Yilong Yang, Zhuyifan Ye, Yan Su, Qianqian Zhao, Xiaoshan Li, Defang Ouyang
Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error methods of pharmaceutical scientists. This approach is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of the present research is to apply deep learning methods to predict pharmaceutical formulations. In this paper, two types of dosage forms were chosen as model systems...
January 2019: Acta Pharmaceutica Sinica. B
Maxence Ernoult, Julie Grollier, Damien Querlioz
One of the biggest stakes in nanoelectronics today is to meet the needs of Artificial Intelligence by designing hardware neural networks which, by fusing computation and memory, process and learn from data with limited energy. For this purpose, memristive devices are excellent candidates to emulate synapses. A challenge, however, is to map existing learning algorithms onto a chip: for a physical implementation, a learning rule should ideally be tolerant to the typical intrinsic imperfections of such memristive devices, and local...
February 12, 2019: Scientific Reports
Daniel Fisch, Artur Yakimovich, Barbara Clough, Joseph Wright, Monique Bunyan, Michael Howell, Jason Mercer, Eva Frickel
For image-based infection biology, accurate unbiased quantification of host-pathogen interactions is essential, yet often performed manually or using limited enumeration employing simple image analysis algorithms based on image segmentation. Host protein recruitment to pathogens is often refractory to accurate automated assessment due to its heterogeneous nature. An intuitive intelligent image analysis program to assess host protein recruitment within general cellular pathogen defense is lacking. We present HRMAn (Host Response to Microbe Analysis), an open-source image analysis platform based on machine learning algorithms and deep learning...
February 12, 2019: ELife
Huiying Liang, Brian Y Tsui, Hao Ni, Carolina C S Valentim, Sally L Baxter, Guangjian Liu, Wenjia Cai, Daniel S Kermany, Xin Sun, Jiancong Chen, Liya He, Jie Zhu, Pin Tian, Hua Shao, Lianghong Zheng, Rui Hou, Sierra Hewett, Gen Li, Ping Liang, Xuan Zang, Zhiqi Zhang, Liyan Pan, Huimin Cai, Rujuan Ling, Shuhua Li, Yongwang Cui, Shusheng Tang, Hong Ye, Xiaoyan Huang, Waner He, Wenqing Liang, Qing Zhang, Jianmin Jiang, Wei Yu, Jianqun Gao, Wanxing Ou, Yingmin Deng, Qiaozhen Hou, Bei Wang, Cuichan Yao, Yan Liang, Shu Zhang, Yaou Duan, Runze Zhang, Sarah Gibson, Charlotte L Zhang, Oulan Li, Edward D Zhang, Gabriel Karin, Nathan Nguyen, Xiaokang Wu, Cindy Wen, Jie Xu, Wenqin Xu, Bochu Wang, Winston Wang, Jing Li, Bianca Pizzato, Caroline Bao, Daoman Xiang, Wanting He, Suiqin He, Yugui Zhou, Weldon Haw, Michael Goldbaum, Adriana Tremoulet, Chun-Nan Hsu, Hannah Carter, Long Zhu, Kang Zhang, Huimin Xia
Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive electronic health record (EHR) data remains challenging. Here, we show that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physicians and unearth associations that previous statistical methods have not found. Our model applies an automated natural language processing system using deep learning techniques to extract clinically relevant information from EHRs...
February 11, 2019: Nature Medicine
Norio Nakata
Deep learning has caused a third boom of artificial intelligence and great changes of diagnostic medical imaging systems such as radiology, pathology, retinal imaging, dermatology inspection, and endoscopic diagnosis will be expected in the near future. However, various attempts and new methods of deep learning have been proposed in recent years, and their progress is extremely fast. Therefore, at the initial stage when medical artificial intelligence papers were published, the artificial intelligence technology itself may be old technology or well-known general-purpose common technology...
January 31, 2019: Japanese Journal of Radiology
Dong Nie, Junfeng Lu, Han Zhang, Ehsan Adeli, Jun Wang, Zhengda Yu, LuYan Liu, Qian Wang, Jinsong Wu, Dinggang Shen
High-grade gliomas are the most aggressive malignant brain tumors. Accurate pre-operative prognosis for this cohort can lead to better treatment planning. Conventional survival prediction based on clinical information is subjective and could be inaccurate. Recent radiomics studies have shown better prognosis by using carefully-engineered image features from magnetic resonance images (MRI). However, feature engineering is usually time consuming, laborious and subjective. Most importantly, the engineered features cannot effectively encode other predictive but implicit information provided by multi-modal neuroimages...
January 31, 2019: Scientific Reports
Y P Zhou, S Li, X X Zhang, Z D Zhang, Y X Gao, L Ding, Y Lu
Objective: To investigate the clinical significance of high definition (HD) MRI rectal lymph node aided diagnostic system based on deep neural network. Methods: The research selected 301 patients with rectal cancer who underwent pelvic HD MRI and reported pelvic lymph node metastasis from July 2016 to December 2017 in Affiliated Hospital of Qingdao University. According to the chronological order, the first 201 cases were used as learning group. The remaining 100 cases were used as verification group. There were 149 males (74...
February 1, 2019: Zhonghua Wai Ke za Zhi [Chinese Journal of Surgery]
Jialiang Li, Ming Gao, Ralph D'Agostino
Deep learning neural network models such as multilayer perceptron (MLP) and convolutional neural network (CNN) are novel and attractive artificial intelligence computing tools. However, evaluation of the performance of these methods is not readily available for practitioners yet. We provide a tutorial for evaluating classification accuracy for various state-of-the-art learning approaches, including familiar shallow and deep learning methods. For qualitative response variables with more than two categories, many traditional accuracy measures such as sensitivity, specificity, and area under the receiver operating characteristic curve are not applicable and we have to consider their extensions properly...
January 30, 2019: Statistics in Medicine
Haibin Yu, Guoxiong Pan, Mian Pan, Chong Li, Wenyan Jia, Li Zhang, Mingui Sun
Recently, egocentric activity recognition has attracted considerable attention in the pattern recognition and artificial intelligence communities because of its wide applicability in medical care, smart homes, and security monitoring. In this study, we developed and implemented a deep-learning-based hierarchical fusion framework for the recognition of egocentric activities of daily living (ADLs) in a wearable hybrid sensor system comprising motion sensors and cameras. Long short-term memory (LSTM) and a convolutional neural network are used to perform egocentric ADL recognition based on motion sensor data and photo streaming in different layers, respectively...
January 28, 2019: Sensors
Jia Xu, Pengwei Yang, Shang Xue, Bhuvan Sharma, Marta Sanchez-Martin, Fang Wang, Kirk A Beaty, Elinor Dehan, Baiju Parikh
In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine...
January 22, 2019: Human Genetics
Tomonori Aoki, Atsuo Yamada, Kazuharu Aoyama, Hiroaki Saito, Akiyoshi Tsuboi, Ayako Nakada, Ryota Niikura, Mitsuhiro Fujishiro, Shiro Oka, Soichiro Ishihara, Tomoki Matsuda, Shinji Tanaka, Kazuhiko Koike, Tomohiro Tada
BACKGROUND AND AIMS: Although erosions and ulcerations are the most common small-bowel abnormalities found on wireless capsule endoscopy (WCE), a computer-aided detection method has not been established. We aimed to develop an artificial intelligence system with deep learning to automatically detect erosions and ulcerations in WCE images. METHODS: We trained a deep convolutional neural network (CNN) system based on a Single Shot Multibox Detector, using 5360 WCE images of erosions and ulcerations...
February 2019: Gastrointestinal Endoscopy
Wenjing Ye, Wen Gu, Xuejun Guo, Ping Yi, Yishuang Meng, Fengfeng Han, Lingwei Yu, Yi Chen, Guorui Zhang, Xueting Wang
BACKGROUND: A deep learning computer artificial intelligence system is helpful for early identification of ground glass opacities (GGOs). METHODS: Images from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database were used in AlexNet and GoogLeNet to detect pulmonary nodules, and 221 GGO images provided by Xinhua Hospital were used in ResNet50 for detecting GGOs. We used computed tomography image radial reorganization to create the input image of the three-dimensional features, and used the extracted features for deep learning, network training, testing, and analysis...
January 22, 2019: Biomedical Engineering Online
Yiming Gao, Krzysztof J Geras, Alana A Lewin, Linda Moy
OBJECTIVE: The purpose of this article is to compare traditional versus machine learning-based computer-aided detection (CAD) platforms in breast imaging with a focus on mammography, to underscore limitations of traditional CAD, and to highlight potential solutions in new CAD systems under development for the future. CONCLUSION: CAD development for breast imaging is undergoing a paradigm shift based on vast improvement of computing power and rapid emergence of advanced deep learning algorithms, heralding new systems that may hold real potential to improve clinical care...
February 2019: AJR. American Journal of Roentgenology
De-Kuang Hwang, Chih-Chien Hsu, Kao-Jung Chang, Daniel Chao, Chuan-Hu Sun, Ying-Chun Jheng, Aliaksandr A Yarmishyn, Jau-Ching Wu, Ching-Yao Tsai, Mong-Lien Wang, Chi-Hsien Peng, Ke-Hung Chien, Chung-Lan Kao, Tai-Chi Lin, Lin-Chung Woung, Shih-Jen Chen, Shih-Hwa Chiou
Artificial intelligence (AI) based on convolutional neural networks (CNNs) has a great potential to enhance medical workflow and improve health care quality. Of particular interest is practical implementation of such AI-based software as a cloud-based tool aimed for telemedicine, the practice of providing medical care from a distance using electronic interfaces. Methods: In this study, we used a dataset of labeled 35,900 optical coherence tomography (OCT) images obtained from age-related macular degeneration (AMD) patients and used them to train three types of CNNs to perform AMD diagnosis...
2019: Theranostics
Mehmet U Dalmiş, Albert Gubern-Mérida, Suzan Vreemann, Peter Bult, Nico Karssemeijer, Ritse Mann, Jonas Teuwen
OBJECTIVES: We investigated artificial intelligence (AI)-based classification of benign and malignant breast lesions imaged with a multiparametric breast magnetic resonance imaging (MRI) protocol with ultrafast dynamic contrast-enhanced MRI, T2-weighted, and diffusion-weighted imaging with apparent diffusion coefficient mapping. MATERIALS AND METHODS: We analyzed 576 lesions imaged with MRI, including a consecutive set of biopsied malignant (368) and benign (149) lesions, and an additional set of 59 benign lesions proven by follow-up...
January 15, 2019: Investigative Radiology
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