Artificial Intelligence in Medicine | Page 2

Maria Del Mar Vila, Beatriz Remeseiro, Maria Grau, Roberto Elosua, Àngels Betriu, Elvira Fernandez-Giraldez, Laura Igual
BACKGROUND AND OBJECTIVE: The measurement of carotid intima media thickness (CIMT) in ultrasound images can be used to detect the presence of atherosclerotic plaques. Usually, the CIMT estimation strategy is semi-automatic, since it requires: (1) a manual examination of the ultrasound image for the localization of a region of interest (ROI), a fast and useful operation when only a small number of images need to be measured; and (2) an automatic delineation of the CIM region within the ROI...
March 2020: Artificial Intelligence in Medicine
Junchi Zhang, Mengchi Liu, Yue Zhang
As a crucial step of biological event extraction, event trigger identification has attracted much attention in recent years. Deep representation methods, which have the superiorities of less feature engineering and end-to-end training, show better performance than statistical methods. While most deep learning methods have been done on sentence-level event extraction, there are few works taking document context into account, losing potentially informative knowledge that is beneficial for trigger detection. In this paper, we propose a variational neural approach for biomedical event extraction, which can take advantage of latent topics underlying documents...
March 2020: Artificial Intelligence in Medicine
Jingfeng Chen, Leilei Sun, Chonghui Guo, Yanming Xie
OBJECTIVE: Electronic Medical Records (EMRs) contain temporal and heterogeneous doctor order information that can be used for treatment pattern discovery. Our objective is to identify "right patient", "right drug", "right dose", "right route", and "right time" from doctor order information. METHODS: We propose a fusion framework to extract typical treatment patterns based on multi-view similarity Network Fusion (SNF) method...
March 2020: Artificial Intelligence in Medicine
Gabriele Piantadosi, Mario Sansone, Roberta Fusco, Carlo Sansone
Nowadays, Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated to be a valid complementary diagnostic tool for early detection and diagnosis of breast cancer. However, without a CAD (Computer Aided Detection) system, manual DCE-MRI examination can be difficult and error-prone. The early stage of breast tissue segmentation, in a typical CAD, is crucial to increase reliability and reduce the computational effort by reducing the number of voxels to analyze and removing foreign tissues and air...
March 2020: Artificial Intelligence in Medicine
Jingchi Jiang, Huanzheng Wang, Jing Xie, Xitong Guo, Yi Guan, Qiubin Yu
The representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models. Although symbolic representation enables quantitative reasoning with statistical probability, it is difficult to utilize with machine learning models as they perform numerical operations. In contrast, knowledge embedding (i.e., high-dimensional and continuous vectors) is a feasible approach to complex reasoning that can not only retain the semantic information of knowledge, but also establish the quantifiable relationship among embeddings...
March 2020: Artificial Intelligence in Medicine
B Savelli, A Bria, M Molinara, C Marrocco, F Tortorella
In this paper, we propose a novel method for the detection of small lesions in digital medical images. Our approach is based on a multi-context ensemble of convolutional neural networks (CNNs), aiming at learning different levels of image spatial context and improving detection performance. The main innovation behind the proposed method is the use of multiple-depth CNNs, individually trained on image patches of different dimensions and then combined together. In this way, the final ensemble is able to find and locate abnormalities on the images by exploiting both the local features and the surrounding context of a lesion...
March 2020: Artificial Intelligence in Medicine
Linfeng Li, Peng Wang, Jun Yan, Yao Wang, Simin Li, Jinpeng Jiang, Zhe Sun, Buzhou Tang, Tsung-Hui Chang, Shenghui Wang, Yuting Liu
OBJECTIVE: Medical knowledge graph (KG) is attracting attention from both academic and healthcare industry due to its power in intelligent healthcare applications. In this paper, we introduce a systematic approach to build medical KG from electronic medical records (EMRs) with evaluation by both technical experiments and end to end application examples. MATERIALS AND METHODS: The original data set contains 16,217,270 de-identified clinical visit data of 3,767,198 patients...
March 2020: Artificial Intelligence in Medicine
Natalia Iglesias, Jose M Juarez, Manuel Campos
BACKGROUND: The over-use of antibiotics in clinical domains is causing an alarming increase in bacterial resistance, thus endangering their effectiveness as regards the treatment of highly recurring severe infectious diseases. Whilst Clinical Guidelines (CGs) focus on the correct prescription of antibiotics in a narrative form, Clinical Decision Support Systems (CDSS) operationalize the knowledge contained in CGs in the form of rules at the point of care. Despite the efforts made to computerize CGs, there is still a gap between CGs and the myriad of rule technologies (based on different logic formalisms) that are available to implement CDSSs in real clinical settings...
March 2020: Artificial Intelligence in Medicine
Jun Guo, Xuan Yuan, Xia Zheng, Xu Pengfei, Yun Xiao, Baoying Liu
No abstract text is available yet for this article.
January 27, 2020: Artificial Intelligence in Medicine
Sofia Zahia, Maria Begoña Garcia Zapirain, Xavier Sevillano, Alejandro González, Paul J Kim, Adel Elmaghraby
Pressure injuries represent a tremendous healthcare challenge in many nations. Elderly and disabled people are the most affected by this fast growing disease. Hence, an accurate diagnosis of pressure injuries is paramount for efficient treatment. The characteristics of these wounds are crucial indicators for the progress of the healing. While invasive methods to retrieve information are not only painful to the patients but may also increase the risk of infections, non-invasive techniques by means of imaging systems provide a better monitoring of the wound healing processes without causing any harm to the patients...
January 2020: Artificial Intelligence in Medicine
Sarah Ali Abdelaziz Ismael, Ammar Mohammed, Hesham Hefny
Cancer is the second leading cause of death after cardiovascular diseases. Out of all types of cancer, brain cancer has the lowest survival rate. Brain tumors can have different types depending on their shape, texture, and location. Proper diagnosis of the tumor type enables the doctor to make the correct treatment choice and help save the patient's life. There is a high need in the Artificial Intelligence field for a Computer Assisted Diagnosis (CAD) system to assist doctors and radiologists with the diagnosis and classification of tumors...
January 2020: Artificial Intelligence in Medicine
Zina Ben Miled, Kyle Haas, Christopher M Black, Rezaul Karim Khandker, Vasu Chandrasekaran, Richard Lipton, Malaz A Boustani
Our aim is to develop a machine learning (ML) model that can predict dementia in a general patient population from multiple health care institutions one year and three years prior to the onset of the disease without any additional monitoring or screening. The purpose of the model is to automate the cost-effective, non-invasive, digital pre-screening of patients at risk for dementia. Towards this purpose, routine care data, which is widely available through Electronic Medical Record (EMR) systems is used as a data source...
January 2020: Artificial Intelligence in Medicine
Douglas M Rocha, Lourdes M Brasil, Janice M Lamas, Glécia V S Luz, Simônides S Bacelar
The structured report is a new trend for the preparation and manipulation of radiological examination reports. The structuring of the radiological report data can bring many benefits and advantages over other existing methodologies. Research and studies about the structured radiological report are highly relevant in clinical and academic subjects, improving medical practice, reducing unobserved problems by radiologists, improving reporting practices and medical diagnoses. Exposing the benefits, advantages and potential of the structured radiological report is important in encouraging the acceptance and implementation of this method by radiology professionals who are still somewhat resistant...
January 2020: Artificial Intelligence in Medicine
Jakub Nalepa, Pablo Ribalta Lorenzo, Michal Marcinkiewicz, Barbara Bobek-Billewicz, Pawel Wawrzyniak, Maksym Walczak, Michal Kawulok, Wojciech Dudzik, Krzysztof Kotowski, Izabela Burda, Bartosz Machura, Grzegorz Mrukwa, Pawel Ulrych, Michael P Hayball
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumors. Although manual DCE biomarker extraction algorithms boost the diagnostic yield of DCE-MRI by providing quantitative information on tumor prognosis and prediction, they are time-consuming and prone to human errors. In this paper, we propose a fully-automated, end-to-end system for DCE-MRI analysis of brain tumors. Our deep learning-powered technique does not require any user interaction, it yields reproducible results, and it is rigorously validated against benchmark and clinical data...
January 2020: Artificial Intelligence in Medicine
Cheng-Hong Yang, Li-Yeh Chuang, Yu-Da Lin
OBJECTIVE: Epistasis identification is critical for determining susceptibility to human genetic diseases. The rapid development of technology has enabled scalability to make multifactor dimensionality reduction (MDR) measurements an effective calculation tool that achieves superior detection. However, the classification of high-risk (H) or low-risk (L) groups in multidrug resistance operations calls for extensive research. METHODS AND MATERIAL: In this study, an improved fuzzy sigmoid (FS) method using the membership degree in MDR (FSMDR) was proposed for solving the limitations of binary classification...
January 2020: Artificial Intelligence in Medicine
Mourad Sarrouti, Said Ouatik El Alaoui
BACKGROUND AND OBJECTIVE: Question answering (QA), the identification of short accurate answers to users questions written in natural language expressions, is a longstanding issue widely studied over the last decades in the open-domain. However, it still remains a real challenge in the biomedical domain as the most of the existing systems support a limited amount of question and answer types as well as still require further efforts in order to improve their performance in terms of precision for the supported questions...
January 2020: Artificial Intelligence in Medicine
Xu Xiaoxiao, Luo Bin, S Ramkumar, S Saravanan, M Sundar Prakash Balaji, S Dhanasekaran, J Thimmiaraja
Due to growth in population, Individual persons with disabilities are increasing daily. To overcome the disability especially in Locked in State (LIS) due to Spinal Cord Injury (SCI), we planned to design four states moving robot from four imagery tasks signals acquired from three electrode systems by placing the electrodes in three positions namely T1, T3 and FP1. At the time of the study we extract the features from Continuous Wavelet Transform (CWT) and trained with Optimized Neural Network model to analyze the features...
January 2020: Artificial Intelligence in Medicine
Geer Teng, Yue He, Hengjun Zhao, Dunhu Liu, Jin Xiao, S Ramkumar
Today's life assistive devices were playing significant role in our life to communicate with others. In that modality Human Computer Interface (HCI) based Electrooculogram (EOG) playing vital part. By using this method we can able to overcome the conventional methods in terms of performance and accuracy. To overcome such problem we analyze the EOG signal from twenty subjects to design nine states EOG based HCI using five electrodes system to measure the horizontal and vertical eye movements. Signals were preprocessed to remove the artifacts and extract the valuable information from collected data by using band power and Hilbert Huang Transform (HHT) and trained with Pattern Recognition Neural Network (PRNN) to classify the tasks...
January 2020: Artificial Intelligence in Medicine
Yaxian Hu, Senlin Luo, Longfei Han, Limin Pan, Tiemei Zhang
Deep Neural Network (DNN), as a deep architectures, has shown excellent performance in classification tasks. However, when the data has different distributions or contains some latent non-observed factors, it is difficult for DNN to train a single model to perform well on the classification tasks. In this paper, we propose mixture model based on DNNs (MoNNs), a supervised approach to perform classification tasks with a gating network and multiple local expert models. We use a neural network as a gating function and use DNNs as local expert models...
January 2020: Artificial Intelligence in Medicine
Yu Sun, Li Wang, Zhongliang Jiang, Bing Li, Ying Hu, Wei Tian
The decompressive laminectomy is a common operation for treatment of lumbar spinal stenosis. The tools for grinding and drilling are used for fenestration and internal fixation, respectively. The state recognition is one of the main technologies in robot-assisted surgery, especially in tele-surgery, because surgeons have limited perception during remote-controlled robot-assisted surgery. The novelty of this paper is that a state recognition system is proposed for the robot-assisted tele-surgery. By combining the learning methods and traditional methods, the robot from the slave-end can think about the current operation state like a surgeon, and provide more information and decision suggestions to the master-end surgeon, which aids surgeons work safer in tele-surgery...
January 2020: Artificial Intelligence in Medicine
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