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Artificial Intelligence in Medicine | Page 2

Fantin Girard, Conrad Kavalec, Farida Cheriet
OBJECTIVE: Automatic artery/vein (A/V) segmentation from fundus images is required to track blood vessel changes occurring with many pathologies including retinopathy and cardiovascular pathologies. One of the clinical measures that quantifies vessel changes is the arterio-venous ratio (AVR) which represents the ratio between artery and vein diameters. This measure significantly depends on the accuracy of vessel segmentation and classification into arteries and veins. This paper proposes a fast, novel method for semantic A/V segmentation combining deep learning and graph propagation...
March 2019: Artificial Intelligence in Medicine
Damla Arifoglu, Abdelhamid Bouchachia
In recent years, there is a rapid increase in the population of elderly people. However, elderly people may suffer from the consequences of cognitive decline, which is a mental health disorder that primarily affects cognitive abilities such as learning, memory, etc. As a result, the elderly people may get dependent on caregivers to complete daily life tasks. Detecting the early indicators of dementia before it gets worsen and warning the caregivers and medical doctors would be helpful for further diagnosis...
March 2019: Artificial Intelligence in Medicine
Golnar K Mahani, Mohammad-Reza Pajoohan
Since a significant number of frequent laboratory blood tests are unnecessary and these tests may have complications, developing a system that could identify unnecessary tests is essential. In this paper, a value prediction approach is presented to predict the values of Calcium and Hematocrit laboratory blood tests for upper gastrointestinal bleeding patients and patients with unspecified hemorrhage in their gastrointestinal tract. The data have been extracted from the MIMIC-II database. By considering the issues of MIMIC-II in the process of data extraction and using expert knowledge, comprehensive preprocessing has been performed to validate the data...
March 2019: Artificial Intelligence in Medicine
Albert Comelli, Alessandro Stefano, Samuel Bignardi, Giorgio Russo, Maria Gabriella Sabini, Massimo Ippolito, Stefano Barone, Anthony Yezzi
In the context of cancer delineation using positron emission tomography datasets, we present an innovative approach which purpose is to tackle the real-time, three-dimensional segmentation task in a full, or at least nearly full automatized way. The approach comprises a preliminary initialization phase where the user highlights a region of interest around the cancer on just one slice of the tomographic dataset. The algorithm takes care of identifying an optimal and user-independent region of interest around the anomalous tissue and located on the slice containing the highest standardized uptake value so to start the successive segmentation task...
March 2019: Artificial Intelligence in Medicine
Margarita Khokhlova, Cyrille Migniot, Alexey Morozov, Olga Sushkova, Albert Dipanda
Computer vision-based clinical gait analysis is the subject of permanent research. However, there are very few datasets publicly available; hence the comparison of existing methods between each other is not straightforward. Even if the test data are in an open access, existing databases contain very few test subjects and single modality measurements, which limit their usage. The contributions of this paper are three-fold. First, we propose a new open-access multi-modal database acquired with the Kinect v.2 camera for the task of gait analysis...
March 2019: Artificial Intelligence in Medicine
Jean-Baptiste Lamy, Boomadevi Sekar, Gilles Guezennec, Jacques Bouaud, Brigitte Séroussi
Case-Based Reasoning (CBR) is a form of analogical reasoning in which the solution for a (new) query case is determined using a database of previous known cases with their solutions. Cases similar to the query are retrieved from the database, and then their solutions are adapted to the query. In medicine, a case usually corresponds to a patient and the problem consists of classifying the patient in a class of diagnostic or therapy. Compared to "black box" algorithms such as deep learning, the responses of CBR systems can be justified easily using the similar cases as examples...
March 2019: Artificial Intelligence in Medicine
M Saifur Rahman, Md Khaledur Rahman, Sanjay Saha, M Kaykobad, M Sohel Rahman
An antigen is a protein capable of triggering an effective immune system response. Protective antigens are the ones that can invoke specific and enhanced adaptive immune response to subsequent exposure to the specific pathogen or related organisms. Such proteins are therefore of immense importance in vaccine preparation and drug design. However, the laboratory experiments to isolate and identify antigens from a microbial pathogen are expensive, time consuming and often unsuccessful. This is why Reverse Vaccinology has become the modern trend of vaccine search, where computational methods are first applied to predict protective antigens or their determinants, known as epitopes...
March 2019: Artificial Intelligence in Medicine
Qasim Al-Shebani, Prashan Premaratne, Darryl J McAndrew, Peter J Vial, Shehan Abey
A capsule endoscopy examination of the human small bowel generates a large number of images that have high similarity. In order to reduce the time it takes to review the high similarity images, clinicians will increase the playback speed, typically to 15 frames per second [1]. Associated with this behaviour is an increased probability of overlooking an image that may contain an abnormality. An alternative option to increasing the playback speed is the application of abnormality detection systems to detect abnormalities such as ulcers, tumors, polyps and bleeding...
March 2019: Artificial Intelligence in Medicine
Kanak Meena, Devendra K Tayal, Vaidehi Gupta, Aiman Fatima
Anemia in children is becoming a worldwide problem owing to the unawareness among people regarding the disease, its causes and preventive measures. This study develops a decision support system using data mining techniques that are applied to a database containing data about nutritional factors for children. The data set was taken from NFHS-4, a survey conducted by the Government of India in 2015-16. The work attempts to predict anemia among children and establish a relation between mother's health and diet during pregnancy and its effects on anemic status of her child...
March 2019: Artificial Intelligence in Medicine
Borna Jafarpour, Samina Raza Abidi, William Van Woensel, Syed Sibte Raza Abidi
Patients with multiple medical conditions (comorbidity) pose major challenges to clinical decision support systems, since the different Clinical Practice Guidelines (CPG) often involve adverse interactions, such as drug-drug or drug-disease interactions. Moreover, opportunities often exist for optimizing care and resources across multiple CPG. These challenges have been taken up in the state of the art, with many approaches focusing on the static integration of comorbid CIG. Nevertheless, we observe that many aspects often change dynamically over time, in ways that cannot be foreseen - such as delays in care tasks, resource availability, test outcomes, and acute comorbid conditions...
March 2019: Artificial Intelligence in Medicine
Su-Dong Lee, Ji-Hyung Lee, Young-Geun Choi, Hee-Cheon You, Ja-Heon Kang, Chi-Hyuck Jun
INTRODUCTION: Visual field testing via standard automated perimetry (SAP) is a commonly used glaucoma diagnosis method. Applying machine learning techniques to the visual field test results, a valid clinical diagnosis of glaucoma solely based on the SAP data is provided. In order to reflect structural-functional patterns of glaucoma on the automated diagnostic models, we propose composite variables derived from anatomically grouped visual field clusters to improve the prediction performance...
March 2019: Artificial Intelligence in Medicine
Sameera V Mohd Sagheer, Sudhish N George
Low-dose Computed Tomography (CT) imaging is a most commonly used medical imaging modality. Though the reduction in dosage reduces the risk due to radiation, it leads to an increase in noise level. Hence, it is a mandatory requirement to include a noise reduction technique as a pre- and/or post-processing step for better disease diagnosis. The nuclear norm minimization has attracted a great deal of research interest in contemporary years. This paper proposes a low-rank approximation based approach for denoising of CT images by effectively utilizing the global spatial correlation and local smoothness properties...
March 2019: Artificial Intelligence in Medicine
Sara Montagna, Daniel Castro Silva, Pedro Henriques Abreu, Marcia Ito, Michael Ignaz Schumacher, Eloisa Vargiu
No abstract text is available yet for this article.
February 27, 2019: Artificial Intelligence in Medicine
Davide Calvaresi, Mauro Marinoni, Aldo Franco Dragoni, Roger Hilfiker, Michael Schumacher
Telerehabilitation in older adults is most needed in the patient environments, rather than in formal ambulatories or hospitals. Supporting such practices brings significant advantages to patients, their family, formal and informal caregivers, clinicians, and researchers. This paper presents a focus group with experts in physiotherapy and telerehabilitation, debating on the requirements, current techniques and technologies developed to facilitate and enhance the effectiveness of telerehabilitation, and the still open challenges...
February 14, 2019: Artificial Intelligence in Medicine
Chang-Jiang Zhang, Xue-You Huang, Ming-Chao Fang
NeighShrink is an efficient image denoising algorithm for the reduction of additive white Gaussian noise. However, it does not perform well in terms of Rician noise removal for MRI (Magnetic Resonance Imaging). Allowing for the characteristics of squared-magnitude MR (Magnetic Resonance) images, which follow a non-central chi-square distribution, the CURE (Chi-Square Unbiased Risk Estimation) is used to determine an optimal threshold for NeighShrink. Therefore, we propose the NeighShrinkCURE denoising algorithm...
January 31, 2019: Artificial Intelligence in Medicine
Maisa Daoud, Michael Mayo
Neural networks are powerful tools used widely for building cancer prediction models from microarray data. We review the most recently proposed models to highlight the roles of neural networks in predicting cancer from gene expression data. We identified articles published between 2013-2018 in scientific databases using keywords such as cancer classification, cancer analysis, cancer prediction, cancer clustering and microarray data. Analyzing the studies reveals that neural network methods have been either used for filtering (data engineering) the gene expressions in a prior step to prediction; predicting the existence of cancer, cancer type or the survivability risk; or for clustering unlabeled samples...
January 30, 2019: Artificial Intelligence in Medicine
Manxia Liu, Fabio Stella, Arjen Hommersom, Peter J F Lucas, Lonneke Boer, Erik Bischoff
BACKGROUND: Recently, mobile devices, such as smartphones, have been introduced into healthcare research to substitute paper diaries as data-collection tools in the home environment. Such devices support collecting patient data at different time points over a long period, resulting in clinical time-series data with high temporal complexity, such as time irregularities. Analysis of such time series poses new challenges for machine-learning techniques. The clinical context for the research discussed in this paper is home monitoring in chronic obstructive pulmonary disease (COPD)...
January 22, 2019: Artificial Intelligence in Medicine
Zuzana Rošťáková, Roman Rosipal
The amount and quality of sleep substantially influences health, daily behaviour and overall quality of life. The main goal of this study was to investigate to what extent sleep structure, as derived from the polysomnographic (PSG) recordings of nocturnal human sleep, can provide information about sleep quality in terms of correlating with a set of variables representing the daytime subjective, neurophysiological and cognitive states of a healthy population without serious sleep problems. We focused on a continuous sleep representation derived from the probabilistic sleep model (PSM), which describes the microstructure of sleep by a set of sleep probabilistic curves representing a finite number of sleep microstates...
December 29, 2018: Artificial Intelligence in Medicine
Francisco S Melo, Alberto Sardinha, David Belo, Marta Couto, Miguel Faria, Anabela Farias, Hugo Gambôa, Cátia Jesus, Mithun Kinarullathil, Pedro Lima, Luís Luz, André Mateus, Isabel Melo, Plinio Moreno, Daniel Osório, Ana Paiva, Jhielson Pimentel, João Rodrigues, Pedro Sequeira, Rubén Solera-Ureña, Miguel Vasco, Manuela Veloso, Rodrigo Ventura
This paper describes the INSIDE system, a networked robot system designed to allow the use of mobile robots as active players in the therapy of children with autism spectrum disorders (ASD). While a significant volume of work has explored the impact of robots in ASD therapy, most such work comprises remotely operated robots and/or well-structured interaction dynamics. In contrast, the INSIDE system allows for complex, semi-unstructured interaction in ASD therapy while featuring a fully autonomous robot. In this paper we describe the hardware and software infrastructure that supports such rich form of interaction, as well as the design methodology that guided the development of the INSIDE system...
December 28, 2018: Artificial Intelligence in Medicine
Iman Sharifi, Sobhan Goudarzi, Mohammad Bagher Khodabakhshi
Continuous cuffless blood pressure (BP) monitoring has attracted much interest in finding the ideal treatment of diseases and the prevention of premature death. This paper presents a novel dynamical method, based on pulse transit time (PTT) and photoplethysmogram intensity ratio (PIR), for the continuous cuffless BP estimation. By taking the advantages of both the modeling and the prediction approaches, the proposed framework effectively estimates diastolic BP (DBP), mean BP (BP), and systolic BP (SBP). Adding past states of the cardiopulmonary system as well as present states of the cardiac system to our model caused two main improvements...
December 23, 2018: Artificial Intelligence in Medicine
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