Read by QxMD icon Read

Artificial Intelligence in Medicine | Page 3

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
Angelo Croatti, Sara Montagna, Alessandro Ricci, Emiliano Gamberini, Vittorio Albarello, Vanni Agnoletti
Personal assistant agents can have an important role in healthcare as a smart technology to support physicians in their daily work, helping to tackle the increasing complexity of their task environment. In this paper we present and discuss a personal medical assistant agent technology for trauma documentation and management, based on the Belief-Desire-Intention (BDI) architecture. The purpose of the personal assistant agent is twofold: to assist the Trauma Team in doing precision tracking during a trauma resuscitation, so as to (automatically) produce an accurate documentation of the trauma, and to generate alerts at real-time, to be eventually displayed either on smart-glasses or room-display...
December 19, 2018: Artificial Intelligence in Medicine
Khalid Raza
The rapid advancements in high-throughput techniques have fueled large-scale production of biological data at very affordable costs. Some of these techniques are microarrays and next-generation sequencing that provide genome level insight of living cells. As a result, the size of most of the biological databases, such as NCBI-GEO, NCBI-SRA, etc., is growing exponentially. These biological data are analyzed using various computational techniques for knowledge discovery - which is also one of the objectives of bioinformatics research...
December 17, 2018: Artificial Intelligence in Medicine
Ahmed M Anter, Aboul Ella Hassenian
Liver tumor segmentation from computed tomography (CT) images is a critical and challenging task. Due to the fuzziness in the liver pixel range, the neighboring organs of the liver with the same intensity, high noise and large variance of tumors. The segmentation process is necessary for the detection, identification, and measurement of objects in CT images. We perform an extensive review of the CT liver segmentation literature. Furthermore, in this paper, an improved segmentation approach based on watershed algorithm, neutrosophic sets (NS), and fast fuzzy c-mean clustering algorithm (FFCM) for CT liver tumor segmentation is proposed...
December 14, 2018: Artificial Intelligence in Medicine
Edilson F Arruda, Basílio B Pereira, Clarissa A Thiers, Bernardo R Tura
This paper proposes a stochastic shortest path approach to find an optimal sequence of tests to confirm or discard a disease, for any prescribed optimality criterion. The idea is to select the best sequence in which to apply a series of available tests, with a view at reaching a diagnosis with minimum expenditure of resources. The proposed approach derives an optimal policy whereby the decision maker is provided with a test strategy for each a priori probability of disease, aiming to reach posterior probabilities that warrant either immediate treatment or a not-ill diagnosis...
December 5, 2018: Artificial Intelligence in Medicine
Ahmet Saygılı, Songül Albayrak
Menisci are structures that directly affect movement, so early detection of meniscus tears also helps to prevent progressive knee disorders such as osteoarthritis. Manual segmentation of the menisci and diagnosis of the meniscal tear is a costly process in terms of time and effort for a radiologist. The aim of this study is to automatically determine the location and the type of meniscal tears that are important in the diagnosis and effective treatment of this problem. For this purpose, 29 different MR images, which were provided by Osteoarthritis Initiative (OAI), were used in the study...
December 4, 2018: Artificial Intelligence in Medicine
Mario Merone, Carlo Sansone, Paolo Soda
BACKGROUND AND OBJECTIVE: The indirect immunofluorescence (IIF) on HEp-2 cells is the recommended technique for the detection of antinuclear antibodies. However, it is burdened by some limitations, as it is time consuming and subjective, and it requires trained personnel. In other fields the adoption of deep neural networks has provided an effective high-level abstraction of the raw data, resulting in the ability to automatically generate optimized high-level features. METHODS: To alleviate IIF limitations, this paper presents a computer-aided diagnosis (CAD) system classifying HEp-2 fluorescence intensity: it represents each image using an Invariant Scattering Convolutional Network (Scatnet), which is locally translation invariant and stable to deformations, a characteristic useful in case of HEp-2 samples...
November 28, 2018: Artificial Intelligence in Medicine
Mireia Calvo, Virginie Le Rolle, Daniel Romero, Nathalie Béhar, Pedro Gomis, Philippe Mabo, Alfredo I Hernández
This paper proposes the integration and analysis of a closed-loop model of the baroreflex and cardiovascular systems, focused on a time-varying estimation of the autonomic modulation of heart rate in Brugada syndrome (BS), during exercise and subsequent recovery. Patient-specific models of 44 BS patients at different levels of risk (symptomatic and asymptomatic) were identified through a recursive evolutionary algorithm. After parameter identification, a close match between experimental and simulated signals (mean error = 0...
November 28, 2018: Artificial Intelligence in Medicine
Imon Banerjee, Yuan Ling, Matthew C Chen, Sadid A Hasan, Curtis P Langlotz, Nathaniel Moradzadeh, Brian Chapman, Timothy Amrhein, David Mong, Daniel L Rubin, Oladimeji Farri, Matthew P Lungren
This paper explores cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scale and compares them to the state-of-the-art domain-specific rule-based system - PEFinder and traditional machine learning methods - SVM and Adaboost. We proposed two distinct deep learning models - (i) CNN Word - Glove, and (ii) Domain phrase attention-based hierarchical recurrent neural network (DPA-HNN), for synthesizing information on pulmonary emboli (PE) from over 7370 clinical thoracic computed tomography (CT) free-text radiology reports collected from four major healthcare centers...
November 23, 2018: Artificial Intelligence in Medicine
Juan Fdez-Olivares, Eva Onaindia, Luis Castillo, Jaume Jordán, Juan Cózar
The conciliation of multiple single-disease guidelines for comorbid patients entails solving potential clinical interactions, discovering synergies in the diagnosis and the recommendations, and managing clinical equipoise situations. Personalized conciliation of multiple guidelines considering additionally patient preferences brings some further difficulties. Recently, several works have explored distinct techniques to come up with an automated process for the conciliation of clinical guidelines for comorbid patients but very little attention has been put in integrating the patient preferences into this process...
November 22, 2018: Artificial Intelligence in Medicine
Nadeem Salamat, Malik M Saad Missen, Aqsa Rashid
The diabetic retinopathy is the main reason of vision loss in people. Medical experts recognize some clinical, geometrical and haemodynamic features of diabetic retinopathy. These features include the blood vessel area, exudates, microaneurysm, hemorrhages and neovascularization, etc. In Computer Aided Diagnosis (CAD) systems, these features are detected in fundus images using computer vision techniques. In this paper, we review the methods of low, middle and high level vision for automatic detection and classification of diabetic retinopathy...
November 15, 2018: Artificial Intelligence in Medicine
Jinao Zhang, Yongmin Zhong, Chengfan Gu
This paper presents a new neural network methodology for modelling of soft tissue deformation for surgical simulation. The proposed methodology formulates soft tissue deformation and its dynamics as the neural propagation and dynamics of cellular neural networks for real-time, realistic, and stable simulation of soft tissue deformation. It develops two cellular neural network models; based on the bioelectric propagation of biological tissues and principles of continuum mechanics, one cellular neural network model is developed for propagation and distribution of mechanical load in soft tissues; based on non-rigid mechanics of motion in continuum mechanics, the other cellular neural network model is developed for governing model dynamics of soft tissue deformation...
November 13, 2018: Artificial Intelligence in Medicine
Nicola Falcionelli, Paolo Sernani, Albert Brugués, Dagmawi Neway Mekuria, Davide Calvaresi, Michael Schumacher, Aldo Franco Dragoni, Stefano Bromuri
Personal Health Systems (PHS) are mobile solutions tailored to monitoring patients affected by chronic non communicable diseases. In general, a patient affected by a chronic disease can generate large amounts of events: for example, in Type 1 Diabetic patients generate several glucose events per day, ranging from at least 6 events per day (under normal monitoring) to 288 per day when wearing a continuous glucose monitor (CGM) that samples the blood every 5 minutes for several days. Just by itself, without considering other physiological parameters, it would be impossible for medical doctors to individually and accurately follow every patient, highlighting the need of simple approaches towards querying physiological time series...
November 12, 2018: Artificial Intelligence in Medicine
Andrik Rampun, Bryan W Scotney, Philip J Morrow, Hui Wang, John Winder
In medical computer aided diagnosis systems, image segmentation is one of the major pre-processing steps used to ensure only the region of interest, such as the breast region, will be processed in subsequent steps. Nevertheless, breast segmentation is a difficult task due to low contrast and inhomogeneity, especially when estimating the chest wall in magnetic resonance (MR) images. In fact, the chest wall comprises fat, skin, muscles, and the thoracic skeleton, which can misguide automatic methods when attempting to estimate its location...
November 9, 2018: Artificial Intelligence in Medicine
Thaína A Azevedo Tosta, Paulo Rogério de Faria, Leandro Alves Neves, Marcelo Zanchetta do Nascimento
Different types of cancer can be diagnosed with the analysis of histological samples stained with hematoxylin-eosin (H&E). Through this stain, it is possible to identify the architecture of tissue components and analyze cellular morphological aspects that are essential for cancer diagnosis. However, preparation and digitization of histological samples can lead to color variations that influence the performance of segmentation and classification algorithms in histological image analysis systems. Among the determinant factors of these color variations are different staining time, concentration and pH of the solutions, and the use of different digitization systems...
November 9, 2018: Artificial Intelligence in Medicine
Tom Van Steenkiste, Joeri Ruyssinck, Leen De Baets, Johan Decruyenaere, Filip De Turck, Femke Ongenae, Tom Dhaene
INTRODUCTION: Blood cultures are often performed in the intensive care unit (ICU) to detect bloodstream infections and identify pathogen type, further guiding treatment. Early detection is essential, as a bloodstream infection can give cause to sepsis, a severe immune response associated with an increased risk of organ failure and death. PROBLEM STATEMENT: The early clinical detection of a bloodstream infection is challenging but rapid targeted treatment, within the first place antimicrobials, substantially increases survival chances...
November 9, 2018: Artificial Intelligence in Medicine
Jeroen S de Bruin, Christian Schuh, Walter Seeling, Eva Luger, Michaela Gall, Elisabeth Hütterer, Gabriela Kornek, Bernhard Ludvik, Friedrich Hoppichler, Karin Schindler
BACKGROUND: Nutritional screening procedures followed by regular nutrition monitoring for oncological outpatients are no standard practice in many European hospital wards and outpatient settings. As a result, early signs of malnutrition are missed and nutritional treatment is initiated when patients have already experienced severe weight loss. OBJECTIVE: We report on a novel clinical decision support system (CDSS) for the global assessment and nutritional triage of the nutritional condition of oncology outpatients...
November 2018: Artificial Intelligence in Medicine
Yihan Deng, André Sander, Lukas Faulstich, Kerstin Denecke
Classification systems such as ICD-10 for diagnoses or the Swiss Operation Classification System (CHOP) for procedure classification in the clinical treatment are essential for clinical management and information exchange. Traditionally, classification codes are assigned manually or by systems that rely upon concept-based or rule-based classification methods. Such methods can reach their limit easily due to the restricted coverage of handcrafted rules and of the vocabulary in underlying terminological systems...
October 29, 2018: Artificial Intelligence in Medicine
Klaus-Peter Adlassnig, Peter Haug, Robert A Jenders
No abstract text is available yet for this article.
October 25, 2018: Artificial Intelligence in Medicine
Yushan Qiu, Hao Jiang, Wai-Ki Ching, Michael K Ng
Identifying tumor metastasis signatures from gene expression data at the whole genome level remains an arduous challenge, particularly so when the number of genes is huge and the number of experimental samples is small. We focus on the prediction of the epithelial-mesenchymal transition (EMT), which is an underlying mechanism of tumor metastasis, here, rather than on tumor metastasis itself, to avoid confounding effects of uncertainties derived from various factors. We apply an extended LASSO model, L1/2 -regularization model, as a feature selector, to identify significant RNA-binding proteins (RBPs) that contribute to regulating the EMT...
October 20, 2018: Artificial Intelligence in Medicine
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"