1,504 results match your criteria Artificial Intelligence in Medicine [Journal]


Corrigendum to "A neutrosophic-entropy based adaptive thresholding segmentation algorithm: A special application in MR images of Parkinson's disease" [Artif Intell Med 104 (2020) 101838].

Authors:
Pritpal Singh

Artif Intell Med 2020 Jun 26:101902. Epub 2020 Jun 26.

Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan. Electronic address:

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http://dx.doi.org/10.1016/j.artmed.2020.101902DOI Listing

Regularized-Ncut: Robust and homogeneous functional parcellation of neonate and adult brain networks.

Artif Intell Med 2020 Jun 12;106:101872. Epub 2020 May 12.

Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Electronic address:

Brain network parcellation based on resting-state functional MRI (rs-fMRI) is affected by noise, resulting in spurious small patches and decreased functional homogeneity within each network. Obtaining robust and homogeneous parcellation of neonate brain is more difficult, because neonate rs-fMRI is associated with relatively higher level of noise and no prior knowledge from a functional neonate atlas is available as spatial constraints. To meet these challenges, we developed a novel data-driven Regularized Normalized-cut (RNcut) method. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101872DOI Listing

Upper-limb functional assessment after stroke using mirror contraction: A pilot study.

Artif Intell Med 2020 Jun 19;106:101877. Epub 2020 May 19.

State Key Laboratory of Robotics and System, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518052, China. Electronic address:

The clinical assessment after stroke depends on the rating scale, usually lack of quantitative feedback such as biomedical signal captured from stroke patients. This study attempts to develop a unified assessment framework for persons after stroke via surface electromyography (sEMG) bias from bilateral limbs, based on four types of selected movements, namely forward lift arm, lateral lift arm, forearm internal/external rotation, forearm pronation/supination. Eleven healthy subjects and six stroke patients are recruited to participate in the experiment to perform the bilateral-mirrored paradigm with six channels of sEMG signals recorded from each of their arms. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101877DOI Listing

Graph Fourier transform of fMRI temporal signals based on an averaged structural connectome for the classification of neuroimaging.

Artif Intell Med 2020 Jun 21;106:101870. Epub 2020 May 21.

IMT Atlantique - Lab STICC, Department of Electronics, Brest, France.

Graph signal processing (GSP) is a framework that enables the generalization of signal processing to multivariate signals described on graphs. In this paper, we present an approach based on Graph Fourier Transform (GFT) and machine learning for the analysis of resting-state functional magnetic resonance imaging (rs-fMRI). For each subject, we use rs-fMRI time series to compute several descriptive statistics in regions of interest (ROI). Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101870DOI Listing

Speckle reduction of OCT via super resolution reconstruction and its application on retinal layer segmentation.

Artif Intell Med 2020 Jun 15;106:101871. Epub 2020 May 15.

Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China. Electronic address:

Optical coherence tomography (OCT) is a rapidly developing non-invasive three dimensional imaging approach, and it has been widely used in examination and diagnosis of eye diseases. However, speckle noise are often inherited from image acquisition process, and may obscure the anatomical structure, such as the retinal layers. In this paper, we propose a novel method to reduce the speckle noise in 3D OCT scans, by introducing a new super-resolution approach. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101871DOI Listing

FDSR: A new fuzzy discriminative sparse representation method for medical image classification.

Artif Intell Med 2020 Jun 25;106:101876. Epub 2020 May 25.

Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. Electronic address:

Recent developments in medical image analysis techniques make them essential tools in medical diagnosis. Medical imaging is always involved with different kinds of uncertainties. Managing these uncertainties has motivated extensive research on medical image classification methods, particularly for the past decade. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101876DOI Listing
June 2020
2.019 Impact Factor

How to identify and treat data inconsistencies when eliciting health-state utility values for patient-centered decision making.

Artif Intell Med 2020 Jun 26;106:101882. Epub 2020 May 26.

Complete Decisions, LLC, Baton Rouge, LA 70810, USA. Electronic address:

Background: Health utilities express the perceptions patients have on the impact potential adverse events of medical treatments may have on their quality of life. Being able to accurately assess health utilities is crucial when deciding what is the best treatment when multiple and diverse treatment options exist, or when performing a cost / utility analysis. Due to the emotional and other complexities that may exist when such data are elicited, the values of the health utilities may be inaccurate and cause inconsistencies. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101882DOI Listing

A trusted medical image super-resolution method based on feedback adaptive weighted dense network.

Artif Intell Med 2020 Jun 16;106:101857. Epub 2020 May 16.

College of Electrical Engineering, Sichuan University, Chengdu, Sichuan 610065, China.

High-resolution (HR) medical images are preferred in clinical diagnoses and subsequent analysis. However, the acquisition of HR medical images is easily affected by hardware devices. As an effective and trusted alternative method, the super-resolution (SR) technology is introduced to improve the image resolution. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101857DOI Listing

ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network.

Artif Intell Med 2020 Jun 11;106:101856. Epub 2020 May 11.

Hefei National Laboratory for Physical Sciences at the Microscale and Department of Electronic Engineering & Information Science, University of Science and Technology of China, Hefei 230026, China. Electronic address:

Automatic arrhythmia detection based on electrocardiogram (ECG) is of great significance for early prevention and diagnosis of cardiac diseases. Recently, deep learning methods have been applied to arrhythmia detection and obtained great success. Among them, convolutional neural network (CNN) is an effective method for extracting features due to its local connectivity and parameter sharing. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101856DOI Listing

A review on segmentation of knee articular cartilage: from conventional methods towards deep learning.

Artif Intell Med 2020 Jun 6;106:101851. Epub 2020 May 6.

School of Computer Science and Electronic Engineering (CSEE), University of Essex, Wivenhoe Park,Colchester CO4 3SQ, UK.

In this paper, we review the state-of-the-art approaches for knee articular cartilage segmentation from conventional techniques to deep learning (DL) based techniques. Knee articular cartilage segmentation on magnetic resonance (MR) images is of great importance in early diagnosis of osteoarthritis (OA). Besides, segmentation allows estimating the articular cartilage loss rate which is utilised in clinical practice for assessing the disease progression and morphological changes. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101851DOI Listing

CT window trainable neural network for improving intracranial hemorrhage detection by combining multiple settings.

Artif Intell Med 2020 Jun 20;106:101850. Epub 2020 May 20.

Department of Emergency Medicine, College of Medicine of Yeungnam University, Daegu, South Korea. Electronic address:

Window settings to rescale and contrast stretch raw data from radiographic images such as Computed Tomography (CT), X-ray and Magnetic Resonance images is a crucial step as data pre-processing to examine abnormalities and diagnose diseases. We propose a distant-supervised method for determining automatically the best window settings by attaching a window estimator module (WEM) to a deep convolutional neural network (DCNN)-based lesion classifier and training them in conjunction. Aside from predicting a flexible window setting for each raw image, we statistically identify the top four window settings by calculating the mean and standard deviations for the entire dataset. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101850DOI Listing

Classification of myocardial infarction based on hybrid feature extraction and artificial intelligence tools by adopting tunable-Q wavelet transform (TQWT), variational mode decomposition (VMD) and neural networks.

Artif Intell Med 2020 Jun 18;106:101848. Epub 2020 May 18.

School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan 364012, PR China.

Cardiovascular diseases (CVD) is the leading cause of human mortality and morbidity around the world, in which myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. Currently, electrocardiogram (ECG) is widely used by the clinicians to diagnose MI patients due to its inexpensiveness and non-invasive nature. Pathological alterations provoked by MI cause slow conduction by increasing axial resistance on coupling between cells. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101848DOI Listing

Toward development of PreVoid alerting system for nocturnal enuresis patients: A fuzzy-based approach for determining the level of liquid encased in urinary bladder.

Artif Intell Med 2020 Jun 22;106:101819. Epub 2020 Feb 22.

University of Genoa, Dept. of Informatics, Bioengineering, Robotics & System Engineering, Genoa 16146, Italy. Electronic address:

Preventive and accurate assessment of bladder voiding dysfunctions necessitates measuring the amount of liquid encapsulated within urinary bladder walls in a non-invasive and real-time manner. The real-time monitoring of urine levels helps patients with urological disorders such as Nocturnal Enuresis (NE) by preventing the occurrence of enuresis via a pre-void stage alerting system. Although some advances have been achieved toward developing a non-invasive approach for determining the amount of accumulated urine inside the bladder, there is still a lack of an easy-to-implement technique which is suitable to embed in a wearable pre-warning device. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101819DOI Listing

Mining incomplete clinical data for the early assessment of Kawasaki disease based on feature clustering and convolutional neural networks.

Artif Intell Med 2020 May 3;105:101859. Epub 2020 May 3.

Department of Heart Centre, Children's Hospital of Chongqing Medical University, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, 400014, China. Electronic address:

Kawasaki disease (KD) is the leading cause of acquired heart disease in children. Its prompt treatment can effectively lower the risk of severe complications, such as coronary aneurysms. However, accurately diagnosing KD at its early stage is impracticable given its unknown pathogenesis and lack of pathognomonic features. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101859DOI Listing

Early temporal prediction of Type 2 Diabetes Risk Condition from a General Practitioner Electronic Health Record: A Multiple Instance Boosting Approach.

Artif Intell Med 2020 May 6;105:101847. Epub 2020 May 6.

Department of Information Engineering (DII), Università Politecnica delle Marche, Ancona, Italy. Electronic address:

Early prediction of target patients at high risk of developing Type 2 diabetes (T2D) plays a significant role in preventing the onset of overt disease and its associated comorbidities. Although fundamental in early phases of T2D natural history, insulin resistance is not usually quantified by General Practitioners (GPs). Triglyceride-glucose (TyG) index has been proven useful in clinical studies for quantifying insulin resistance and for the early identification of individuals at T2D risk but still not applied by GPs for diagnostic purposes. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101847DOI Listing

Explainable AI meets persuasiveness: Translating reasoning results into behavioral change advice.

Artif Intell Med 2020 May 5;105:101840. Epub 2020 Mar 5.

Fondazione Bruno Kessler, Trento, Italy. Electronic address:

Explainable AI aims at building intelligent systems that are able to provide a clear, and human understandable, justification of their decisions. This holds for both rule-based and data-driven methods. In management of chronic diseases, the users of such systems are patients that follow strict dietary rules to manage such diseases. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101840DOI Listing

Breast cancer diagnosis from histopathological images using textural features and CBIR.

Artif Intell Med 2020 May 22;105:101845. Epub 2020 Apr 22.

Pontifical Catholic University of Rio de Janeiro - PUC - Rio, Rio de Janeiro, RJ, Brazil. Electronic address:

Currently, breast cancer diagnosis is an extensively researched topic. An effective method to diagnose breast cancer is to use histopathological images. However, extracting features from these images is a challenging task. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101845DOI Listing

Vessel Structure Extraction using Constrained Minimal Path Propagation.

Artif Intell Med 2020 May 25;105:101846. Epub 2020 Apr 25.

Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), Rennes, France.

Minimal path method has been widely recognized as an efficient tool for extracting vascular structures in medical imaging. In a previous paper, a method termed minimal path propagation with backtracking (MPP-BT) was derived to deal with curve-like structures such as vessel centerlines. A robust approach termed CMPP (constrained minimal path propagation) is here proposed to extend this work. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101846DOI Listing
May 2020
2.019 Impact Factor

Deep neural network for semi-automatic classification of term and preterm uterine recordings.

Authors:
Lili Chen Huoyao Xu

Artif Intell Med 2020 May 19;105:101861. Epub 2020 Apr 19.

School of Mechatronics & Vehicle Engineering, Chongqing Jiaotong University, Chongqing, 400074, China; School of Chongqing Key Laboratory of Urban Rail Transit Vehicle System Integration and Control, Chongqing Jiaotong University, Chongqing, 400074, China.

Pregnancy is a complex process, and the prediction of premature birth is uncertain. Many researchers are exploring non-invasive approaches to enhance its predictability. Currently, the ElectroHysteroGram (EHG) and Tocography (TOCO) signal are a real-time and non-invasive technology which can be employed to predict preterm birth. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101861DOI Listing

Fuzzy support vector machine-based personalizing method to address the inter-subject variance problem of physiological signals in a driver monitoring system.

Artif Intell Med 2020 May 21;105:101843. Epub 2020 Mar 21.

Department of Electrical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea. Electronic address:

Physiological signals can be utilized to monitor conditions of a driver, but the inter-subject variance of physiological signals can degrade the classification accuracy of the monitoring system. Personalization of the system using the data of a tested subject, called local data, can be a solution, but the acquisition of sufficient local data may not be possible in real situations. Therefore, this paper proposes an effective personalizing method using small-sized local data. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101843DOI Listing

Mining post-surgical care processes in breast cancer patients.

Artif Intell Med 2020 May 15;105:101855. Epub 2020 Apr 15.

Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy. Electronic address:

In this work we describe the application of a careflow mining algorithm to detect the most frequent patterns of care in a cohort of 3000 breast cancer patients. The applied method relies on longitudinal data extracted from electronic health records, recorded from the first surgical procedure after a breast cancer diagnosis. Careflows are mined from events data recorded for administrative purposes, including procedures from ICD9 - CM billing codes and chemotherapy treatments. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101855DOI Listing

Automatic segmentation of knee menisci - A systematic review.

Artif Intell Med 2020 May 6;105:101849. Epub 2020 May 6.

Institute of Orthopedic Research and Biomechanics, Ulm University Medical Center, Helmholtzstr. 14, 89081 Ulm, Germany.

Magnetic resonance imaging (MRI) has proved to be an invaluable component of pathogenesis research in osteoarthritis. Nevertheless, the detection of a meniscal lesion from magnetic resonance (MR) images is always challenging for both clinicians and researchers, because the surrounding tissues lead to similar signals within MR measurements, thus being difficult to discriminate. Moreover, the size and shape of osteoarthritic and non-osteoarthritic menisci vary to a large extent between individuals of same features, e. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101849DOI Listing

On the use of pairwise distance learning for brain signal classification with limited observations.

Artif Intell Med 2020 May 11;105:101852. Epub 2020 May 11.

INESC-ID and IST, Universidade de Lisboa, Lisbon, Portugal. Electronic address:

The increasing access to brain signal data using electroencephalography creates new opportunities to study electrophysiological brain activity and perform ambulatory diagnoses of neurological disorders. This work proposes a pairwise distance learning approach for schizophrenia classification relying on the spectral properties of the signal. To be able to handle clinical trials with a limited number of observations (i. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101852DOI Listing

Reconstructing the patient's natural history from electronic health records.

Artif Intell Med 2020 May 3;105:101860. Epub 2020 May 3.

Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain. Electronic address:

The automatic extraction of a patient's natural history from Electronic Health Records (EHRs) is a critical step towards building intelligent systems that can reason about clinical variables and support decision making. Although EHRs contain a large amount of valuable information about the patient's medical care, this information can only be fully understood when analyzed in a temporal context. Any intelligent system should then be able to extract medical concepts, date expressions, temporal relations and the temporal ordering of medical events from the free texts of EHRs; yet, this task is hard to tackle, due to the domain specific nature of EHRs, writing quality and lack of structure of these texts, and more generally the presence of redundant information. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101860DOI Listing

Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics.

Artif Intell Med 2020 May 7;105:101854. Epub 2020 Apr 7.

Department of Radiology, Health Care Global, Bangalore, India.

Motivation: Breast cancer is the leading cause of cancer deaths among women today. Survival rates in developing countries are around 50%-60% due to late detection. A personalized, accurate risk scoring method can help in targeting the right population for follow-up tests and enables early detection of breast abnormalities. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101854DOI Listing

Fuzzy inference model based on triaxial signals for pronation and supination assessment in Parkinson's disease patients.

Artif Intell Med 2020 May 6;105:101873. Epub 2020 May 6.

Instituto Politécnico Nacional, Centro de Investigación en Computación, Juan de Dios Bátiz Ave., 07738, México City, Mexico.

Nowadays, the Unified Parkinson Disease Rating Scale supported by the Movement Disorder Society (MDS-UPDRS), is a standardized and widely accepted instrument to rate Parkinson's disease (PD). This work presents a thorough analysis of item 3.6 of the MDS-UPDRS scale which corresponds to the pronation and supination hand movements. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101873DOI Listing

An improved multi-swarm particle swarm optimizer for optimizing the electric field distribution of multichannel transcranial magnetic stimulation.

Artif Intell Med 2020 Apr 3;104:101790. Epub 2020 Jan 3.

School of Electrical Engineering and Automation, TIANGONG University, Tianjin 300387, China; Key Laboratory of Advanced Electrical Engineering and Energy Technology, TIANGONG University, Tianjin 300387, China.

Multichannel transcranial magnetic stimulation (mTMS) is a therapeutic method to improve psychiatric diseases, which has a flexible working pattern used to different applications. In order to make the electric field distribution in the brain meet the treatment expectations, we have developed a novel multi-swam particle swarm optimizer (NMSPSO) to optimize the current configuration of double layer coil array. To balance the exploration and exploitation abilities, three novel improved strategies are used in NMSPSO based on multi-swarm particle swarm optimizer. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101790DOI Listing

Modeling and processing up-to-dateness of patient information in probabilistic therapy decision support.

Artif Intell Med 2020 Apr 9;104:101842. Epub 2020 Mar 9.

University of Leipzig, Medical Faculty, ICCAS, Leipzig, Germany; Department of Neurology, University of Magdeburg, Germany.

Objectives: Probabilistic modeling of a patient's situation with the goal of providing calculated therapy recommendations can improve the decision making of interdisciplinary teams. Relevant information entities and direct causal dependencies, as well as uncertainty, must be formally described. Possible therapy options, tailored to the patient, can be inferred from the clinical data using these descriptions. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101842DOI Listing

Characterizing the critical features when personalizing antihypertensive drugs using spectrum analysis and machine learning methods.

Artif Intell Med 2020 Apr 29;104:101841. Epub 2020 Feb 29.

Cardiovascular Department, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China. Electronic address:

Globally, methods of controlling blood pressure in hypertension patients remain inefficient. The difficulty of prescribing appropriate drugs specific to a patient's clinical features serves as one of the most important factors. Characterizing the critical drug-related features, just like that of the antibacterial spectrum (where each item is sensitive to the targeted drug's effectiveness or a specified indication), may help a doctor easily prescribe appropriate drugs by matching a patient's attributes with drug-related features, and effectiveness of the selected drugs would also be ascertained. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101841DOI Listing

Detecting potential signals of adverse drug events from prescription data.

Artif Intell Med 2020 Apr 27;104:101839. Epub 2020 Feb 27.

School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, South Australia 5095, Australia. Electronic address:

Adverse drug events (ADEs) may occur and lead to severe consequences for the public, even though clinical trials are conducted in the stage of pre-market. Computational methods are still needed to fulfil the task of pharmacosurveillance. In post-market surveillance, the spontaneous reporting system (SRS) has been widely used to detect suspicious associations between medicines and ADEs. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101839DOI Listing

A neutrosophic-entropy based adaptive thresholding segmentation algorithm: A special application in MR images of Parkinson's disease.

Authors:
Pritpal Singh

Artif Intell Med 2020 Apr 28;104:101838. Epub 2020 Feb 28.

Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan. Electronic address:

Brain MR images are composed of three main regions such as gray matter, white matter and cerebrospinal fluid. Radiologists and medical practitioners make decisions through evaluating the developments in these regions. Study of these MR images suffers from two major issues such as: (a) the boundaries of their gray matter and white matter regions are ambiguous and unclear in nature, and (b) their regions are formed with unclear inhomogeneous gray structures. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101838DOI Listing

Offline identification of surgical deviations in laparoscopic rectopexy.

Artif Intell Med 2020 Apr 27;104:101837. Epub 2020 Feb 27.

UGA/CNRS/INSERM, TIMC-IMAG UMR 5525, Grenoble F-38041, France.

Objective: According to a meta-analysis of 7 studies, the median number of patients with at least one adverse event during the surgery is 14.4%, and a third of those adverse events were preventable. The occurrence of adverse events forces surgeons to implement corrective strategies and, thus, deviate from the standard surgical process. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101837DOI Listing

Reinforcement learning application in diabetes blood glucose control: A systematic review.

Artif Intell Med 2020 Apr 21;104:101836. Epub 2020 Feb 21.

Department of Mathematics and Statistics, University of Tromsø-The Arctic University of Norway, Norway.

Background: Reinforcement learning (RL) is a computational approach to understanding and automating goal-directed learning and decision-making. It is designed for problems which include a learning agent interacting with its environment to achieve a goal. For example, blood glucose (BG) control in diabetes mellitus (DM), where the learning agent and its environment are the controller and the body of the patient respectively. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101836DOI Listing

Wearable sensor-based evaluation of psychosocial stress in patients with metabolic syndrome.

Artif Intell Med 2020 Apr 20;104:101824. Epub 2020 Feb 20.

Department of Electrical and Electronics Engineering, Izmir University of Economics, Izmir, Turkey. Electronic address:

The prevalence of metabolic disorders has increased rapidly as such they become a major health issue recently. Despite the definition of genetic associations with obesity and cardiovascular diseases, they constitute only a small part of the incidence of disease. Environmental and physiological effects such as stress, behavioral and metabolic disturbances, infections, and nutritional deficiencies have now revealed as contributing factors to develop metabolic diseases. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101824DOI Listing

A recurrent neural network approach to predicting hemoglobin trajectories in patients with End-Stage Renal Disease.

Artif Intell Med 2020 Apr 19;104:101823. Epub 2020 Feb 19.

Division of Nephrology, Department of Medicine, University of Virginia, Charlottesville, VA 22908, United States.

The most severe form of kidney disease, End-Stage Renal Disease (ESRD) is treated with various forms of dialysis - artificial blood cleansing. Dialysis patients suffer many health burdens including high mortality and hospitalization rates, and symptomatic anemia: a low red blood cell count as indicated by a low hemoglobin (Hgb) level. ESRD-induced anemia is treated, with variable patient response, by erythropoiesis stimulating agents (ESAs): expensive injectable medications typically administered during dialysis sessions. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101823DOI Listing

Automated machine learning: Review of the state-of-the-art and opportunities for healthcare.

Artif Intell Med 2020 Apr 21;104:101822. Epub 2020 Feb 21.

Dana-Farber Cancer Institute, Department of Informatics & Analytics, Boston, MA, 02215, United States; Harvard T.H. Chan School of Public Health, Department of Biostatistics, Boston, MA, 02215, United States; Massachusetts Institute of Technology, Cambridge, MA, 02139, United States. Electronic address:

Objective: This work aims to provide a review of the existing literature in the field of automated machine learning (AutoML) to help healthcare professionals better utilize machine learning models "off-the-shelf" with limited data science expertise. We also identify the potential opportunities and barriers to using AutoML in healthcare, as well as existing applications of AutoML in healthcare.

Methods: Published papers, accompanied with code, describing work in the field of AutoML from both a computer science perspective or a biomedical informatics perspective were reviewed. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101822DOI Listing

A novel deep mining model for effective knowledge discovery from omics data.

Artif Intell Med 2020 Apr 24;104:101821. Epub 2020 Feb 24.

School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, United Kingdom.

Knowledge discovery from omics data has become a common goal of current approaches to personalised cancer medicine and understanding cancer genotype and phenotype. However, high-throughput biomedical datasets are characterised by high dimensionality and relatively small sample sizes with small signal-to-noise ratios. Extracting and interpreting relevant knowledge from such complex datasets therefore remains a significant challenge for the fields of machine learning and data mining. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101821DOI Listing
April 2020
2.019 Impact Factor

Early detection of sepsis utilizing deep learning on electronic health record event sequences.

Artif Intell Med 2020 Apr 19;104:101820. Epub 2020 Feb 19.

Enversion A/S, Fiskerivej 12, 8000 Aarhus C, Denmark; Department of Engineering, Aarhus University School of Engineering, Denmark.

Background: The timeliness of detection of a sepsis incidence in progress is a crucial factor in the outcome for the patient. Machine learning models built from data in electronic health records can be used as an effective tool for improving this timeliness, but so far, the potential for clinical implementations has been largely limited to studies in intensive care units. This study will employ a richer data set that will expand the applicability of these models beyond intensive care units. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101820DOI Listing

Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction.

Artif Intell Med 2020 Apr 19;104:101818. Epub 2020 Feb 19.

Universitary Hospital of Getafe, Madrid, Spain.

Antimicrobial resistance has become one of the most important health problems and global action plans have been proposed globally. Prevention plays a key role in these actions plan and, in this context, we propose the use of Artificial Intelligence, specifically Time Series Forecasting techniques, for predicting future outbreaks of Methicillin-resistant Staphylococcus aureus (MRSA). Infection incidence forecasting is approached as a Feature Selection based Time Series Forecasting problem using multivariate time series composed of incidence of Staphylococcus aureus Methicillin-sensible and MRSA infections, influenza incidence and total days of therapy of both of Levofloxacin and Oseltamivir antimicrobials. Read More

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http://dx.doi.org/10.1016/j.artmed.2020.101818DOI Listing

Efficient treatment of outliers and class imbalance for diabetes prediction.

Artif Intell Med 2020 Apr 10;104:101815. Epub 2020 Feb 10.

Department of Computer Science, Edge Hill University, Ormskirk, United Kingdom. Electronic address:

Learning from outliers and imbalanced data remains one of the major difficulties for machine learning classifiers. Among the numerous techniques dedicated to tackle this problem, data preprocessing solutions are known to be efficient and easy to implement. In this paper, we propose a selective data preprocessing approach that embeds knowledge of the outlier instances into artificially generated subset to achieve an even distribution. Read More

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Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing.

Artif Intell Med 2020 Apr 19;104:101813. Epub 2020 Feb 19.

CIPCE, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran; Image Analysis Lab, Depts. of Radiology and Research Administration, Henry Ford Health System, MI, USA.

Background And Objective: Multimodal data analysis and large-scale computational capability is entering medicine in an accelerative fashion and has begun to influence investigational work in a variety of disciplines. It is also informing us of therapeutic interventions that will come about with such development. Epilepsy is a chronic brain disorder in which functional changes may precede structural ones and which may be detectable using existing modalities. Read More

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April 2020
2.019 Impact Factor

Computerized decision support and machine learning applications for the prevention and treatment of childhood obesity: A systematic review of the literature.

Artif Intell Med 2020 Apr 19;104:101844. Epub 2020 Mar 19.

Information Technologies Institute, Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece.

Background: Digital health interventions based on tools for Computerized Decision Support (CDS) and Machine Learning (ML), which take advantage of new information, sensing and communication technologies, can play a key role in childhood obesity prevention and treatment.

Objectives: We present a systematic literature review of CDS and ML applications for the prevention and treatment of childhood obesity. The main characteristics and outcomes of studies using CDS and ML are demonstrated, to advance our understanding towards the development of smart and effective interventions for childhood obesity care. Read More

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Optimisation and control of the supply of blood bags in hemotherapic centres via Markov decision process with discounted arrival rate.

Artif Intell Med 2020 Apr 8;104:101791. Epub 2020 Jan 8.

Hemorio - Arthur de Siqueira Cavalcanti State Institute of Hematology, Rua Frei Caneca 8, Centro, Rio de Janeiro, RJ 20.211-030, Brazil. Electronic address:

Running a cost-effective human blood transfusion supply chain challenges decision makers in blood services world-wide. In this paper, we develop a Markov decision process with the objective of minimising the overall costs of internal and external collections, storing, producing and disposing of blood bags, whilst explicitly considering the probability that a donated blog bag will perish before demanded. The model finds an optimal policy to collect additional bags based on the number of bags in stock rather than using information about the age of the oldest item. Read More

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Deep learning in generating radiology reports: A survey.

Artif Intell Med 2020 Jun 15;106:101878. Epub 2020 May 15.

School of Computer Science, University of Sydney, Sydney, Australia.

Substantial progress has been made towards implementing automated radiology reporting models based on deep learning (DL). This is due to the introduction of large medical text/image datasets. Generating radiology coherent paragraphs that do more than traditional medical image annotation, or single sentence-based description, has been the subject of recent academic attention. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7227610PMC

Automatic computation of mandibular indices in dental panoramic radiographs for early osteoporosis detection.

Artif Intell Med 2020 03 5;103:101816. Epub 2020 Feb 5.

Instituto del Conocimiento (Knowledge Institute). Complutense University, Madrid, Spain. Electronic address:

Aim: A new automatic method for detecting specific points and lines (straight and curves) in dental panoramic radiographies (orthopantomographies) is proposed, where the human knowledge is mapped to the automatic system. The goal is to compute relevant mandibular indices (Mandibular Cortical Width, Panoramic Mandibular Index, Mandibular Ratio, Mandibular Cortical Index) in order to detect the thinning and deterioration of the mandibular bone. Data can be stored for posterior massive analysis. Read More

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A multicenter random forest model for effective prognosis prediction in collaborative clinical research network.

Artif Intell Med 2020 03 5;103:101814. Epub 2020 Feb 5.

Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China; Research center for healthcare data science, Zhejiang Lab, Hangzhou, China. Electronic address:

Background: The accuracy of a prognostic prediction model has become an essential aspect of the quality and reliability of the health-related decisions made by clinicians in modern medicine. Unfortunately, individual institutions often lack sufficient samples, which might not provide sufficient statistical power for models. One mitigation is to expand data collection from a single institution to multiple centers to collectively increase the sample size. Read More

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March 2020
2.019 Impact Factor

An incremental explanation of inference in Bayesian networks for increasing model trustworthiness and supporting clinical decision making.

Artif Intell Med 2020 03 31;103:101812. Epub 2020 Jan 31.

School of Electronic Engineering & Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK.

Various AI models are increasingly being considered as part of clinical decision-support tools. However, the trustworthiness of such models is rarely considered. Clinicians are more likely to use a model if they can understand and trust its predictions. Read More

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March 2020
2.019 Impact Factor

Random Forest enhancement using improved Artificial Fish Swarm for the medial knee contact force prediction.

Artif Intell Med 2020 03 3;103:101811. Epub 2020 Feb 3.

Jiangxi Provincial People's Hospital, Nanchang, China. Electronic address:

Knee contact force (KCF) is an important factor to evaluate the knee joint function for the patients with knee joint impairment. However, the KCF measurement based on the instrumented prosthetic implants or inverse dynamics analysis is limited due to the invasive, expensive price and time consumption. In this work, we propose a KCF prediction method by integrating the Artificial Fish Swarm and the Random Forest algorithm. Read More

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Quantitative knowledge presentation models of traditional Chinese medicine (TCM): A review.

Artif Intell Med 2020 03 24;103:101810. Epub 2020 Jan 24.

Center of Faculty Dvpt. and Tech., Guangdong Univ. of Finance and Economics, Guangzhou, 510320, China. Electronic address:

Modern computer technology sheds light on new ways of innovating Traditional Chinese Medicine (TCM). One method that gets increasing attention is the quantitative research method, which makes use of data mining and artificial intelligence technology as well as the mathematical principles in the research on rationales, academic viewpoints of famous doctors of TCM, dialectical treatment by TCM, clinical technology of TCM, the patterns of TCM prescriptions, clinical curative effects of TCM and other aspects. This paper reviews the methods, means, progress and achievements of quantitative research on TCM. Read More

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Scalogram based prediction model for respiratory disorders using optimized convolutional neural networks.

Artif Intell Med 2020 03 20;103:101809. Epub 2020 Jan 20.

Department of Electronics and Communication Engineering, Pondicherry Engineering College Puducherry, 605 014, India. Electronic address:

Auscultation of the lung is a conventional technique used for diagnosing chronic obstructive pulmonary diseases (COPDs) and lower respiratory infections and disorders in patients. In most of the earlier works, wavelet transforms or spectrograms have been used to analyze the lung sounds. However, an accurate prediction model for respiratory disorders has not been developed so far. Read More

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