1,461 results match your criteria Artificial intelligence in medicine[Journal]


Deep learning in generating radiology reports: A survey.

Artif Intell Med 2020 May 15:101878. 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

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.101878DOI Listing
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

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.101816DOI Listing

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

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.101814DOI Listing
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

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.101812DOI Listing
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

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.101811DOI Listing

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

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.101810DOI Listing

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

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.101809DOI Listing

Prognostic factors of Rapid symptoms progression in patients with newly diagnosed parkinson's disease.

Artif Intell Med 2020 03 21;103:101807. Epub 2020 Jan 21.

Unit of Medical Technology and Intelligent Information Systems, Dept. of Material Science and Engineering, University of Ioannina, GR45110, Ioannina, Greece; Dept. of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, GR45110, Ioannina, Greece. Electronic address:

Tracking symptoms progression in the early stages of Parkinson's disease (PD) is a laborious endeavor as the disease can be expressed with vastly different phenotypes, forcing clinicians to follow a multi-parametric approach in patient evaluation, looking for not only motor symptomatology but also non-motor complications, including cognitive decline, sleep problems and mood disturbances. Being neurodegenerative in nature, PD is expected to inflict a continuous degradation in patients' condition over time. The rate of symptoms progression, however, is found to be even more chaotic than the vastly different phenotypes that can be expressed in the initial stages of PD. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.101807DOI Listing

Mixed-integer optimization approach to learning association rules for unplanned ICU transfer.

Artif Intell Med 2020 03 30;103:101806. Epub 2020 Jan 30.

Department of Emergency Medicine, Taichung Veterans General Hospital Puli Branch, Taiwan. Electronic address:

After admission to emergency department (ED), patients with critical illnesses are transferred to intensive care unit (ICU) due to unexpected clinical deterioration occurrence. Identifying such unplanned ICU transfers is urgently needed for medical physicians to achieve two-fold goals: improving critical care quality and preventing mortality. A priority task is to understand the crucial rationale behind diagnosis results of individual patients during stay in ED, which helps prepare for an early transfer to ICU. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.101806DOI Listing

Classification of glomerular hypercellularity using convolutional features and support vector machine.

Artif Intell Med 2020 03 25;103:101808. Epub 2020 Jan 25.

IVISION Lab, Universidade Federal da Bahia, Bahia, Brazil. Electronic address:

Glomeruli are histological structures of the kidney cortex formed by interwoven blood capillaries, and are responsible for blood filtration. Glomerular lesions impair kidney filtration capability, leading to protein loss and metabolic waste retention. An example of lesion is the glomerular hypercellularity, which is characterized by an increase in the number of cell nuclei in different areas of the glomeruli. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.101808DOI Listing

Batch Mode Active Learning on the Riemannian Manifold for Automated Scoring of Nuclear Pleomorphism in Breast Cancer.

Artif Intell Med 2020 03 25;103:101805. Epub 2020 Jan 25.

Artificial Intelligence & Computer Vision Lab, Department of Computer Science, Cochin University of Science and Technology, Kochi, 682022, Kerala, India.

Breast cancer is the most prevalent invasive type of cancer among women. The mortality rate of the disease can be reduced considerably through timely prognosis and felicitous treatment planning, by utilizing the computer aided detection and diagnosis techniques. With the advent of whole slide image (WSI) scanners for digitizing the histopathological tissue samples, there is a drastic increase in the availability of digital histopathological images. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.101805DOI Listing

Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI).

Artif Intell Med 2020 03 13;103:101804. Epub 2020 Jan 13.

Dipartimento di Informatica, Università di Pisa, Italy. Electronic address:

Over the years, there has been growing interest in using machine learning techniques for biomedical data processing. When tackling these tasks, one needs to bear in mind that biomedical data depends on a variety of characteristics, such as demographic aspects (age, gender, etc.) or the acquisition technology, which might be unrelated with the target of the analysis. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.101804DOI Listing

Gait characteristics and clinical relevance of hereditary spinocerebellar ataxia on deep learning.

Artif Intell Med 2020 03 7;103:101794. Epub 2020 Jan 7.

Department of Neurology, Neuroscience Center, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, China. Electronic address:

Background: Deep learning has always been at the forefront of scientific research. It has also been applied to medical research. Hereditary spinocerebellar ataxia (SCA) is characterized by gait abnormalities and is usually evaluated semi-quantitatively by scales. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.101794DOI Listing

Seven pillars of precision digital health and medicine.

Artif Intell Med 2020 03 11;103:101793. Epub 2020 Jan 11.

McGill Clinical and Health Informatics, School of Population and Global Health, McGill University, Montreal, Quebec H3A 1A3, Canada.

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.101793DOI Listing
March 2020
2.019 Impact Factor

An effective approach for CT lung segmentation using mask region-based convolutional neural networks.

Artif Intell Med 2020 03 8;103:101792. Epub 2020 Jan 8.

Programa de Pós-Graduação em Ciência da Computação, Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Brazil. Electronic address:

Computer vision systems have numerous tools to assist in various medical fields, notably in image diagnosis. Computed tomography (CT) is the principal imaging method used to assist in the diagnosis of diseases such as bone fractures, lung cancer, heart disease, and emphysema, among others. Lung cancer is one of the four main causes of death in the world. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.101792DOI Listing

Comprehensive electrocardiographic diagnosis based on deep learning.

Artif Intell Med 2020 03 20;103:101789. Epub 2020 Jan 20.

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan; International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University, Kumamoto, Japan. Electronic address:

Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irreversible heart muscle damage, resulting in heart chamber remodeling and eventual congestive heart failure (CHF). Electrocardiography (ECG) signals can be useful to detect established MI, and may also be helpful for early diagnosis of CAD. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101789DOI Listing

An intelligent learning approach for improving ECG signal classification and arrhythmia analysis.

Artif Intell Med 2020 03 31;103:101788. Epub 2019 Dec 31.

State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China. Electronic address:

The recognition of cardiac arrhythmia in minimal time is important to prevent sudden and untimely deaths. The proposed work includes a complete framework for analyzing the Electrocardiogram (ECG) signal. The three phases of analysis include 1) the ECG signal quality enhancement through noise suppression by a dedicated filter combination; 2) the feature extraction by a devoted wavelet design and 3) a proposed hidden Markov model (HMM) for cardiac arrhythmia classification into Normal (N), Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Premature Ventricular Contraction (PVC) and Atrial Premature Contraction (APC). Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101788DOI Listing

A novel method of motor imagery classification using eeg signal.

Artif Intell Med 2020 03 31;103:101787. Epub 2019 Dec 31.

Computational Optics Research Group, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. Electronic address:

A subject of extensive research interest in the Brain Computer Interfaces (BCIs) niche is motor imagery (MI), where users imagine limb movements to control the system. This interest is owed to the immense potential for its applicability in gaming, neuro-prosthetics and neuro-rehabilitation, where the user's thoughts of imagined movements need to be decoded. Electroencephalography (EEG) equipment is commonly used for keeping track of cerebrum movement in BCI systems. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101787DOI Listing

ADHD classification by dual subspace learning using resting-state functional connectivity.

Artif Intell Med 2020 03 13;103:101786. Epub 2020 Jan 13.

Department of Psychiatry and Translational Imaging, Columbia University & NYSPI, USA. Electronic address:

As one of the most common neurobehavioral diseases in school-age children, Attention Deficit Hyperactivity Disorder (ADHD) has been increasingly studied in recent years. But it is still a challenge problem to accurately identify ADHD patients from healthy persons. To address this issue, we propose a dual subspace classification algorithm by using individual resting-state Functional Connectivity (FC). Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101786DOI Listing

The impact of machine learning on patient care: A systematic review.

Artif Intell Med 2020 03 31;103:101785. Epub 2019 Dec 31.

Department of Clinical Neurosciences, Division of Neurosurgery, 1403 29 St. NW, Calgary, Alberta, T2N 2T9, Canada; University of Calgary Combine Spine Program, 1403 29 St. NW, Calgary, Alberta, T2N 2T9, Canada; Hotchkiss Brain Institute, University of Calgary, 1403 29 St. NW, Calgary, Alberta, T2N 2T9, Canada; Department of Radiology, University of Calgary, 1403 29 St. NW, Calgary, Alberta, T2N 2T9, Canada. Electronic address:

Background: Despite the expanding use of machine learning (ML) in fields such as finance and marketing, its application in the daily practice of clinical medicine is almost non-existent. In this systematic review, we describe the various areas within clinical medicine that have applied the use of ML to improve patient care.

Methods: A systematic review was performed in accordance with the PRISMA guidelines using Medline(R), EBM Reviews, Embase, Psych Info, and Cochrane Databases, focusing on human studies that used ML to directly address a clinical problem. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101785DOI Listing

Semantic segmentation with DenseNets for carotid artery ultrasound plaque segmentation and CIMT estimation.

Artif Intell Med 2020 03 31;103:101784. Epub 2019 Dec 31.

Departament de Matemà tiques i Informà tica, Universitat de Barcelona, Gran Via de les Corts Catalanes 585, 08007 Barcelona, Spain.

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. The existing efforts for automating the process have replicated the same two-step structure, resulting in two consecutive independent approaches. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101784DOI Listing

Topic-informed neural approach for biomedical event extraction.

Artif Intell Med 2020 03 30;103:101783. Epub 2019 Dec 30.

School of Engineering, Westlake University, Hangzhou, Zhejiang, China; Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China.

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101783DOI Listing

A fusion framework to extract typical treatment patterns from electronic medical records.

Artif Intell Med 2020 03 28;103:101782. Epub 2019 Dec 28.

Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China. Electronic address:

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101782DOI Listing

Multi-planar 3D breast segmentation in MRI via deep convolutional neural networks.

Artif Intell Med 2020 03 23;103:101781. Epub 2019 Dec 23.

Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, via Claudio 21, 80125 Naples, Italy. Electronic address:

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101781DOI Listing

Medical knowledge embedding based on recursive neural network for multi-disease diagnosis.

Artif Intell Med 2020 03 28;103:101772. Epub 2019 Nov 28.

Medical Record Room, Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China. Electronic address:

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101772DOI Listing
March 2020
2.019 Impact Factor

A multi-context CNN ensemble for small lesion detection.

Artif Intell Med 2020 03 13;103:101749. Epub 2019 Nov 13.

Department of Electrical, Information Engineering and Applied Mathematics, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano (SA), Italy. Electronic address:

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101749DOI Listing

Real-world data medical knowledge graph: construction and applications.

Artif Intell Med 2020 03 6;103:101817. Epub 2020 Feb 6.

School of Science, Beijing Jiaotong University, Beijing, China. Electronic address:

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.101817DOI Listing

Retraction notice to "Diagnosis Labeling with Disease-Specific Characteristics Mining" [Artif. Intell. Med. 90 (2018) 25-33].

Artif Intell Med 2020 03 27;103:101803. Epub 2020 Jan 27.

School of Information Science and Technology, Northwest University, Xian 710127, PR China; Department of Culture Heritage and Museology, Zhejiang University, Hangzhou 310028, PR China.

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2020.101803DOI Listing

Pressure injury image analysis with machine learning techniques: A systematic review on previous and possible future methods.

Artif Intell Med 2020 01 13;102:101742. Epub 2019 Nov 13.

Department of Computer Engineering and Computer Science, Duthie Center for Engineering, University of Louisville, Louisville, KY 40292, USA.

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101742DOI Listing
January 2020
2.019 Impact Factor

An enhanced deep learning approach for brain cancer MRI images classification using residual networks.

Artif Intell Med 2020 01 10;102:101779. Epub 2019 Dec 10.

Department of Computer Science, Faculty of Graduate Studies for Statistical Research, Cairo University, Cairo, Egypt.

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101779DOI Listing
January 2020

Predicting dementia with routine care EMR data.

Artif Intell Med 2020 01 5;102:101771. Epub 2019 Dec 5.

Regenstrief Institute, Inc., 1101 W. 10th Street, Indianapolis, IN 46202, USA; Indiana University School of Medicine, Center for Aging Research, 340 W. 10th Street, Suite 6200, Indianapolis, IN 46202, USA.

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101771DOI Listing
January 2020

Evidence of the benefits, advantages and potentialities of the structured radiological report: An integrative review.

Artif Intell Med 2020 01 25;102:101770. Epub 2019 Nov 25.

University Hospital of Brasilia (HUB), Distrito Federal, Brasília, Brazil.

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101770DOI Listing
January 2020

Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors.

Artif Intell Med 2020 01 27;102:101769. Epub 2019 Nov 27.

Feedback Medical Ltd., Broadway, Bourn, Cambridge CB23 2TA, UK. Electronic address:

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101769DOI Listing
January 2020

An improved fuzzy set-based multifactor dimensionality reduction for detecting epistasis.

Artif Intell Med 2020 01 22;102:101768. Epub 2019 Nov 22.

Department of Electronic Engineering, National Kaohsiung University of Science and Technology, No. 415, Jiangong Rd., Sanmin Dist., Kaohsiung City, 80778, Taiwan. Electronic address:

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101768DOI Listing
January 2020

SemBioNLQA: A semantic biomedical question answering system for retrieving exact and ideal answers to natural language questions.

Artif Intell Med 2020 01 28;102:101767. Epub 2019 Nov 28.

National School of Applied Sciences, Ibn Tofail University, Kenitra, Morocco; Laboratory of Informatics and Modeling, FSDM, Sidi Mohammed Ben Abdellah University, Fez, Morocco.

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. Here, we present a semantic biomedical QA system named SemBioNLQA which has the ability to handle the kinds of yes/no, factoid, list, and summary natural language questions. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101767DOI Listing
January 2020

Electroencephalogram based communication system for locked in state person using mentally spelled tasks with optimized network model.

Artif Intell Med 2020 01 19;102:101766. Epub 2019 Nov 19.

School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar (Dt), India.

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101766DOI Listing
January 2020

DESIGN AND DEVELOPMENT OF HUMAN COMPUTER INTERFACE USING ELECTROOCULOGRAM WITH DEEP LEARNING.

Artif Intell Med 2020 01 21;102:101765. Epub 2019 Nov 21.

School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar (Dt), India.

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101765DOI Listing
January 2020

Deep supervised learning with mixture of neural networks.

Artif Intell Med 2020 01 18;102:101764. Epub 2019 Nov 18.

Beijing Institute of Geriatrics, Beijing Hospital, Ministry of Health, Beijing, PR China.

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101764DOI Listing
January 2020

State recognition of decompressive laminectomy with multiple information in robot-assisted surgery.

Artif Intell Med 2020 01 16;102:101763. Epub 2019 Nov 16.

Beijing Jishuitan Hospital, Beijing, 100035, China. Electronic address:

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101763DOI Listing
January 2020
2.019 Impact Factor

Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review.

Artif Intell Med 2020 01 17;102:101762. Epub 2019 Nov 17.

IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Portugal. Electronic address:

Motivation: Emergency Departments' (ED) modern triage systems implemented worldwide are solely based upon medical knowledge and experience. This is a limitation of these systems, since there might be hidden patterns that can be explored in big volumes of clinical historical data. Intelligent techniques can be applied to these data to develop clinical decision support systems (CDSS) thereby providing the health professionals with objective criteria. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101762DOI Listing
January 2020

A modular cluster based collaborative recommender system for cardiac patients.

Artif Intell Med 2020 01 16;102:101761. Epub 2019 Nov 16.

Department of Computer Engineering, University of Engineering and Technology Taxila, Pakistan.

In the last few years, hospitals have been collecting a large amount of health related digital data for patients. This includes clinical test reports, treatment updates and disease diagnosis. The information extracted from this data is used for clinical decisions and treatment recommendations. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101761DOI Listing
January 2020

Multi-objective evolutionary design of antibiotic treatments.

Artif Intell Med 2020 01 17;102:101759. Epub 2019 Nov 17.

University of Stirling, Stirling, Scotland, UK.

Antibiotic resistance is one of the major challenges we face in modern times. Antibiotic use, especially their overuse, is the single most important driver of antibiotic resistance. Efforts have been made to reduce unnecessary drug prescriptions, but limited work is devoted to optimising dosage regimes when they are prescribed. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101759DOI Listing
January 2020

Ophthalmic diagnosis using deep learning with fundus images - A critical review.

Artif Intell Med 2020 01 22;102:101758. Epub 2019 Nov 22.

Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Ontario, Canada; Department of Systems Design Engineering, University of Waterloo, Ontario, Canada.

An overview of the applications of deep learning for ophthalmic diagnosis using retinal fundus images is presented. We describe various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk, optic cup, blood vessels as well as detection of lesions are reviewed. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101758DOI Listing
January 2020

Skin cancer diagnosis based on optimized convolutional neural network.

Artif Intell Med 2020 01 8;102:101756. Epub 2019 Nov 8.

University of Georgia, Athens, USA.

Early detection of skin cancer is very important and can prevent some skin cancers, such as focal cell carcinoma and melanoma. Although there are several reasons that have bad impacts on the detection precision. Recently, the utilization of image processing and machine vision in medical applications is increasing. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101756DOI Listing
January 2020

Signal identification system for developing rehabilitative device using deep learning algorithms.

Artif Intell Med 2020 01 8;102:101755. Epub 2019 Nov 8.

Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar (Dt). India.

Paralyzed patients were increasing day by day. Some of the neurodegenerative diseases like amyotrophic lateral sclerosis, Brainstem Leison, Stupor and Muscular dystrophy affect the muscle movements in the body. The affected persons were unable to migrate. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101755DOI Listing
January 2020

Optimized artificial neural network based performance analysis of wheelchair movement for ALS patients.

Artif Intell Med 2020 01 9;102:101754. Epub 2019 Nov 9.

Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar (Dt), India. Electronic address:

Individuals with neurodegenerative attacks loose the entire motor neuron movements. These conditions affect the individual actions like walking, speaking impairment and totally make the person in to locked in state (LIS). To overcome the miserable condition the person need rehabilitation devices through a Brain Computer Interfaces (BCI) to satisfy their needs. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101754DOI Listing
January 2020

Artificial intelligence and the future of psychiatry: Insights from a global physician survey.

Artif Intell Med 2020 01 18;102:101753. Epub 2019 Nov 18.

Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, United States. Electronic address:

Background: Futurists have predicted that new autonomous technologies, embedded with artificial intelligence (AI) and machine learning (ML), will lead to substantial job losses in many sectors disrupting many aspects of healthcare. Mental health appears ripe for such disruption given the global illness burden, stigma, and shortage of care providers.

Objective: To characterize the global psychiatrist community's opinion regarding the potential of future autonomous technology (referred to here as AI/ML) to replace key tasks carried out in mental health practice. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101753DOI Listing
January 2020

Artificial plant optimization algorithm to detect heart rate & presence of heart disease using machine learning.

Artif Intell Med 2020 01 8;102:101752. Epub 2019 Nov 8.

Indira Gandhi Delhi Technical University, Delhi, India. Electronic address:

In today's world, cardiovascular diseases are prevalent becoming the leading cause of death; more than half of the cardiovascular diseases are due to Coronary Heart Disease (CHD) which generates the demand of predicting them timely so that people can take precautions or treatment before it becomes fatal. For serving this purpose a Modified Artificial Plant Optimization (MAPO) algorithm has been proposed which can be used as an optimal feature selector along with other machine learning algorithms to predict the heart rate using the fingertip video dataset which further predicts the presence or absence of Coronary Heart Disease in an individual at the moment. Initially, the video dataset has been pre-processed, noise is filtered and then MAPO is applied to predict the heart rate with a Pearson correlation and Standard Error Estimate of 0. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101752DOI Listing
January 2020

A methodology based on multiple criteria decision analysis for combining antibiotics in empirical therapy.

Artif Intell Med 2020 01 13;102:101751. Epub 2019 Nov 13.

University Hospital of Getafe, Madrid, Spain.

Background: The current situation of critical progression in resistance to more effective antibiotics has forced the reuse of old highly toxic antibiotics and, for several reasons, the extension of the indications of combined antibiotic therapy as alternative options to broad spectrum empirical mono-therapy. A key aspect for selecting an appropriate and adequate antimicrobial therapy is that prescription must be based on local epidemiology and knowledge since many aspects, such as prevalence of microorganisms and effectiveness of antimicrobials, change from hospitals, or even areas and services within a single hospital. Therefore, the selection of combinations of antibiotics requires the application of a methodology that provides objectivity, completeness and reproducibility to the analysis of the detailed microbiological, epidemiological, pharmacological information on which to base a rational and reasoned choice. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101751DOI Listing
January 2020

Prediction of fetal weight at varying gestational age in the absence of ultrasound examination using ensemble learning.

Artif Intell Med 2020 01 17;102:101748. Epub 2019 Nov 17.

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; School of Electrical and Electronic Engineering, University of Adelaide, Adelaide, Australia. Electronic address:

Obstetric ultrasound examination of physiological parameters has been mainly used to estimate the fetal weight during pregnancy and baby weight before labour to monitor fetal growth and reduce prenatal morbidity and mortality. However, the problem is that ultrasound estimation of fetal weight is subject to population's difference, strict operating requirements for sonographers, and poor access to ultrasound in low-resource areas. Inaccurate estimations may lead to negative perinatal outcomes. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2019.101748DOI Listing
January 2020