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Implementation of artificial intelligence in medicine: status analysis and development suggestions
The objective of this study is to investigate public perceptions, receptivity, and demands regarding the implementation of medical AI. An online questionnaire was designed to investigate the perceptions, receptivity, and demands of general public regarding medical AI between October 13 and October 30, 2018. The distributions of the current achievements, public perceptions, receptivity, and demands among individuals in different lines of work (i.e., healthcare vs non-healthcare) and different age groups were assessed by performing descriptive statistics. The factors associated with public receptivity of medical AI were asse...
Source: Artificial Intelligence in Medicine - December 20, 2019 Category: Bioinformatics Source Type: research

Ophthalmic diagnosis using deep learning with fundus images – A critical review
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. Recent deep learning models for classification of diseases such as age-related macular degeneration, glaucoma, and diabetic retinopathy are also discussed. Important critical insights and future research directions are given. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - December 14, 2019 Category: Bioinformatics Source Type: research

An Enhanced Deep Learning Approach for Brain Cancer MRI Images Classification using Residual Networks
Publication date: Available online 10 December 2019Source: Artificial Intelligence in MedicineAuthor(s): Sarah Ali Abdelaziz Ismael, Ammar Mohammed, Hesham HefnyAbstractCancer is the second leading cause of death after cardiovascular diseases. Out of all types of cancer, brain cancer has the lowest survival rate. Brain tumors can have different types depending on their shape, texture, and location. Proper diagnosis of the tumor type enables the doctor to make the correct treatment choice and help save the patient's life. There is a high need in the Artificial Intelligence field for a Computer Assisted Diagnosis (CAD) syste...
Source: Artificial Intelligence in Medicine - December 11, 2019 Category: Bioinformatics Source Type: research

Editorial Board
Publication date: November 2019Source: Artificial Intelligence in Medicine, Volume 101Author(s): (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - December 7, 2019 Category: Bioinformatics Source Type: research

Predicting dementia with routine care EMR data
Publication date: Available online 5 December 2019Source: Artificial Intelligence in MedicineAuthor(s): Zina Ben Miled, Kyle Haas, Christopher M. Black, Rezaul Karim Khandker, Vasu Chandrasekaran, Richard Lipton, Malaz A. BoustaniAbstractOur 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...
Source: Artificial Intelligence in Medicine - December 5, 2019 Category: Bioinformatics Source Type: research

Signal identification system for developing rehabilitative device using deep learning algorithms
Publication date: January 2020Source: Artificial Intelligence in Medicine, Volume 102Author(s): Wenping Tang, Aiqun Wang, S. Ramkumar, Radeep Krishna Radhakrishnan NairAbstractParalyzed 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. To overcome from their problem they need some assistive technology with the help of bio signals. Electrooculogram (EOG) based Human Computer Interaction (HCI) is one of the technique used in recent ...
Source: Artificial Intelligence in Medicine - December 5, 2019 Category: Bioinformatics Source Type: research

Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors
Publication date: Available online 27 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Jakub Nalepa, Pablo Ribalta Lorenzo, Michal Marcinkiewicz, Barbara Bobek-Billewicz, Pawel Wawrzyniak, Maksym Walczak, Michal Kawulok, Wojciech Dudzik, Krzysztof Kotowski, Izabela Burda, Bartosz Machura, Grzegorz Mrukwa, Pawel Ulrych, Michael P. HayballAbstractDynamic 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...
Source: Artificial Intelligence in Medicine - November 28, 2019 Category: Bioinformatics Source Type: research

Medical Knowledge Embedding Based on Recursive Neural Network for Multi-Disease Diagnosis
Publication date: Available online 28 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Jingchi Jiang, Huanzheng Wang, Jing Xie, Xitong Guo, Yi Guan, Qiubin YuAbstractThe representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models. Although symbolic representation enables quantitative reasoning with statistical probability, it is difficult to utilize with machine learning models as they perform numerical operations. In contrast, knowledge embedding (i.e., high-dimensional and continuous vectors) is a feasible approach...
Source: Artificial Intelligence in Medicine - November 28, 2019 Category: Bioinformatics Source Type: research

SemBioNLQA: A semantic biomedical question answering system for retrieving exact and ideal answers to natural language questions
Publication date: Available online 28 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Mourad Sarrouti, Said Ouatik El AlaouiAbstractBackground and objectiveQuestion 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 term...
Source: Artificial Intelligence in Medicine - November 28, 2019 Category: Bioinformatics Source Type: research

Evidence of the benefits, advantages and potentialities of the structured radiological report: an integrative review
Publication date: Available online 25 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Douglas M. Rocha, Lourdes M. Brasil, Janice M. Lamas, Glécia V.S. Luz, Simônides S. BacelarAbstractThe 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, ...
Source: Artificial Intelligence in Medicine - November 26, 2019 Category: Bioinformatics Source Type: research

An improved fuzzy set-based multifactor dimensionality reduction for detecting epistasis
ConclusionFSMDR successfully detected significant epistasis of coronary artery disease in the Wellcome Trust Case Control Consortium data set. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - November 24, 2019 Category: Bioinformatics Source Type: research

Design and development of human computer interface using electrooculogram with deep learning
Publication date: Available online 21 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Geer Teng, Yue He, Hengjun Zhao, Dunhu Liu, Jin Xiao, S. RamkumarAbstractToday’s life assistive devices were playing significant role in our life to communicate with others. In that modality Human Computer Interface (HCI) based Electrooculogram (EOG) playing vital part. By using this method we can able to overcome the conventional methods in terms of performance and accuracy. To overcome such problem we analyze the EOG signal from twenty subjects to design nine states EOG based HCI using five electrodes system to meas...
Source: Artificial Intelligence in Medicine - November 22, 2019 Category: Bioinformatics Source Type: research

Ophthalmic Diagnosis Using Deep Learning with Fundus Images - A Critical Review
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. Recent deep learning models for classification of diseases such as age-related macular degeneration, glaucoma and diabetic retinopathy are also discussed. Important critical insights and future research directions have been given. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - November 22, 2019 Category: Bioinformatics Source Type: research

Electroencephalogram Based Communication System for Locked In State Person Using Mentally Spelled Tasks with Optimized Network Model
Publication date: Available online 19 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Xu Xiaoxiao, Luo Bin, S. Ramkumar, S Saravanan, M. Sundar Prakash Balaji, S. Dhanasekaran, J. ThimmiarajaAbstractDue 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 feat...
Source: Artificial Intelligence in Medicine - November 21, 2019 Category: Bioinformatics Source Type: research

Disease phenotype synonymous prediction through network representation learning from PubMed database
Publication date: Available online 19 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Shiwen Ma, Kuo Yang, Ning Wang, Qiang Zhu, Zhuye Gao, Runshun Zhang, Baoyan Liu, Xuezhong ZhouAbstractSynonym mapping between phenotype concepts from different terminologies is difficult because terminology databases have been developed largely independently. Existing maps of synonymous phenotype concepts from different terminology databases are highly incomplete, and manually mapping is time consuming and laborious. Therefore, building an automatic method for predictive mapping of synonymous phenotypes is of special im...
Source: Artificial Intelligence in Medicine - November 19, 2019 Category: Bioinformatics Source Type: research