1,220 research outputs found

    Half a century of computer methods and programs in biomedicine: A bibliometric analysis from 1970 to 2017

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    ยฉ 2019 Background and Objective: Computer Methods and Programs in Biomedicine (CMPB) is a leading international journal that presents developments about computing methods and their application in biomedical research. The journal published its first issue in 1970. In 2020, the journal celebrates the 50th anniversary. Motivated by this event, this article presents a bibliometric analysis of the publications of the journal during this period (1970โ€“2017). Methods: The objective is to identify the leading trends occurring in the journal by analysing the most cited papers, keywords, authors, institutions and countries. For doing so, the study uses the Web of Science Core Collection database. Additionally, the work presents a graphical mapping of the bibliographic information by using the visualization of similarities (VOS) viewer software. This is done to analyze bibliographic coupling, co-citation and co-occurrence of keywords. Results: CMPB is identified as a leading and core journal for biomedical researchers. The journal is strongly connected to IEEE Transactions on Biomedical Engineering and IEEE Transactions on Medical Imaging. Paper from Wang, Jacques, Zheng (published in 1995) is its most cited document. The top author in this journal is James Geoffrey Chase and the top contributing institution is Uppsala U (Sweden). Most of the papers in CMPB are from the USA followed by the UK and Italy. China and Taiwan are the only Asian countries to appear in the top 10 publishing in CMPB. A keyword co-occurrences analysis revealed strong co-occurrences for classification, picture archiving and communication system (PACS), heart rate variability, survival analysis and simulation. Keywords analysis for the last decade revealed that machine learning for a variety of healthcare problems (including image processing and analysis) dominated other research fields in CMPB. Conclusions: It can be concluded that CMPB is a world-renowned publication outlet for biomedical researchers which has been growing in a number of publications since 1970. The analysis also conclude that the journal is very international with publications from all over the world although today European countries are the most productive ones

    A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks

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    BACKGROUND: The lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment. METHODS: A new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels. FCNs are then mapped into the form of undirected graphs and subjected to extraction of graph theory based features. An unsupervised labeling technique based on Gaussian mixtures model (GMM) is then used to delineate the pediatric epilepsy group from the control group. RESULTS:The study results show the existence of a statistically significant difference (p \u3c 0.0001) between the mean FCNs of PC and PE groups. The system was able to diagnose pediatric epilepsy subjects with the accuracy of 88.8% with 81.8% sensitivity and 100% specificity purely based on exploration of associations among brain cortical regions and without a priori knowledge of diagnosis. CONCLUSIONS:The current study created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed

    Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review

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    Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, the process of diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist the specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We conclude by suggesting future approaches to detecting ASDs using AI techniques and MRI neuroimaging

    ์˜ํ•™ ์—ฐ๊ตฌ์—์„œ์˜ ๊ณผํ•™์  ์ฆ๊ฑฐ์˜ ํ™œ์šฉ์„ ์œ„ํ•œ ์‹œ๊ฐ์  ๋ถ„์„ ์‹œ์Šคํ…œ ๋””์ž์ธ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2022. 8. ์„œ์ง„์šฑ.Evidence-based medicine, "the conscientious, explicit, and judicious use of current best evidence in healthcare and medical research" [98], is one of the most widely accepted medical paradigms of modern times. Searching, reviewing, and synthesizing reliable and high-quality scientific evidence is the key step for the paradigm. However, despite the widespread use of the EBM paradigm, challenges remain in applying Evidence-based medicine protocols to medical research. One of the barriers to applying the best scientific evidence to medical research is the severe literature and clinical data overload that causes the evidence-based tasks to be tremendous time-consuming tasks that require vast human effort. In this dissertation, we aim to employ visual analytics approaches to address the challenges of searching and reviewing massive scientific evidence in medical research. To overcome the burden and facilitate handling scientific evidence in medical research, we conducted three design studies and implemented novel visual analytics systems for laborious evidence-based tasks. First, we designed PLOEM, a novel visual analytics system to aid evidence synthesis, an essential step in Evidence-Based medicine, and generate an Evidence Map in a standardized method. We conducted a case study with an oncologist with years of evidence-based medicine experience. In the second study, we conducted a preliminary survey with 76 medical doctors to derive the design requirements for a biomedical literature search. Based on the results, We designed EEEVis, an interactive visual analytic system for biomedical literature search tasks. The system enhances the PubMed search result with several bibliographic visualizations and PubTator annotations. We performed a user study to evaluate the designs with 24 medical doctors and presented the design guidelines and challenges for a biomedical literature search system design. The third study presents GeneVis, a visual analytics system to identify and analyze gene expression signatures across major cancer types. A task that cancer researchers utilize to discover biomarkers in precision medicine. We conducted four case studies with domain experts in oncology and genomics. The study results show that the system can facilitate the task and provide new insights from the data. Based on the three studies of this dissertation, we conclude that carefully designed visual analytics approaches can provide an enhanced understanding and support medical researchers for laborious evidence-based tasks in medical research.๊ทผ๊ฑฐ์ค‘์‹ฌ์˜ํ•™(Evidence-Based Medicine)์ด๋ž€ "์ž„์ƒ ์น˜๋ฃŒ ๋ฐ ์˜ํ•™ ์—ฐ๊ตฌ์—์„œ ํ˜„์žฌ ์กด์žฌํ•˜๋Š” ์ตœ๊ณ ์˜ ์ฆ๊ฑฐ๋ฅผ ์–‘์‹ฌ์ ์ด๊ณ , ๋ช…๋ฐฑํ•˜๋ฉฐ, ๋ถ„๋ณ„ ์žˆ๊ฒŒ ์ด์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก "์ด๋ฉฐ [98], ํ˜„๋Œ€ ์˜ํ•™์—์„œ ๊ฐ€์žฅ ๋„๋ฆฌ ๋ฐ›์•„๋“ค์—ฌ์ง€๋Š” ์˜ํ•™ ํŒจ๋Ÿฌ๋‹ค์ž„์ด๋‹ค. ์‹ ๋ขฐํ•  ์ˆ˜ ์žˆ๋Š” ๊ณ ์ˆ˜์ค€์˜ ๊ณผํ•™์  ๊ทผ๊ฑฐ๋ฅผ ๊ฒ€์ƒ‰, ๊ฒ€ํ† , ํ•ฉ์„ฑํ•˜๋Š” ๊ฒƒ์ด์•ผ ๋ง๋กœ ๊ทผ๊ฑฐ์ค‘์‹ฌ์˜ํ•™์˜ ํ•ต์‹ฌ์ด๋‹ค. ํ•˜์ง€๋งŒ, ๊ทผ๊ฑฐ์ค‘์‹ฌ์˜ํ•™์ด ์ด๋ฏธ ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ์˜ํ•™ ์—ฐ๊ตฌ์— ๊ทผ๊ฑฐ์ค‘์‹ฌ์˜ํ•™์˜ ํ”„๋กœํ† ์ฝœ์„ ์‹ค์ฒœํ•˜๋Š” ๋ฐ์—๋Š” ์—ฌ์ „ํžˆ ๋งŽ์€ ์–ด๋ ค์›€์ด ๋”ฐ๋ฅธ๋‹ค. ์˜๋ฃŒ ๋ฌธํ—Œ ์ •๋ณด, ์ž„์ƒ ์ •๋ณด ๋ฐ ์œ ์ „์ฒดํ•™ ์ •๋ณด๊นŒ์ง€ ์—ฐ๊ตฌ์ž๊ฐ€ ๊ฒ€ํ† ํ•ด์•ผ ํ•  ๊ทผ๊ฑฐ์˜ ์–‘์€ ๋ฐฉ๋Œ€ํ•˜๋ฉฐ ๊ด‘๋ฒ”์œ„ํ•˜๋‹ค. ๋˜ํ•œ ์˜ํ•™๊ณผ ๊ธฐ์ˆ ์˜ ๋ฐœ์ „์œผ๋กœ ์ธํ•ด ์ ์ฐจ ๋” ๋น ๋ฅธ ์†๋„๋กœ ๋Š˜์–ด๋‚˜๊ณ  ์žˆ๊ธฐ์—, ์ด๋ฅผ ๋ชจ๋‘ ์—„๋ฐ€ํžˆ ๊ฒ€ํ† ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋ง‰๋Œ€ํ•œ ์–‘์˜ ์‹œ๊ฐ„๊ณผ ์ธ๋ ฅ์ด ์žˆ์–ด์•ผ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์‹œ๊ฐ์  ๋ถ„์„ ๋ฐฉ๋ฒ•๋ก ์„ ์ ‘๋ชฉํ•˜์—ฌ ์˜ํ•™ ์—ฐ๊ตฌ์—์„œ ๋ฐฉ๋Œ€ํ•œ ๊ณผํ•™์  ์ฆ๊ฑฐ๋ฅผ ๊ฒ€์ƒ‰ํ•˜๊ณ  ๊ฒ€ํ† ํ•  ์‹œ ๋ฐœ์ƒํ•˜๋Š” ๋ง‰๋Œ€ํ•œ ์ธ์  ์ž์›์˜ ๊ณผ๋ถ€ํ•˜ ๋ฌธ์ œ๋ฅผ ์™„ํ™”ํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•˜์—ฌ ๊ทผ๊ฑฐ์ค‘์‹ฌ์˜ํ•™์˜ ์ ˆ์ฐจ ์ค‘ ํŠนํžˆ ์ธ๋ ฅ ์†Œ๋ชจ๊ฐ€ ๋ง‰์‹ฌํ•œ ์ ˆ์ฐจ๋“ค์„ ์„ ์ •ํ•˜๊ณ , ์ด๋Ÿฌํ•œ ๋‚œ๊ด€์„ ๊ทน๋ณตํ•˜๊ณ  ๋ณด๋‹ค ํšจ์œจ์ ์ด๊ณ  ํšจ๊ณผ์ ์œผ๋กœ ๋ฐ์ดํ„ฐ์—์„œ ์œ ์˜๋ฏธํ•œ ์ •๋ณด๋ฅผ ๋„์ถœํ•  ์ˆ˜ ์žˆ๊ฒŒ๋” ๋ณด์กฐํ•˜๋Š” ์„ธ ๊ฐ€์ง€ ์‹œ๊ฐ์  ๋ถ„์„ ์‹œ์Šคํ…œ๋“ค์„ ๊ตฌํ˜„ํ•˜์˜€์œผ๋ฉฐ, ๊ฐ๊ฐ์˜ ์‹œ์Šคํ…œ์— ๊ด€ํ•œ ๋””์ž์ธ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์šฐ์„  ์ฒซ ๋””์ž์ธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ทผ๊ฑฐ์ค‘์‹ฌ์˜ํ•™ ์—ฐ๊ตฌ์— ์žˆ์–ด ํ•„์ˆ˜์  ๋‹จ๊ณ„์ธ ๊ทผ๊ฑฐ ํ•ฉ์„ฑ ๋ฐฉ๋ฒ•๋ก ์˜ ํ•˜๋‚˜์ธ ๊ทผ๊ฑฐ ๋งคํ•‘(Evidence Mapping) ๊ณผ์ •์„ ์ง€์›ํ•˜๊ธฐ ์œ„ํ•œ ์‹œ๊ฐ์  ๋ถ„์„ ์‹œ์Šคํ…œ PLOEM์„ ์„ค๊ณ„ํ–ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด๋ฅผ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค๋…„๊ฐ„์˜ ๊ทผ๊ฑฐ ๊ธฐ๋ฐ˜ ์˜๋ฃŒ ๊ฒฝํ—˜์ด ์žˆ๋Š” ์ข…์–‘ํ•™์ž์™€ ํ•จ๊ป˜ ์‚ฌ๋ก€ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ–ˆ๋‹ค. ๋‘ ๋ฒˆ์งธ ๋””์ž์ธ ์—ฐ๊ตฌ์—์„œ๋Š” ์˜ํ•™ ๋ฌธํ—Œ ๊ฒ€์ƒ‰ ์‹œ์Šคํ…œ์˜ ์š”๊ตฌ์‚ฌํ•ญ ๋ถ„์„์„ ์œ„ํ•ด ์ด 76๋ช…์˜ ์˜์‚ฌ๋ฅผ ์ƒ๋Œ€๋กœ ์„ค๋ฌธ์กฐ์‚ฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๊ณ , ์ด๋Ÿฌํ•œ ๋ถ„์„์„ ๋ฐ”ํƒ•์œผ๋กœ ๋Œ€ํ™”ํ˜• ์‹œ๊ฐ์  ๋ถ„์„ ์‹œ์Šคํ…œ์ธ EEEVis๋ฅผ ์„ค๊ณ„ํ–ˆ๋‹ค. ์ด ์‹œ์Šคํ…œ์€ ์—ฌ๋Ÿฌ ์ข…์˜ ์„œ์ง€ ์ •๋ณด ์‹œ๊ฐํ™” ์ธํ„ฐํŽ˜์ด์Šค์™€ PubTator์˜ ์ฃผ์„ ์ •๋ณด๋ฅผ ํ™œ์šฉํ•˜์—ฌ PubMed ๊ฒ€์ƒ‰ ์—”์ง„์˜ ๊ฒ€์ƒ‰ ๊ฒฐ๊ณผ๋ฅผ ์ฆ๊ฐ•ํ•˜๋Š” ์‹œ์Šคํ…œ์ด๋ฉฐ, ์ด๋ฅผ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ์ด 24๋ช…์˜ ์˜์‚ฌ์™€ ํ•จ๊ป˜ ์‚ฌ์šฉ์ž ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ด ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์˜ํ•™ ๋ฌธํ—Œ ๊ฒ€์ƒ‰ ์‹œ์Šคํ…œ์— ๋Œ€ํ•œ ์„ค๊ณ„ ์ง€์นจ๊ณผ ๊ณผ์ œ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์„ธ ๋ฒˆ์งธ ๋””์ž์ธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ž„์˜์˜ ์œ ์ „์ž๊ตฐ์˜ ์œ ์ „์ž ๋ฐœํ˜„ ํŒจํ„ด์„ ์ฃผ์š” ์•” ์œ ํ˜•์— ๋”ฐ๋ผ ์‹œ๊ฐํ™”ํ•˜๊ณ  ๋ถ„์„ํ•  ์ˆ˜ ์žˆ๋Š” ์‹œ์Šคํ…œ์ธ GeneVis๋ฅผ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์•” ์œ ํ˜•์— ๋”ฐ๋ฅธ ์œ ์ „์ž ๋ฐœํ˜„ ํŒจํ„ด์˜ ๋ถ„์„๊ณผ ๋น„๊ต๋Š” ์•” ์—ฐ๊ตฌ์ž๋“ค์ด ์ •๋ฐ€ ์˜ํ•™์—์„œ ์ƒ์ฒด ์ง€ํ‘œ(Biomarker)๋ฅผ ๋ฐœ๊ฒฌํ•˜๊ธฐ ์œ„ํ•ด ๋นˆ๋ฒˆํžˆ ์ˆ˜ํ–‰ํ•˜๋Š” ์ž‘์—…์ด๋‹ค. ์šฐ๋ฆฌ๋Š” ์ข…์–‘ํ•™ ์ „๋ฌธ๊ฐ€ ๋ฐ ์œ ์ „์ฒดํ•™ ์ „๋ฌธ๊ฐ€ ์ด 4์ธ์„ ๋Œ€์ƒ์œผ๋กœ ์‚ฌ๋ก€ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๊ณ , ๊ทธ ๊ฒฐ๊ณผ GeneVis๊ฐ€ ํ•ด๋‹น ์ž‘์—…์„ ๋” ์ˆ˜์›”ํ•˜๊ฒŒ ์ˆ˜ํ–‰ํ•˜๋Š” ๊ฒƒ๊ณผ ๊ธฐ์กด์˜ ๋ฐ์ดํ„ฐ์—์„œ ์ƒˆ๋กœ์šด ์ •๋ณด๋ฅผ ๋„์ถœํ•˜๋Š” ๊ฒƒ์— ๋„์›€์ด ๋˜์—ˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์œ„์˜ ์„ธ ๋””์ž์ธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ, ๋ณธ ๋…ผ๋ฌธ์€ ์‚ฌ์šฉ์ž ๋ถ„์„๊ณผ ์ž‘์—… ๋ถ„์„์„ ๋™๋ฐ˜ํ•œ ์‹œ๊ฐ์  ๋ถ„์„ ๋ฐฉ๋ฒ•๋ก ์ด ์˜ํ•™ ์—ฐ๊ตฌ์˜ ๊ทผ๊ฑฐ ๊ด€๋ จ ์ž‘์—…์˜ ์–ด๋ ค์›€์„ ํ•ด์†Œํ•˜๊ณ , ๋ถ„์„ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•œ ๋ณด๋‹ค ๋‚˜์€ ์ดํ•ด๋ฅผ ์ œ๊ณตํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ๊ฒฐ๋ก  ๋‚ด๋ฆฐ๋‹ค.CHAPTER1 Introduction 1 1.1 Background and Motivation 1 1.2 Dissertation Outline 5 CHAPTER2 Related Work 7 2.1 Evidence Mapping: Graphical Representation for a Scientific Evidence Landscape 7 2.2 Scientific Literature Visualizations and Bibliography Visualizations 9 2.3 Visual Anlytics Systems for Genomics Data sets and Research Tasks 10 CHAPTER3 PLOEM: An Interactive Visualization Tool for Effective Evidence Mapping with Biomedical literature 12 3.1 Introduction 12 3.2 Visual Representations and Interactions of PLOEM 14 3.2.1 Overview of the PICO Criteria 14 3.2.2 Trend Visualization with the Timeline view 17 3.2.3 Representing the PICO Co-occurrence with the Relation view 20 3.2.4 Study detail view 22 3.3 Usage Scenarios: Visualizing Various Study Sizes with PLOEM 23 3.4 Conclusion 24 CHAPTER4 EEEvis: Efficacy improvement in searching MEDLINE database using a novel PubMed visual analytic system 26 4.1 Introduction 26 4.1.1 Motivation 26 4.1.2 Preliminary Survey: A Questionnaire on conventional literature search methods 28 4.1.3 Design Requirements for Biomedical Literature Search Systems 36 4.2 System and Interface Implementation of EEEVis 37 4.2.1 System Overview 37 4.2.2 Bibliography Filters 40 4.2.3 Timeline View 41 4.2.4 Co-authorship Network View 43 4.2.5 Article List and Detail View 44 4.3 User Study 46 4.3.1 Participants 46 4.3.2 Procedures 48 4.3.3 Results and Observations 50 4.4 Discussion 54 4.4.1 Design Implications 56 4.4.2 Limitations and Future Work 57 4.5 Conclusions 59 CHAPTER5 GeneVis: A Visual Analytics Systemfor Gene Signature Analysis in Cancers 68 5.1 Motivation 68 5.2 System and Interface Implementation 69 5.2.1 System Overview 69 5.2.2 Gene Expression Detail View 71 5.2.3 Gene Vector Projection View 72 5.2.4 Gene x Cancer Type Heatmap view 74 5.2.5 User Interaction in Multiple Coordinated Views 76 5.3 Case Studies 76 5.3.1 Participants 76 5.3.2 Task and Procedures 76 5.3.3 Case1: Identifying SimilarGeneSignatures with TGFB1in Hallmark Gene Sets 80 5.3.4 Case2: Identifying Cluster Patterns in the HRD data set 81 5.3.5 Results 82 5.4 Summary 85 CHAPTER6 Conclusion and future work 86 6.1 Conclusion 86 6.2 Future Work 87 Abstract (Korean) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102๋ฐ•

    An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works

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    Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed the temporal and anterior lobes of hippocampus regions of brain get affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. The magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to obtain accurate diagnosis of SZ. This paper presents a comprehensive overview of studies conducted on automated diagnosis of SZ using MRI modalities. Main findings, various challenges, and future works in developing the automated SZ detection are described in this paper

    Advanced Brain Tumour Segmentation from MRI Images

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    Magnetic resonance imaging (MRI) is widely used medical technology for diagnosis of various tissue abnormalities, detection of tumors. The active development in the computerized medical image segmentation has played a vital role in scientific research. This helps the doctors to take necessary treatment in an easy manner with fast decision making. Brain tumor segmentation is a hot point in the research field of Information technology with biomedical engineering. The brain tumor segmentation is motivated by assessing tumor growth, treatment responses, computer-based surgery, treatment of radiation therapy, and developing tumor growth models. Therefore, computer-aided diagnostic system is meaningful in medical treatments to reducing the workload of doctors and giving the accurate results. This chapter explains the causes, awareness of brain tumor segmentation and its classification, MRI scanning process and its operation, brain tumor classifications, and different segmentation methodologies

    Augmented Reality and Health Informatics: A Study based on Bibliometric and Content Analysis of Scholarly Communication and Social Media

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    Healthcare outcomes have been shown to improve when technology is used as part of patient care. Health Informatics (HI) is a multidisciplinary study of the design, development, adoption, and application of IT-based innovations in healthcare services delivery, management, and planning. Augmented Reality (AR) is an emerging technology that enhances the userโ€™s perception and interaction with the real world. This study aims to illuminate the intersection of the field of AR and HI. The domains of AR and HI by themselves are areas of significant research. However, there is a scarcity of research on augmented reality as it applies to health informatics. Given both scholarly research and social media communication having contributed to the domains of AR and HI, research methodologies of bibliometric and content analysis on scholarly research and social media communication were employed to investigate the salient features and research fronts of the field. The study used Scopus data (7360 scholarly publications) to identify the bibliometric features and to perform content analysis of the identified research. The Altmetric database (an aggregator of data sources) was used to determine the social media communication for this field. The findings from this study included Publication Volumes, Top Authors, Affiliations, Subject Areas and Geographical Locations from scholarly publications as well as from a social media perspective. The highest cited 200 documents were used to determine the research fronts in scholarly publications. Content Analysis techniques were employed on the publication abstracts as a secondary technique to determine the research themes of the field. The study found the research frontiers in the scholarly communication included emerging AR technologies such as tracking and computer vision along with Surgical and Learning applications. There was a commonality between social media and scholarly communication themes from an applications perspective. In addition, social media themes included applications of AR in Healthcare Delivery, Clinical Studies and Mental Disorders. Europe as a geographic region dominates the research field with 50% of the articles and North America and Asia tie for second with 20% each. Publication volumes show a steep upward slope indicating continued research. Social Media communication is still in its infancy in terms of data extraction, however aggregators like Altmetric are helping to enhance the outcomes. The findings from the study revealed that the frontier research in AR has made an impact in the surgical and learning applications of HI and has the potential for other applications as new technologies are adopted

    Information retrieval and text mining technologies for chemistry

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    Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Communityโ€™s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Conselleriฬa de Cultura, Educacioฬn e Ordenacioฬn Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Inฬƒigo Garciaฬ -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio
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