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Feng J, Zhang R, Chen D, Shi L. A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound Reports. Biomimetics (Basel) 2023; 8:560. [PMID: 38132500 PMCID: PMC10741754 DOI: 10.3390/biomimetics8080560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 10/30/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023] Open
Abstract
Knowledge graph visualization in ultrasound reports is essential for enhancing medical decision making and the efficiency and accuracy of computer-aided analysis tools. This study aims to propose an intelligent method for analyzing ultrasound reports through knowledge graph visualization. Firstly, we provide a novel method for extracting key term networks from the narrative text in ultrasound reports with high accuracy, enabling the identification and annotation of clinical concepts within the report. Secondly, a knowledge representation framework based on ultrasound reports is proposed, which enables the structured and intuitive visualization of ultrasound report knowledge. Finally, we propose a knowledge graph completion model to address the lack of entities in physicians' writing habits and improve the accuracy of visualizing ultrasound knowledge. In comparison to traditional methods, our proposed approach outperforms the extraction of knowledge from complex ultrasound reports, achieving a significantly higher extraction index (η) of 2.69, surpassing the general pattern-matching method (2.12). In comparison to other state-of-the-art methods, our approach achieves the highest P (0.85), R (0.89), and F1 (0.87) across three testing datasets. The proposed method can effectively utilize the knowledge embedded in ultrasound reports to obtain relevant clinical information and improve the accuracy of using ultrasound knowledge.
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Affiliation(s)
- Jiayi Feng
- Department of Information Management, Beijing Jiaotong University, Beijing 100044, China;
| | - Runtong Zhang
- Department of Information Management, Beijing Jiaotong University, Beijing 100044, China;
| | - Donghua Chen
- Department of Information Management, University of International Business and Economics, Beijing 100029, China;
| | - Lei Shi
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK;
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2
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Jost E, Kosian P, Jimenez Cruz J, Albarqouni S, Gembruch U, Strizek B, Recker F. Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology. J Clin Med 2023; 12:6833. [PMID: 37959298 PMCID: PMC10649694 DOI: 10.3390/jcm12216833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/17/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
Artificial intelligence (AI) has gained prominence in medical imaging, particularly in obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is considered cost effective and easily accessible but is time consuming and hindered by the need for specialized training. To overcome these limitations, AI models have been proposed for automated plane acquisition, anatomical measurements, and pathology detection. This study aims to overview recent literature on AI applications in OB/GYN US imaging, highlighting their benefits and limitations. For the methodology, a systematic literature search was performed in the PubMed and Cochrane Library databases. Matching abstracts were screened based on the PICOS (Participants, Intervention or Exposure, Comparison, Outcome, Study type) scheme. Articles with full text copies were distributed to the sections of OB/GYN and their research topics. As a result, this review includes 189 articles published from 1994 to 2023. Among these, 148 focus on obstetrics and 41 on gynecology. AI-assisted US applications span fetal biometry, echocardiography, or neurosonography, as well as the identification of adnexal and breast masses, and assessment of the endometrium and pelvic floor. To conclude, the applications for AI-assisted US in OB/GYN are abundant, especially in the subspecialty of obstetrics. However, while most studies focus on common application fields such as fetal biometry, this review outlines emerging and still experimental fields to promote further research.
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Affiliation(s)
- Elena Jost
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Philipp Kosian
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Jorge Jimenez Cruz
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Shadi Albarqouni
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
- Helmholtz AI, Helmholtz Munich, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Ulrich Gembruch
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Brigitte Strizek
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Florian Recker
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
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3
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Sadeghi-Ghyassi F, Damanabi S, Kalankesh LR, Van de Velde S, Feizi-Derakhshi MR, Hajebrahimi S. How are ontologies implemented to represent clinical practice guidelines in clinical decision support systems: protocol for a systematic review. Syst Rev 2022; 11:183. [PMID: 36042520 PMCID: PMC9429575 DOI: 10.1186/s13643-022-02063-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 08/23/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Clinical practice guidelines are statements which are based on the best available evidence, and their goal is to improve the quality of patient care. Integrating clinical practice guidelines into computer systems can help physicians reduce medical errors and help them to have the best possible practice. Guideline-based clinical decision support systems play a significant role in supporting physicians in their decisions. Meantime, system errors are the most critical concerns in designing decision support systems that can affect their performance and efficacy. A well-developed ontology can be helpful in this matter. The proposed systematic review will specify the methods, components, language of rules, and evaluation methods of current ontology-driven guideline-based clinical decision support systems. METHODS This review will identify literature through searching MEDLINE (via Ovid), PubMed, EMBASE, Cochrane Library, CINAHL, ScienceDirect, IEEEXplore, and ACM Digital Library. Gray literature, reference lists, and citing articles of the included studies will be searched. The quality of the included studies will be assessed by the mixed methods appraisal tool (MMAT-version 2018). At least two independent reviewers will perform the screening, quality assessment, and data extraction. A third reviewer will resolve any disagreements. Proper data analysis will be performed based on the type of system and ontology engineering evaluation data. DISCUSSION The study will provide evidence regarding applying ontologies in guideline-based clinical decision support systems. The findings of this systematic review will be a guide for decision support system designers and developers, technologists, system providers, policymakers, and stakeholders. Ontology builders can use the information in this review to build well-structured ontologies for personalized medicine. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42018106501.
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Affiliation(s)
- Fatemeh Sadeghi-Ghyassi
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.,Research Center for Evidence Based-Medicine, Iranian EBM Center: A Joanna Briggs Institute Center of Excellence, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Shahla Damanabi
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
| | - Leila R Kalankesh
- Health Services Management Research Center, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | | | - Sakineh Hajebrahimi
- Research Center for Evidence Based-Medicine, Iranian EBM Center: A Joanna Briggs Institute Center of Excellence, Tabriz University of Medical Sciences, Tabriz, Iran
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4
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Dhombres F, Bonnard J, Bailly K, Maurice P, Papageorghiou A, Jouannic JM. Contributions of artificial intelligence reported in Obstetrics and Gynecology journals: a systematic review. J Med Internet Res 2022; 24:e35465. [PMID: 35297766 PMCID: PMC9069308 DOI: 10.2196/35465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 02/11/2022] [Accepted: 03/15/2022] [Indexed: 11/13/2022] Open
Abstract
Background The applications of artificial intelligence (AI) processes have grown significantly in all medical disciplines during the last decades. Two main types of AI have been applied in medicine: symbolic AI (eg, knowledge base and ontologies) and nonsymbolic AI (eg, machine learning and artificial neural networks). Consequently, AI has also been applied across most obstetrics and gynecology (OB/GYN) domains, including general obstetrics, gynecology surgery, fetal ultrasound, and assisted reproductive medicine, among others. Objective The aim of this study was to provide a systematic review to establish the actual contributions of AI reported in OB/GYN discipline journals. Methods The PubMed database was searched for citations indexed with “artificial intelligence” and at least one of the following medical subject heading (MeSH) terms between January 1, 2000, and April 30, 2020: “obstetrics”; “gynecology”; “reproductive techniques, assisted”; or “pregnancy.” All publications in OB/GYN core disciplines journals were considered. The selection of journals was based on disciplines defined in Web of Science. The publications were excluded if no AI process was used in the study. Review, editorial, and commentary articles were also excluded. The study analysis comprised (1) classification of publications into OB/GYN domains, (2) description of AI methods, (3) description of AI algorithms, (4) description of data sets, (5) description of AI contributions, and (6) description of the validation of the AI process. Results The PubMed search retrieved 579 citations and 66 publications met the selection criteria. All OB/GYN subdomains were covered: obstetrics (41%, 27/66), gynecology (3%, 2/66), assisted reproductive medicine (33%, 22/66), early pregnancy (2%, 1/66), and fetal medicine (21%, 14/66). Both machine learning methods (39/66) and knowledge base methods (25/66) were represented. Machine learning used imaging, numerical, and clinical data sets. Knowledge base methods used mostly omics data sets. The actual contributions of AI were method/algorithm development (53%, 35/66), hypothesis generation (42%, 28/66), or software development (3%, 2/66). Validation was performed on one data set (86%, 57/66) and no external validation was reported. We observed a general rising trend in publications related to AI in OB/GYN over the last two decades. Most of these publications (82%, 54/66) remain out of the scope of the usual OB/GYN journals. Conclusions In OB/GYN discipline journals, mostly preliminary work (eg, proof-of-concept algorithm or method) in AI applied to this discipline is reported and clinical validation remains an unmet prerequisite. Improvement driven by new AI research guidelines is expected. However, these guidelines are covering only a part of AI approaches (nonsymbolic) reported in this review; hence, updates need to be considered.
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Affiliation(s)
- Ferdinand Dhombres
- Sorbonne University, Armand Trousseau University hospital, Fetal Medicine department, APHP, Armand Trousseau University hospital, Fetal Medicine department, APHP26 AV du Dr Arnold Netter, Paris, FR.,INSERM, Laboratory in Medical Informatics and Knowledge Engineering in e-Health (LIMICS), Paris, FR
| | - Jules Bonnard
- Sorbonne University, Institute for Intelligent Systems and Robotics (ISIR), Paris, FR
| | - Kévin Bailly
- Sorbonne University, Institute for Intelligent Systems and Robotics (ISIR), Paris, FR
| | - Paul Maurice
- Sorbonne University, Armand Trousseau University hospital, Fetal Medicine department, APHP, Paris, FR
| | - Aris Papageorghiou
- Oxford Maternal & Perinatal Health Institute, Green Templeton College, Oxford, GB
| | - Jean-Marie Jouannic
- Sorbonne University, Armand Trousseau University hospital, Fetal Medicine department, APHP, Paris, FR.,INSERM, Laboratory in Medical Informatics and Knowledge Engineering in e-Health (LIMICS), Paris, FR
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5
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Yang H, Wang R, Zhao L, Ye J, Li N, Kong L. Diagnosis and Analysis of Transabdominal and Intracavitary Ultrasound in Gynecological Acute Abdomen. Comput Math Methods Med 2021; 2021:9508838. [PMID: 35003327 DOI: 10.1155/2021/9508838] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 11/08/2021] [Accepted: 11/22/2021] [Indexed: 01/30/2023]
Abstract
In order to explore the effective diagnosis method of gynecological acute abdomen, this paper takes hospital gynecological acute abdomen patients as samples and selects gynecological acute abdomen patients admitted to the hospital to be included in this study. They are divided into transabdominal ultrasound group, intracavitary ultrasound group, and combined group. Moreover, this paper uses mathematical statistics to carry out sample statistics. The statistical data mainly include ectopic pregnancy, torsion of ovarian tumor pedicle, acute suppurative salpingitis, torsion of fallopian tube, hemorrhagic salpingitis, acute pelvic inflammatory disease, rupture of corpus luteum cyst, and diagnosis accuracy rate. In addition, this paper compares the diagnostic accuracy of the abdominal ultrasound group, the intracavitary ultrasound group, and the combined group. The experimental research shows that the combined ultrasound diagnosis method can effectively improve the accuracy of the diagnosis of gynecological acute abdomen.
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López-Úbeda P, Pomares-Quimbaya A, Díaz-Galiano MC, Schulz S. Collecting specialty-related medical terms: Development and evaluation of a resource for Spanish. BMC Med Inform Decis Mak 2021; 21:145. [PMID: 33947365 PMCID: PMC8094531 DOI: 10.1186/s12911-021-01495-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 04/03/2021] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Controlled vocabularies are fundamental resources for information extraction from clinical texts using natural language processing (NLP). Standard language resources available in the healthcare domain such as the UMLS metathesaurus or SNOMED CT are widely used for this purpose, but with limitations such as lexical ambiguity of clinical terms. However, most of them are unambiguous within text limited to a given clinical specialty. This is one rationale besides others to classify clinical text by the clinical specialty to which they belong. RESULTS This paper addresses this limitation by proposing and applying a method that automatically extracts Spanish medical terms classified and weighted per sub-domain, using Spanish MEDLINE titles and abstracts as input. The hypothesis is biomedical NLP tasks benefit from collections of domain terms that are specific to clinical subdomains. We use PubMed queries that generate sub-domain specific corpora from Spanish titles and abstracts, from which token n-grams are collected and metrics of relevance, discriminatory power, and broadness per sub-domain are computed. The generated term set, called Spanish core vocabulary about clinical specialties (SCOVACLIS), was made available to the scientific community and used in a text classification problem obtaining improvements of 6 percentage points in the F-measure compared to the baseline using Multilayer Perceptron, thus demonstrating the hypothesis that a specialized term set improves NLP tasks. CONCLUSION The creation and validation of SCOVACLIS support the hypothesis that specific term sets reduce the level of ambiguity when compared to a specialty-independent and broad-scope vocabulary.
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Affiliation(s)
| | | | | | - Stefan Schulz
- Medical University of Graz, Auenbruggerpl No 2, 8036 Graz, Austria
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7
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Behzadpour N, Ranjbar A, Azarpira N, Sattarahmady N. Development of a Composite of Polypyrrole-Coated Carbon Nanotubes as a Sonosensitizer for Treatment of Melanoma Cancer Under Multi-Step Ultrasound Irradiation. Ultrasound Med Biol 2020; 46:2322-2334. [PMID: 32522457 DOI: 10.1016/j.ultrasmedbio.2020.05.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Revised: 04/25/2020] [Accepted: 05/04/2020] [Indexed: 06/11/2023]
Abstract
Sonodynamic therapy (SDT) has established a novel route for treating solid cancers. Low-intensity ultrasound irradiation accompanied by a sonosensitizer has revealed remarkable advantages for cancer therapy such as targeted uptake, access to deeper tumors, insignificant side effects and invasiveness, compared with other therapeutic methods. In this study, we scrutinized synthesis and characterization of a polypyrrole-coated multi-walled carbon nanotubes composite (PPy@MWCNTs). PPy@MWCNTs can absorb ultrasound irradiation by both of its components, and it was introduced as a new sonosensitizer. The composite was characterized by field emission scanning electron microscopy (FESEM), and its ability to temperature elevation was explored. FESEM images revealed that PPy@MWCNTs comprised nanotubes of 36.3 ± 5.1 nm in diameter with up to several micrometer in length. Ultrasound irradiation at 1 MHz and 1.0 W cm-2 for 60 s in four steps led to an efficient SDT in vitro (16.3 ± 2.8°C temperature increment for 250 μg mL-1 of PPy@MWCNTs), in C540 (B16/F10) cell line and a melanoma tumor model in male balb/c mice. In vitro examinations revealed that PPy@MWCNTs represented a concentration-dependent cytotoxicity on multi-step ultrasound irradiation (a cell viability of 8.9% for 250 μg mL-1 of PPy@MWCNTs). Histologic analyses and tumor volume decrement after 10 d revealed detrimental SDT effects of PPy@MWCNTs on tumors (75% necrosis and 50% decrement in tumor volume). Thermal effects and reactive oxygen species generation were the reasons of the working function of PPy@MWCNTs in SDT.
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Affiliation(s)
- Niloufar Behzadpour
- Department of Medical Physics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran; Nanomedicine and Nanobiology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Aliyeh Ranjbar
- Nanomedicine and Nanobiology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; Student research committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Negar Azarpira
- Nanomedicine and Nanobiology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; Transplant Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Naghmeh Sattarahmady
- Department of Medical Physics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran; Nanomedicine and Nanobiology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
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Abstract
Diagnostic ultrasound (DUS) is, arguably, the most common technique used in obstetrical practice. From A mode, first described by Ian Donald for gynecology in the late 1950s, to B mode in the 1970s, real-time and gray-scale in the early 1980s, Doppler a little later, sophisticated color Doppler in the 1990s and three dimensional/four-dimensional ultrasound in the 2000s, DUS has not ceased to be closely associated with the practice of obstetrics. The latest innovation is the use of artificial intelligence which will, undoubtedly, take an increasing role in all aspects of our lives, including medicine and, specifically, obstetric ultrasound. In addition, in the future, new visualization methods may be developed, training methods expanded, and workflow and ergonomics improved.
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Affiliation(s)
- Jacques S Abramowicz
- University of Chicago, Chicago, IL, USA.,World Federation for Ultrasound in Medicine and Biology, London, UK
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9
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Chaki J, Dey N. Data Tagging in Medical Images: A Survey of the State-of-Art. Curr Med Imaging 2020; 16:1214-1228. [PMID: 32108002 DOI: 10.2174/1573405616666200218130043] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 12/02/2019] [Accepted: 12/16/2019] [Indexed: 11/22/2022]
Abstract
A huge amount of medical data is generated every second, and a significant percentage of the data are images that need to be analyzed and processed. One of the key challenges in this regard is the recovery of the data of medical images. The medical image recovery procedure should be done automatically by the computers that are the method of identifying object concepts and assigning homologous tags to them. To discover the hidden concepts in the medical images, the lowlevel characteristics should be used to achieve high-level concepts and that is a challenging task. In any specific case, it requires human involvement to determine the significance of the image. To allow machine-based reasoning on the medical evidence collected, the data must be accompanied by additional interpretive semantics; a change from a pure data-intensive methodology to a model of evidence rich in semantics. In this state-of-art, data tagging methods related to medical images are surveyed which is an important aspect for the recognition of a huge number of medical images. Different types of tags related to the medical image, prerequisites of medical data tagging, different techniques to develop medical image tags, different medical image tagging algorithms and different tools that are used to create the tags are discussed in this paper. The aim of this state-of-art paper is to produce a summary and a set of guidelines for using the tags for the identification of medical images and to identify the challenges and future research directions of tagging medical images.
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Affiliation(s)
- Jyotismita Chaki
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Nilanjan Dey
- Department of Information Technology, Techno India College of Technology, West Bengal, India
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10
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Elsayed EK, ElDahshan KA, El-Sharawy EE, Ghannam NE. Reverse engineering approach for improving the quality of mobile applications. PeerJ Comput Sci 2019; 5:e212. [PMID: 33816865 PMCID: PMC7924421 DOI: 10.7717/peerj-cs.212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 07/10/2019] [Indexed: 06/12/2023]
Abstract
BACKGROUND Portable-devices applications (Android applications) are becoming complex software systems that must be developed quickly and continuously evolved to fit new user requirements and execution contexts. Applications must be produced rapidly and advance persistently in order to fit new client requirements and execution settings. However, catering to these imperatives may bring about poor outline decisions on design choices, known as anti-patterns, which may possibly corrupt programming quality and execution. Thus, the automatic detection of anti-patterns is a vital process that facilitates both maintenance and evolution tasks. Additionally, it guides developers to refactor their applications and consequently enhance their quality. METHODS We proposed a general method to detect mobile applications' anti-patterns that can detect both semantic and structural design anti-patterns. The proposed method is via reverse-engineering and ontology by using a UML modeling environment, an OWL ontology-based platform and ontology-driven conceptual modeling. We present and test a new method that generates the OWL ontology of mobile applications and analyzes the relationships among object-oriented anti-patterns and offer methods to resolve the anti-patterns by detecting and treating 15 different design's semantic and structural anti-patterns that occurred in analyzing of 29 mobile applications. We choose 29 mobile applications randomly. Selecting a browser is not a criterion in this method because the proposed method is applied on a design level. We demonstrate a semantic integration method to reduce the incidence of anti-patterns using the ontology merging on mobile applications. RESULTS The proposed method detected 15 semantic and structural design anti-patterns which have appeared 1,262 times in a random sample of 29 mobile applications. The proposed method introduced a new classification of the anti-patterns divided into four groups. "The anti-patterns in the class group" is the most group that has the maximum occurrences of anti-patterns and "The anti-patterns in the operation group" is the smallest one that has the minimum occurrences of the anti-patterns which are detected by the proposed method. The results also showed the correlation between the selected tools which we used as Modelio, the Protégé platform, and the OLED editor of the OntoUML. The results showed that there was a high positive relation between Modelio and Protégé which implies that the combination between both increases the accuracy level of the detection of anti-patterns. In the evaluation and analyzing the suitable integration method, we applied the different methods on homogeneous mobile applications and found that using ontology increased the detection percentage approximately by 11.3% in addition to guaranteed consistency.
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Affiliation(s)
- Eman K. Elsayed
- Department of Mathematical and Computer Science, Faculty of Science, Al-Azhar University, (Girls Branch), Cairo, Egypt
| | - Kamal A. ElDahshan
- Department of Mathematical and Computer Science, Faculty of Science, Al-Azhar University, Cairo, Egypt
| | - Enas E. El-Sharawy
- Department of Mathematical and Computer Science, Faculty of Science, Al-Azhar University, (Girls Branch), Cairo, Egypt
- Computer Department, College of Science and Humanities in Jubail, Imam Abdulrahman Bin Faisal University, Kingdom of Saudi Arabia
| | - Naglaa E. Ghannam
- Department of Mathematical and Computer Science, Faculty of Science, Al-Azhar University, (Girls Branch), Cairo, Egypt
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11
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Dhombres F, Maurice P, Guilbaud L, Franchinard L, Dias B, Charlet J, Blondiaux E, Khoshnood B, Jurkovic D, Jauniaux E, Jouannic JM. A Novel Intelligent Scan Assistant System for Early Pregnancy Diagnosis by Ultrasound: Clinical Decision Support System Evaluation Study. J Med Internet Res 2019; 21:e14286. [PMID: 31271152 PMCID: PMC6636237 DOI: 10.2196/14286] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 06/11/2019] [Accepted: 06/11/2019] [Indexed: 01/26/2023] Open
Abstract
Background Early pregnancy ultrasound scans are usually performed by nonexpert examiners in obstetrics/gynecology (OB/GYN) emergency departments. Establishing the precise diagnosis of pregnancy location is key for appropriate management of early pregnancies, and experts are usually able to locate a pregnancy in the first scan. A decision-support system based on a semantic, expert-validated knowledge base may improve the diagnostic performance of nonexpert examiners for early pregnancy transvaginal ultrasound. Objective This study aims to evaluate a novel Intelligent Scan Assistant System for early pregnancy ultrasound to diagnose the pregnancy location and determine the image quality. Methods Two trainees performed virtual transvaginal ultrasound examinations of early pregnancy cases with and without the system. The ultrasound images and reports were blindly reviewed by two experts using scoring methods. A diagnosis of pregnancy location and ultrasound image quality were compared between scans performed with and without the system. Results Each trainee performed a virtual vaginal examination for all 32 cases with and without use of the system. The analysis of the 128 resulting scans showed higher quality of the images (quality score: +23%; P<.001), less images per scan (4.6 vs 6.3 [without the CDSS]; P<.001), and higher confidence in reporting conclusions (trust score: +20%; P<.001) with use of the system. Further, use of the system cost an additional 8 minutes per scan. We observed a correct diagnosis of pregnancy location in 39 (61%) and 52 (81%) of 64 scans in the nonassisted mode and assisted mode, respectively. Additionally, an exact diagnosis (with precise ectopic location) was made in 30 (47%) and 49 (73%) of the 64 scans without and with use of the system, respectively. These differences in diagnostic performance (+20% for correct location diagnosis and +30% for exact diagnosis) were both statistically significant (P=.002 and P<.001, respectively). Conclusions The Intelligent Scan Assistant System is based on an expert-validated knowledge base and demonstrates significant improvement in early pregnancy scanning, both in diagnostic performance (pregnancy location and precise diagnosis) and scan quality (selection of images, confidence, and image quality).
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Affiliation(s)
- Ferdinand Dhombres
- Service de Médecine Fœtale, Sorbonne Université, Assistance Publique - Hôpitaux de Paris / Hôpitaux Universitaires Est Parisiens, Hôpital Armand Trousseau, Paris, France.,Medical Informatics and Knowledge Engineering for eHealth Lab, INSERM, Paris, France
| | - Paul Maurice
- Service de Médecine Fœtale, Sorbonne Université, Assistance Publique - Hôpitaux de Paris / Hôpitaux Universitaires Est Parisiens, Hôpital Armand Trousseau, Paris, France.,Medical Informatics and Knowledge Engineering for eHealth Lab, INSERM, Paris, France
| | - Lucie Guilbaud
- Service de Médecine Fœtale, Sorbonne Université, Assistance Publique - Hôpitaux de Paris / Hôpitaux Universitaires Est Parisiens, Hôpital Armand Trousseau, Paris, France
| | - Loriane Franchinard
- Service de Médecine Fœtale, Sorbonne Université, Assistance Publique - Hôpitaux de Paris / Hôpitaux Universitaires Est Parisiens, Hôpital Armand Trousseau, Paris, France
| | - Barbara Dias
- Service de Médecine Fœtale, Sorbonne Université, Assistance Publique - Hôpitaux de Paris / Hôpitaux Universitaires Est Parisiens, Hôpital Armand Trousseau, Paris, France
| | - Jean Charlet
- Medical Informatics and Knowledge Engineering for eHealth Lab, INSERM, Paris, France.,Direction de la Recherche et de l'Innovation, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Eléonore Blondiaux
- Service de Radiologie, Sorbonne Université, Assistance Publique - Hôpitaux de Paris / Hôpitaux Universitaires Est Parisiens, Hôpital Armand Trousseau, Paris, France
| | - Babak Khoshnood
- Obstetrical, Perinatal and Pediatric Epidemiology Research Team, Center for Biostatistics and Epidemiology, INSERM, Paris, France
| | - Davor Jurkovic
- Gynaecology Diagnostic and Outpatient Treatment Unit, University College Hospital and Institute for Women's Health, University College London, London, United Kingdom
| | - Eric Jauniaux
- Gynaecology Diagnostic and Outpatient Treatment Unit, University College Hospital and Institute for Women's Health, University College London, London, United Kingdom
| | - Jean-Marie Jouannic
- Service de Médecine Fœtale, Sorbonne Université, Assistance Publique - Hôpitaux de Paris / Hôpitaux Universitaires Est Parisiens, Hôpital Armand Trousseau, Paris, France.,Medical Informatics and Knowledge Engineering for eHealth Lab, INSERM, Paris, France
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Dórea FC, Vial F, Hammar K, Lindberg A, Lambrix P, Blomqvist E, Revie CW. Drivers for the development of an Animal Health Surveillance Ontology (AHSO). Prev Vet Med 2019; 166:39-48. [PMID: 30935504 DOI: 10.1016/j.prevetmed.2019.03.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Revised: 01/07/2019] [Accepted: 03/05/2019] [Indexed: 02/01/2023]
Abstract
Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major component of any solution will involve the adoption of ontologies. Here we describe the advantages of such an approach, and the steps taken to set up the Animal Health Surveillance Ontological (AHSO) framework. The AHSO framework is modelled in OWL, the W3C standard Semantic Web language for representing rich and complex knowledge. We illustrate how the framework can incorporate knowledge directly from domain experts or from data-driven sources, as well as by integrating existing mature ontological components from related disciplines. The development and extent of AHSO will be community driven and the final products in the framework will be open-access.
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Affiliation(s)
- Fernanda C Dórea
- Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden.
| | | | - Karl Hammar
- Department of Computer Science and Informatics, Jönköping University, Sweden; Department of Computer and Information Science, Linköping University, Sweden
| | - Ann Lindberg
- Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden
| | - Patrick Lambrix
- Department of Computer and Information Science, Linköping University, Sweden; Swedish e-Science Centre, Linköping University, Sweden
| | - Eva Blomqvist
- Department of Computer and Information Science, Linköping University, Sweden
| | - Crawford W Revie
- Atlantic Veterinary College, University of Prince Edward Island, Canada
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Abstract
For centuries, humans have sought to classify diseases based on phenotypic presentation and available treatments. Today, a wide landscape of strategies, resources, and tools exist to classify patients and diseases. Ontologies can provide a robust foundation of logic for precise stratification and classification along diverse axes such as etiology, development, treatment, and genetics. Disease and phenotype ontologies are used in four primary ways: ( a) search, retrieval, and annotation of knowledge; ( b) data integration and analysis; ( c) clinical decision support; and ( d) knowledge discovery. Computational inference can connect existing knowledge and generate new insights and hypotheses about drug targets, prognosis prediction, or diagnosis. In this review, we examine the rise of disease and phenotype ontologies and the diverse ways they are represented and applied in biomedicine.
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Affiliation(s)
- Melissa A. Haendel
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon 97239, USA
- Linus Pauling Institute, Oregon State University, Corvallis, Oregon 97331, USA
| | - Julie A. McMurry
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon 97239, USA
| | - Rose Relevo
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon 97239, USA
| | - Christopher J. Mungall
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | | | - Christopher G. Chute
- School of Medicine, School of Public Health, and School of Nursing, Johns Hopkins University, Baltimore, Maryland 21205, USA
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