Pub Date : 2020-07-01DOI: 10.1109/CBMS49503.2020.00011
Edson A. G. Coutinho, B. Carvalho
Remote visualization of medical data is a very attractive alternative to increased mobility, allowing volumetric data to be accessed even in devices with low processing capability. However, the amount of simultaneous accesses and the bandwidth available are natural bottlenecks for any solution in this field. This paper presents a methodology to evaluate 3D volumetric rendering client-servers systems with the goal of determining the maximum load of a specific system based on Quality of Service (QoS). With such input in mind, a system architect could project systems with better cost-benefit ratio, or even design a cloud system that predicts and rents servers based on the number of service requests. In order to check the viability of the methodology, a stress test was conducted in a client-server system developed to visualize Computed Tomography (CT) scans. Results have shown that it could handle at least 20 simultaneous remote visualizations, even in scenarios with low bandwidth, finding its upper limit when dealing with around 30 simultaneous visualizations.
{"title":"Evaluation of Real-Time Remote 3D Rendering of Medical Images using GPUs","authors":"Edson A. G. Coutinho, B. Carvalho","doi":"10.1109/CBMS49503.2020.00011","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00011","url":null,"abstract":"Remote visualization of medical data is a very attractive alternative to increased mobility, allowing volumetric data to be accessed even in devices with low processing capability. However, the amount of simultaneous accesses and the bandwidth available are natural bottlenecks for any solution in this field. This paper presents a methodology to evaluate 3D volumetric rendering client-servers systems with the goal of determining the maximum load of a specific system based on Quality of Service (QoS). With such input in mind, a system architect could project systems with better cost-benefit ratio, or even design a cloud system that predicts and rents servers based on the number of service requests. In order to check the viability of the methodology, a stress test was conducted in a client-server system developed to visualize Computed Tomography (CT) scans. Results have shown that it could handle at least 20 simultaneous remote visualizations, even in scenarios with low bandwidth, finding its upper limit when dealing with around 30 simultaneous visualizations.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"102 1","pages":"19-24"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79143955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1109/CBMS49503.2020.00033
L. W. Oliveira, S. T. Carvalho
This work investigates how gamification is used in self-care applications. Evidence in the literature indicates that the development of gamified mobile health applications has not taken into account the user's profile in order to correctly use the game elements in the solution; there also cases in which the use of gamification goes beyond the main purpose of the application, which is to treat health. This results in inefficiency in the use of the gamification strategy. To overcome this problem, this paper presents a gamification-based Framework, called Framework L, a method which incorporates concepts and practices in terms of two dimensions, Self-Care and Gamification, so that an mobile health application developer can design his application. In this context, adaptive gamification experiments were carried out in different ways. The first aims to improve the user experience when performing a manual test for the player profile. The second experiment uses machine learning to classify the user by player profile. These aspects make up the adaptive gamification cycle. The framework evaluation used the mixed method composed of a questionnaire and an online interview with experts. The results indicate that the framework helps developers marshal mobile health applications, primarily by encouraging user engagement.
{"title":"A Gamification-Based Framework for mHealth Developers in the Context of Self-Care","authors":"L. W. Oliveira, S. T. Carvalho","doi":"10.1109/CBMS49503.2020.00033","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00033","url":null,"abstract":"This work investigates how gamification is used in self-care applications. Evidence in the literature indicates that the development of gamified mobile health applications has not taken into account the user's profile in order to correctly use the game elements in the solution; there also cases in which the use of gamification goes beyond the main purpose of the application, which is to treat health. This results in inefficiency in the use of the gamification strategy. To overcome this problem, this paper presents a gamification-based Framework, called Framework L, a method which incorporates concepts and practices in terms of two dimensions, Self-Care and Gamification, so that an mobile health application developer can design his application. In this context, adaptive gamification experiments were carried out in different ways. The first aims to improve the user experience when performing a manual test for the player profile. The second experiment uses machine learning to classify the user by player profile. These aspects make up the adaptive gamification cycle. The framework evaluation used the mixed method composed of a questionnaire and an online interview with experts. The results indicate that the framework helps developers marshal mobile health applications, primarily by encouraging user engagement.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"1 1","pages":"138-141"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73514452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Despite the huge findings made by the study of the behaviour of diseases, there are currently many non-cure or non-treatment diseases and only some of their symptoms can be beaten. Understanding how the diseases behave implies a complex analysis that together with the new technologies provide researchers with more calculation and observational capabilities, as well as novel approaches that allow us to observe how the diseases behave and relate in different environments with distinct factors. Current research aims to find new ways of characterizing the diseases based on phenotypic manifestations using knowledge extraction techniques from public sources. With the characterization of the diseases, a better understanding about the diseases and how similar they are can be achieved, leading for example to find new drugs that can be applied to different diseases. In order to carry out the present research we have made use of our own dataset of symptoms and diseases developed using an approach that allows us to generate phenotypic knowledge from the extraction of medical information from several data sources.
{"title":"Characterization of Diseases Based on Phenotypic Information Through Knowledge Extraction using Public Sources","authors":"Gerardo Lagunes García, A. R. González","doi":"10.1109/CBMS.2019.00124","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00124","url":null,"abstract":"Despite the huge findings made by the study of the behaviour of diseases, there are currently many non-cure or non-treatment diseases and only some of their symptoms can be beaten. Understanding how the diseases behave implies a complex analysis that together with the new technologies provide researchers with more calculation and observational capabilities, as well as novel approaches that allow us to observe how the diseases behave and relate in different environments with distinct factors. Current research aims to find new ways of characterizing the diseases based on phenotypic manifestations using knowledge extraction techniques from public sources. With the characterization of the diseases, a better understanding about the diseases and how similar they are can be achieved, leading for example to find new drugs that can be applied to different diseases. In order to carry out the present research we have made use of our own dataset of symptoms and diseases developed using an approach that allows us to generate phenotypic knowledge from the extraction of medical information from several data sources.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"16 1","pages":"596-599"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84403314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gilvan Veras Magalhães Júnior, João Paulo Albuquerque Vieira, Roney L. S. Santos, J. L. N. Barbosa, P. S. Neto, R. Moura
In Brazil, a current health problem is the low capacity of meeting an increasing demand for medical services. As a result, some people have resorted to supplementary health care, which involves the operation of private health plans and health insurance. However, many health maintenance organizations (HMO) face financial difficulties due to unnecessary procedures, fraud or abuses in the use of health services. In order to avoid unnecessary expenses, the HMO began to use a mechanism called prior authorization, where a prior analysis of each user's need is made to authorize or deny the required requests. This work aims to study the influence of the use of textual features in automatic prior authorization evaluation, by using Text Mining, Natural Language Processing and Machine Learning techniques. Experiments were performed using several machine learning algorithms combined with textual features, increasing the performance of the automatic prior authorization. Results indicate not only the textual features influence to the evaluation of the automatic prior authorization process but also improved the prediction of the classifiers.
{"title":"A Study of the Influence of Textual Features in Learning Medical Prior Authorization","authors":"Gilvan Veras Magalhães Júnior, João Paulo Albuquerque Vieira, Roney L. S. Santos, J. L. N. Barbosa, P. S. Neto, R. Moura","doi":"10.1109/CBMS.2019.00021","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00021","url":null,"abstract":"In Brazil, a current health problem is the low capacity of meeting an increasing demand for medical services. As a result, some people have resorted to supplementary health care, which involves the operation of private health plans and health insurance. However, many health maintenance organizations (HMO) face financial difficulties due to unnecessary procedures, fraud or abuses in the use of health services. In order to avoid unnecessary expenses, the HMO began to use a mechanism called prior authorization, where a prior analysis of each user's need is made to authorize or deny the required requests. This work aims to study the influence of the use of textual features in automatic prior authorization evaluation, by using Text Mining, Natural Language Processing and Machine Learning techniques. Experiments were performed using several machine learning algorithms combined with textual features, increasing the performance of the automatic prior authorization. Results indicate not only the textual features influence to the evaluation of the automatic prior authorization process but also improved the prediction of the classifiers.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"451 2","pages":"56-61"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CBMS.2019.00021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72457359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jefferson Henrique Camelo Soares, J. L. N. Barbosa, L. A. Lopes, Gilvan Veras Magalhães Júnior, R. Rabêlo, E. Passos, P. S. Neto
In a Health Plan, beneficiaries can cancel their contracts at any given time. For that reason, Health Insurance/Plan Providers (HIP) need to avoid optional contract cancellations to keep their financial operations stable. This work's main purpose is to develop an approach to predict the optional contract cancellation in a Private HIP and help them to prevent those cancelations.
{"title":"How to Avoid Customer Churn in Health Insurance/Plans? A Machine Learn Approach","authors":"Jefferson Henrique Camelo Soares, J. L. N. Barbosa, L. A. Lopes, Gilvan Veras Magalhães Júnior, R. Rabêlo, E. Passos, P. S. Neto","doi":"10.1109/CBMS.2019.00115","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00115","url":null,"abstract":"In a Health Plan, beneficiaries can cancel their contracts at any given time. For that reason, Health Insurance/Plan Providers (HIP) need to avoid optional contract cancellations to keep their financial operations stable. This work's main purpose is to develop an approach to predict the optional contract cancellation in a Private HIP and help them to prevent those cancelations.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"3 1","pages":"559-562"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83793528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. M. Soriano, J. Castro, J. Fernández-breis, I. S. Román, A. A. Barriuso, David Guevara Baraza
Hospital Information Systems (H.I.S) use Electronic Health Record to store heterogeneous data from the patients. One important goal in this kind of systems is that the information must be, normalized and codify with a clinical terminology to represent exactly the healthcare meaning. Usually this process need human experts to identify and map the correct concept, this is a slow and tedious task. One of the most widespread clinical terminologies with more projection is Snomed-CT. This is an ontology multilingual clinical terminology that represent the clinical concepts with a unique code. We introduce in this paper Snomed2Vec, new approach of semantic search tool to find the most similar concepts using Snomed-CT. This is an ontology based named entity recognition system using word embedding, that suggest what is the most similar concept, that appear in a text. To evaluate the tool we suggest two kind of validations, one against a corpus gold with diagnostic from clinical reports, and a social validation, with a public free web access. We publish an access web to the academic world to use, test and validate the tool. The results of validation shows that this process help to the specialist to the election of choose the correct concepts from Snomed-CT. The paper illustrates 1) how create the initial big corpus of texts, to train the word2vec models, 2) how we use this vector space model to create our final Snomed2Vec vector space model, 3) The use of the cosine similarity distance, to obtain the most similar concepts, grouping by the hierarchies from Snomed-CT. We publish to the academic world: https://github.com/NachusS/Snomed2Vec access to the public web tool, and the notebook, for develop and test this paper.
{"title":"Snomed2Vec: Representation of SNOMED CT Terms with Word2Vec","authors":"I. M. Soriano, J. Castro, J. Fernández-breis, I. S. Román, A. A. Barriuso, David Guevara Baraza","doi":"10.1109/CBMS.2019.00138","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00138","url":null,"abstract":"Hospital Information Systems (H.I.S) use Electronic Health Record to store heterogeneous data from the patients. One important goal in this kind of systems is that the information must be, normalized and codify with a clinical terminology to represent exactly the healthcare meaning. Usually this process need human experts to identify and map the correct concept, this is a slow and tedious task. One of the most widespread clinical terminologies with more projection is Snomed-CT. This is an ontology multilingual clinical terminology that represent the clinical concepts with a unique code. We introduce in this paper Snomed2Vec, new approach of semantic search tool to find the most similar concepts using Snomed-CT. This is an ontology based named entity recognition system using word embedding, that suggest what is the most similar concept, that appear in a text. To evaluate the tool we suggest two kind of validations, one against a corpus gold with diagnostic from clinical reports, and a social validation, with a public free web access. We publish an access web to the academic world to use, test and validate the tool. The results of validation shows that this process help to the specialist to the election of choose the correct concepts from Snomed-CT. The paper illustrates 1) how create the initial big corpus of texts, to train the word2vec models, 2) how we use this vector space model to create our final Snomed2Vec vector space model, 3) The use of the cosine similarity distance, to obtain the most similar concepts, grouping by the hierarchies from Snomed-CT. We publish to the academic world: https://github.com/NachusS/Snomed2Vec access to the public web tool, and the notebook, for develop and test this paper.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"27 1","pages":"678-683"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75239892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An integrated platform can be used to support the analysis of gene expression data. In principle, this platform should provide support for accessibility, reproducibility and transparency in order to facilitate its use. However, this support varies among different platforms. SemanticSCo consists of an integrated platform that allows the development of a gene expression analysis workflow based on the semiautomatic composition of semantic web services. SemanticSCo provides limited support for accessibility. Further, the platform lacks support for reproducibility and transparency. This paper describes how accessibility was improved and how reproducibility and transparency were introduced into a new version of the platform, called SemanticSCo Web. These improvements facilitate the development, execution and sharing of gene expression analysis workflows.
{"title":"Support for Accessibility, Reproducibility and Transparency in a Service-Oriented Gene Expression Analysis Platform","authors":"Wilson Daniel da Silva, C. D. Farias","doi":"10.1109/CBMS.2019.00098","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00098","url":null,"abstract":"An integrated platform can be used to support the analysis of gene expression data. In principle, this platform should provide support for accessibility, reproducibility and transparency in order to facilitate its use. However, this support varies among different platforms. SemanticSCo consists of an integrated platform that allows the development of a gene expression analysis workflow based on the semiautomatic composition of semantic web services. SemanticSCo provides limited support for accessibility. Further, the platform lacks support for reproducibility and transparency. This paper describes how accessibility was improved and how reproducibility and transparency were introduced into a new version of the platform, called SemanticSCo Web. These improvements facilitate the development, execution and sharing of gene expression analysis workflows.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"254 1","pages":"477-482"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74935929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Pognonec, M. Barlaud, B. Wattellier, T. Pourcher, Yuxiang Zhou, Sherazade Aknoun, M. Yonnet, M. Antonini
{"title":"Non Invasive Live Cell Cycle Monitoring using Quantitative Phase Imaging and Proximal Machine Learning Methods","authors":"P. Pognonec, M. Barlaud, B. Wattellier, T. Pourcher, Yuxiang Zhou, Sherazade Aknoun, M. Yonnet, M. Antonini","doi":"10.1109/CBMS.2019.00099","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00099","url":null,"abstract":"","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"4 1","pages":"483-488"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76032297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper we propose a recognition system of medical concepts from free text clinical reports. Our approach tries to recognize also concepts which are named with local terminology, with medical writing scripts, short words, abbreviations and even spelling mistakes. We consider a clinical terminology ontology (Snomed-CT), as a dictionary of concepts. In a first step we obtain an embedding model using word2vec methodology from a big corpus database of clinical reports. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space, and so the geometrical similarity can be considered a measure of semantic relation. We have considered 615513 emergency clinical reports from the Hospital "Rafael Mendez" in Lorca, Murcia. In these reports there are a lot of local language of the emergency domain, medical writing scripts, short words, abbreviations and even spelling mistakes. With the model obtained we represent the words and sentences as vectors, and by applying cosine similarity we identify which concepts of the ontology are named in the text. Finally, we represent the clinical reports (EHR) like a bag of concepts, and use this representation to search similar documents. The paper illustrates 1) how we build the word2vec model from the free text clinical reports, 2) How we extend the embedding from words to sentences, and 3) how we use the cosine similarity to identify concepts. The experimentation, and expert human validation, shows that: a) the concepts named in the text with the ontology terminology are well recognized, and b) others concepts that are not named with the ontology terminology are also recognized, obtaining a high precision and recall measures.
{"title":"STMC: Semantic Tag Medical Concept Using Word2Vec Representation","authors":"I. M. Soriano, J. Castro","doi":"10.1109/CBMS.2018.00075","DOIUrl":"https://doi.org/10.1109/CBMS.2018.00075","url":null,"abstract":"In this paper we propose a recognition system of medical concepts from free text clinical reports. Our approach tries to recognize also concepts which are named with local terminology, with medical writing scripts, short words, abbreviations and even spelling mistakes. We consider a clinical terminology ontology (Snomed-CT), as a dictionary of concepts. In a first step we obtain an embedding model using word2vec methodology from a big corpus database of clinical reports. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space, and so the geometrical similarity can be considered a measure of semantic relation. We have considered 615513 emergency clinical reports from the Hospital \"Rafael Mendez\" in Lorca, Murcia. In these reports there are a lot of local language of the emergency domain, medical writing scripts, short words, abbreviations and even spelling mistakes. With the model obtained we represent the words and sentences as vectors, and by applying cosine similarity we identify which concepts of the ontology are named in the text. Finally, we represent the clinical reports (EHR) like a bag of concepts, and use this representation to search similar documents. The paper illustrates 1) how we build the word2vec model from the free text clinical reports, 2) How we extend the embedding from words to sentences, and 3) how we use the cosine similarity to identify concepts. The experimentation, and expert human validation, shows that: a) the concepts named in the text with the ontology terminology are well recognized, and b) others concepts that are not named with the ontology terminology are also recognized, obtaining a high precision and recall measures.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"32 1","pages":"393-398"},"PeriodicalIF":0.0,"publicationDate":"2018-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87924068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. F. Carvalho, José Antonio Camacho Guerrero, A. Uscamayta, H. Vale, H. Oliveira
Cancer is one type of lethal disease that needs an immediate diagnosis and treatment, demanding the use of advanced technologies. External Radiotherapy is the most common treatment used in the world. Radiotherapy Planning Systems (RTPS) is a software suite used for delineation of organs and calculation of irradiation doses and is essential to ensure the accuracy of oncologic treatment in clinics and hospitals. This works presents a type of RTPS, named SIPRAD, developed for public hospitals reality in Brazil with the assistance of the doctors. SIPRAD architecture uses a number of management and operational tools for all radiotherapy treatment process. One of the main contributions of this work is its interoperable infrastructure capable of communicating with all health systems and devices. Another contribution is a drawing tool, which is able to perform an automatic contour delineation in organs at risk and tumors volumes. Additionally also extends the use of tomography slices in grayscale images applied in three plans: axial, coronal and sagittal. SIPRAD automatic contour delineation saves up 75% of the time for drawings in three different types of regions body exams. Finally, SIPRAD was tested in a set of oncologic exams provided by the University of Sao Paulo Clinical Hospital (HCRP and HC-USP) in Brazil obtained from patients with different conditions of ethnology, ages, tumor severities and body regions.
{"title":"SIPRAD: A Radiotherapy Planning System (RTPS)","authors":"D. F. Carvalho, José Antonio Camacho Guerrero, A. Uscamayta, H. Vale, H. Oliveira","doi":"10.1109/CBMS.2018.00081","DOIUrl":"https://doi.org/10.1109/CBMS.2018.00081","url":null,"abstract":"Cancer is one type of lethal disease that needs an immediate diagnosis and treatment, demanding the use of advanced technologies. External Radiotherapy is the most common treatment used in the world. Radiotherapy Planning Systems (RTPS) is a software suite used for delineation of organs and calculation of irradiation doses and is essential to ensure the accuracy of oncologic treatment in clinics and hospitals. This works presents a type of RTPS, named SIPRAD, developed for public hospitals reality in Brazil with the assistance of the doctors. SIPRAD architecture uses a number of management and operational tools for all radiotherapy treatment process. One of the main contributions of this work is its interoperable infrastructure capable of communicating with all health systems and devices. Another contribution is a drawing tool, which is able to perform an automatic contour delineation in organs at risk and tumors volumes. Additionally also extends the use of tomography slices in grayscale images applied in three plans: axial, coronal and sagittal. SIPRAD automatic contour delineation saves up 75% of the time for drawings in three different types of regions body exams. Finally, SIPRAD was tested in a set of oncologic exams provided by the University of Sao Paulo Clinical Hospital (HCRP and HC-USP) in Brazil obtained from patients with different conditions of ethnology, ages, tumor severities and body regions.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"119 1","pages":"428-433"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77454337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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