Eduardo Beckhauser, V. A. Petrolini, Alexandre Savaris, A. V. Wangenheim, D. Krechel
A structured reporting system is a set of standard-compliant practices that guides the physicians in order to create structured documents, organizing and simplifying their workload. In spite of this advantages, many physicians still use conventional methods to write their findings, like free-text reports. One main reason for this is the little convention on how to disseminate a structured reporting routine in the report environment. This work proposes a systematic approach to migrate a system routine from free-text reports to structured reports, focusing on the DICOM Structured Reporting guidelines. To evaluate this proposal, a structured reporting system was created in the context of the Santa Catarina State Integrated Telemedicine and Telehealth System (STT/SC), in Brazil, and, in a case study covering obstetric ultrasonography reports, was evaluated by a group of experts using the AdEQUATE model, showing a high user perception from the system. The results are a set of defined premises and steps that turns a telemedicine system into a complete structured reporting environment.
{"title":"Systematic Approach and Tools for Migrating from Text-Based Reports to Structured Reports: Based on the DICOM Structured Reporting Guidelines","authors":"Eduardo Beckhauser, V. A. Petrolini, Alexandre Savaris, A. V. Wangenheim, D. Krechel","doi":"10.1109/CBMS.2019.00054","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00054","url":null,"abstract":"A structured reporting system is a set of standard-compliant practices that guides the physicians in order to create structured documents, organizing and simplifying their workload. In spite of this advantages, many physicians still use conventional methods to write their findings, like free-text reports. One main reason for this is the little convention on how to disseminate a structured reporting routine in the report environment. This work proposes a systematic approach to migrate a system routine from free-text reports to structured reports, focusing on the DICOM Structured Reporting guidelines. To evaluate this proposal, a structured reporting system was created in the context of the Santa Catarina State Integrated Telemedicine and Telehealth System (STT/SC), in Brazil, and, in a case study covering obstetric ultrasonography reports, was evaluated by a group of experts using the AdEQUATE model, showing a high user perception from the system. The results are a set of defined premises and steps that turns a telemedicine system into a complete structured reporting environment.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127406701","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. Wong, S. Relton, H. Fang, Rami Qhawaji, Christopher D Graham, Jane Elizabeth Alty, S. Williams
Slowness of movement, known as bradykinesia, is an important early symptom of Parkinson's disease. This symptom is currently assessed subjectively by clinical experts. However, expert assessment has been shown to be subject to inter-rater variability. We propose a low-cost, contactless system using smarthphone videos to automatically determine the presence of bradykinesia. Using 70 videos recorded in a pilot study, we predicted the presence of bradykinesia with an estimated test accuracy of 0.79 and the presence of Parkinson's disease with estimated test accuracy 0.63. Even on a small set of pilot data this accuracy is comparable to that recorded by blinded human experts.
{"title":"Supervised Classification of Bradykinesia for Parkinson's Disease Diagnosis from Smartphone Videos","authors":"D. Wong, S. Relton, H. Fang, Rami Qhawaji, Christopher D Graham, Jane Elizabeth Alty, S. Williams","doi":"10.1109/CBMS.2019.00017","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00017","url":null,"abstract":"Slowness of movement, known as bradykinesia, is an important early symptom of Parkinson's disease. This symptom is currently assessed subjectively by clinical experts. However, expert assessment has been shown to be subject to inter-rater variability. We propose a low-cost, contactless system using smarthphone videos to automatically determine the presence of bradykinesia. Using 70 videos recorded in a pilot study, we predicted the presence of bradykinesia with an estimated test accuracy of 0.79 and the presence of Parkinson's disease with estimated test accuracy 0.63. Even on a small set of pilot data this accuracy is comparable to that recorded by blinded human experts.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114965646","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}
This document introduces the DeepHealth project: "Deep-Learning and HPC to Boost Biomedical Applications for Health". This project is funded by the European Commission under the H2020 framework program and aims to reduce the gap between the availability of mature enough AI-solutions and their deployment in real scenarios. Several existing software platforms provided by industrial partners will integrate state-of-the-art machine-learning algorithms and will be used for giving support to doctors in diagnosis, increasing their capabilities and efficiency. The DeepHealth consortium is composed by 21 partners from 9 European countries including hospitals, universities, large industry and SMEs.
{"title":"Deep-Learning and HPC to Boost Biomedical Applications for Health (DeepHealth)","authors":"Mónica Caballero, J. A. Gómez, Aimilia Bantouna","doi":"10.1109/CBMS.2019.00040","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00040","url":null,"abstract":"This document introduces the DeepHealth project: \"Deep-Learning and HPC to Boost Biomedical Applications for Health\". This project is funded by the European Commission under the H2020 framework program and aims to reduce the gap between the availability of mature enough AI-solutions and their deployment in real scenarios. Several existing software platforms provided by industrial partners will integrate state-of-the-art machine-learning algorithms and will be used for giving support to doctors in diagnosis, increasing their capabilities and efficiency. The DeepHealth consortium is composed by 21 partners from 9 European countries including hospitals, universities, large industry and SMEs.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127311514","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}
Digital pathology is a new branch of medical imaging referring to the aggregation of equipment and software to acquire, store and display microscopic images in a distributed network environment. This article proposes an architecture and describes the implementation of a collaborative pathology web platform. The solution brings the modern collaborative concept, common in social and business networks, into Digital Pathology workflows supported by a customised PACS-DICOM infrastructure. The system assures services like the creation of working sessions, users groups, access control to sessions, synchronisation of operations in a rich web interface, replaying of the actions performed in a session, among others. The solution data management is ensured by a PACS compliant with the DICOM standard, more concretely the recent Whole Slide Imaging format and the DICOM Web communication services.
{"title":"Collaborative Framework for a Whole-Slide Image Viewer","authors":"R. Lebre, Rui Jesus, Pedro Nunes, C. Costa","doi":"10.1109/CBMS.2019.00053","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00053","url":null,"abstract":"Digital pathology is a new branch of medical imaging referring to the aggregation of equipment and software to acquire, store and display microscopic images in a distributed network environment. This article proposes an architecture and describes the implementation of a collaborative pathology web platform. The solution brings the modern collaborative concept, common in social and business networks, into Digital Pathology workflows supported by a customised PACS-DICOM infrastructure. The system assures services like the creation of working sessions, users groups, access control to sessions, synchronisation of operations in a rich web interface, replaying of the actions performed in a session, among others. The solution data management is ensured by a PACS compliant with the DICOM standard, more concretely the recent Whole Slide Imaging format and the DICOM Web communication services.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125913209","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}
Maria Sanchez-Doria, Jose Emilio Labra Gayo, Daniel Fernández-Álvarez, Herminio García-González
AppWesomed is a website developed to test the suggestion of SNOMED CT terms in medical reports. When the user is writing the report, the web shows the terms that match the last written word. The use of SNOMED CT in medical applications is important because it can facilitate the integration of medical records. The tests were conducted with twenty-five people, divided into two groups, fifteen doctors and eleven non-doctors. The average age of the groups was 45 years and 32 years, respectively. A survey designed according to the Likert scale was used to test the website usability, the results of the survey were used to calculate the Cronbach's Alpha whose value was 0.894. According to the results, 44% of the participants considered the website usability as "Good", 36% as "Excellent" and 20% as "Regular". The app has been positively evaluated by the surveyed doctors. In response to the general experience, doctors highlight the great application utility, especially in their own research studies for the data analysis in medical reports.
{"title":"Testing the Usability of SNOMED CT Terms Suggestion in Medical Report","authors":"Maria Sanchez-Doria, Jose Emilio Labra Gayo, Daniel Fernández-Álvarez, Herminio García-González","doi":"10.1109/CBMS.2019.00137","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00137","url":null,"abstract":"AppWesomed is a website developed to test the suggestion of SNOMED CT terms in medical reports. When the user is writing the report, the web shows the terms that match the last written word. The use of SNOMED CT in medical applications is important because it can facilitate the integration of medical records. The tests were conducted with twenty-five people, divided into two groups, fifteen doctors and eleven non-doctors. The average age of the groups was 45 years and 32 years, respectively. A survey designed according to the Likert scale was used to test the website usability, the results of the survey were used to calculate the Cronbach's Alpha whose value was 0.894. According to the results, 44% of the participants considered the website usability as \"Good\", 36% as \"Excellent\" and 20% as \"Regular\". The app has been positively evaluated by the surveyed doctors. In response to the general experience, doctors highlight the great application utility, especially in their own research studies for the data analysis in medical reports.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126164412","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}
J. Balcázar, Marie Ely Piceno, Laura Rodríguez-Navas
The authors have recently proposed the usage of modular decompositions of Gaifman graphs as an exploratory data analysis tool. We describe how these techniques allow for a compact, hierarchical visualization of the patterns of cooccurrence between data items, in the context of medical data corresponding to simultaneous diagnostics of patients.
{"title":"Hierarchical Visualization of Co-Occurrence Patterns on Diagnostic Data","authors":"J. Balcázar, Marie Ely Piceno, Laura Rodríguez-Navas","doi":"10.1109/CBMS.2019.00043","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00043","url":null,"abstract":"The authors have recently proposed the usage of modular decompositions of Gaifman graphs as an exploratory data analysis tool. We describe how these techniques allow for a compact, hierarchical visualization of the patterns of cooccurrence between data items, in the context of medical data corresponding to simultaneous diagnostics of patients.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125567371","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}
R. Gómez, N. García, Gonzalo Collantes, F. Ponce, P. Redón
Heart rate variability (HRV) monitoring has shown to be promising to early diagnose neonatal sepsis and therefore the objective is to develop a minimally invasive and cost-effective tool, based on HRV monitoring and machine learning (ML) algorithms, to predict sepsis risk in neonates within the first 48 hours of life. Seventy-nine new-borns, with less than 48 hours of life and with a gestational age between 36 and 41 weeks, borned in the Consorci Hospital General Universitari of València were enrolled after the tutor's authorization. Fifteen of them were diagnosed with sepsis. Electrocardiogram signal was monitored and recorded for 90 minutes and HRV parameters were calculated. Clinical data was extracted from the electronic medical record and sepsis was confirmed by central laboratory analyses. Supervised ML algorithms were evaluated based on sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver operating characteristic curve (AUC). Significant differences were observed in the power spectrum density at very low and low frequency bands and in long-term non-linear components. The AUC revealed that Adaptive boosting was the ML model with greater sensitivity and specificity (AUC=0.94) followed by Bagged Trees (AUC=0.88) and Random Forest (AUC=0.84). In conclusion, HRV and Adaptive Boosting algorithm can be used to identify new-borns with higher risk of suffering neonatal sepsis during their first 48 hours.
{"title":"Development of a Non-Invasive Procedure to Early Detect Neonatal Sepsis using HRV Monitoring and Machine Learning Algorithms","authors":"R. Gómez, N. García, Gonzalo Collantes, F. Ponce, P. Redón","doi":"10.1109/CBMS.2019.00037","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00037","url":null,"abstract":"Heart rate variability (HRV) monitoring has shown to be promising to early diagnose neonatal sepsis and therefore the objective is to develop a minimally invasive and cost-effective tool, based on HRV monitoring and machine learning (ML) algorithms, to predict sepsis risk in neonates within the first 48 hours of life. Seventy-nine new-borns, with less than 48 hours of life and with a gestational age between 36 and 41 weeks, borned in the Consorci Hospital General Universitari of València were enrolled after the tutor's authorization. Fifteen of them were diagnosed with sepsis. Electrocardiogram signal was monitored and recorded for 90 minutes and HRV parameters were calculated. Clinical data was extracted from the electronic medical record and sepsis was confirmed by central laboratory analyses. Supervised ML algorithms were evaluated based on sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver operating characteristic curve (AUC). Significant differences were observed in the power spectrum density at very low and low frequency bands and in long-term non-linear components. The AUC revealed that Adaptive boosting was the ML model with greater sensitivity and specificity (AUC=0.94) followed by Bagged Trees (AUC=0.88) and Random Forest (AUC=0.84). In conclusion, HRV and Adaptive Boosting algorithm can be used to identify new-borns with higher risk of suffering neonatal sepsis during their first 48 hours.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117084662","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}
Automatic and accurate segmentation of anatomical structures in chest radiographs is fundamental and essential for computer-aided diagnosis system. We introduce Mask R-CNN for instance segmentation of lung fields, heart and clavicles. This method efficiently detects different structures and generates accurate segmentation mask for each instance. To the best of our knowledge, we are the first to implement instance segmentation of these three anatomical structures in chest radiographs. We have done extensive experiments on a common benchmark dataset. Results show that the best of our models achieves the state-of-the-art segmentation performance on image resolution of 512 × 512. The Dice and Ω similarity are 0.976 and 0.953 for lung fields, 0.949 and 0.904 for heart, 0.920 and 0.852 for clavicles. And the average contour distance outperforms human observer on both lungs and heart with image resolution of 256 × 256. In addition, it takes only 0.16 and 0.12 seconds per image for the above two resolutions during inference, which is comparable to or even better than current methods.
{"title":"Instance Segmentation of Anatomical Structures in Chest Radiographs","authors":"Jie Wang, Zhigang Li, R. Jiang, Zhen Xie","doi":"10.1109/CBMS.2019.00092","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00092","url":null,"abstract":"Automatic and accurate segmentation of anatomical structures in chest radiographs is fundamental and essential for computer-aided diagnosis system. We introduce Mask R-CNN for instance segmentation of lung fields, heart and clavicles. This method efficiently detects different structures and generates accurate segmentation mask for each instance. To the best of our knowledge, we are the first to implement instance segmentation of these three anatomical structures in chest radiographs. We have done extensive experiments on a common benchmark dataset. Results show that the best of our models achieves the state-of-the-art segmentation performance on image resolution of 512 × 512. The Dice and Ω similarity are 0.976 and 0.953 for lung fields, 0.949 and 0.904 for heart, 0.920 and 0.852 for clavicles. And the average contour distance outperforms human observer on both lungs and heart with image resolution of 256 × 256. In addition, it takes only 0.16 and 0.12 seconds per image for the above two resolutions during inference, which is comparable to or even better than current methods.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123928309","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}
{"title":"Special Tracks","authors":"","doi":"10.1109/cbms.2019.00008","DOIUrl":"https://doi.org/10.1109/cbms.2019.00008","url":null,"abstract":"","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123490486","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}
Hadrien Lorenzo, Misbah Razzaq, Jacob Odeberg, P. Morange, J. Saracco, D. Alexandre, R. Thiébaut
The identification of novel biological factors associated with thrombin generation, a key biomarker of the coagulation process, remains a relevant strategy to disentangle pathophysiological mechanisms underlying the risk of venous thrombosis (VT). As part of the MARseille THrombosis Association Study (MARTHA), we measured whole blood DNA methylation levels, plasma levels of 300 proteins, 3 thrombin generation biomarkers (endogeneous thrombin potential, peak and lagtime), clinical and genetic data in 700 patients with VT. The application of a novel high-dimensional multi-levels statistical methodology we recently developed, the data driven sparse Partial Least Square method (ddsPLS), on the MARTHA datasets enabled us 1/ to confirm the role of a known mutation of the variability of endogenous thrombin potential and peak, 2/ to identify a new signature of 7 proteins strongly associated with lagtime.
{"title":"High-Dimensional Multi-Block Analysis of Factors Associated with Thrombin Generation Potential","authors":"Hadrien Lorenzo, Misbah Razzaq, Jacob Odeberg, P. Morange, J. Saracco, D. Alexandre, R. Thiébaut","doi":"10.1109/CBMS.2019.00094","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00094","url":null,"abstract":"The identification of novel biological factors associated with thrombin generation, a key biomarker of the coagulation process, remains a relevant strategy to disentangle pathophysiological mechanisms underlying the risk of venous thrombosis (VT). As part of the MARseille THrombosis Association Study (MARTHA), we measured whole blood DNA methylation levels, plasma levels of 300 proteins, 3 thrombin generation biomarkers (endogeneous thrombin potential, peak and lagtime), clinical and genetic data in 700 patients with VT. The application of a novel high-dimensional multi-levels statistical methodology we recently developed, the data driven sparse Partial Least Square method (ddsPLS), on the MARTHA datasets enabled us 1/ to confirm the role of a known mutation of the variability of endogenous thrombin potential and peak, 2/ to identify a new signature of 7 proteins strongly associated with lagtime.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127940003","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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