Pub Date : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635576
D. Zaridis, E. Mylona, N. Tachos, K. Marias, M. Tsiknakis, D. Fotiadis
Automatic segmentation of the prostate peripheral zone on Magnetic Resonance Images (MRI) is a necessary but challenging step for accurate prostate cancer diagnosis. Deep learning (DL) based methods, such as U-Net, have recently been developed to segment the prostate and its' sub-regions. Nevertheless, the presence of class imbalance in the image labels, where the background pixels dominate over the region to be segmented, may severely hamper the segmentation performance. In the present work, we propose a DL-based preprocessing pipeline for segmenting the peripheral zone of the prostate by cropping unnecessary information without making a priori assumptions regarding the location of the region of interest. The effect of DL-cropping for improving the segmentation performance was compared to the standard center-cropping using three state-of-the-art DL networks, namely U-net, Bridged U-net and Dense U-net. The proposed method achieved an improvement of 24%, 12% and 15% for the U-net, Bridged U-net and Dense U-net, respectively, in terms of Dice score.
{"title":"A Deep Learning-based cropping technique to improve segmentation of prostate's peripheral zone","authors":"D. Zaridis, E. Mylona, N. Tachos, K. Marias, M. Tsiknakis, D. Fotiadis","doi":"10.1109/BIBE52308.2021.9635576","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635576","url":null,"abstract":"Automatic segmentation of the prostate peripheral zone on Magnetic Resonance Images (MRI) is a necessary but challenging step for accurate prostate cancer diagnosis. Deep learning (DL) based methods, such as U-Net, have recently been developed to segment the prostate and its' sub-regions. Nevertheless, the presence of class imbalance in the image labels, where the background pixels dominate over the region to be segmented, may severely hamper the segmentation performance. In the present work, we propose a DL-based preprocessing pipeline for segmenting the peripheral zone of the prostate by cropping unnecessary information without making a priori assumptions regarding the location of the region of interest. The effect of DL-cropping for improving the segmentation performance was compared to the standard center-cropping using three state-of-the-art DL networks, namely U-net, Bridged U-net and Dense U-net. The proposed method achieved an improvement of 24%, 12% and 15% for the U-net, Bridged U-net and Dense U-net, respectively, in terms of Dice score.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128952589","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635348
M. Antonakakis, K. Politof, Georgios A. Klados, Glykeria Sdoukopoulou, S. Schiza, M. Papadogiorgaki, C. Farmaki, M. Pediaditis, M. Zervakis, V. Sakkalis
During sleep., breathing-related sleep disorders (BSD) are very probable to cause distortions on human health and even be life-threatening. Among the different types of BSD., apnea accounts for one of the most common. Many detection algorithms have been proposed for spotting and classifying apneas, using one feature or being designed for binary classification. Also, many proposed clinical setups for respiratory data acquisition are invasive, making the application to patients a non-trial task. In this study, we aim to propose an easy-to-apply and patient-friendly clinical setup with a BSD detection that utilizes a multi-feature classification scheme for binary (apnea, healthy), as well as multiple classes (healthy, central, mixed, and obstructive apneas and hypopneas). Our clinical setup includes a high-resolution microphone attached to the bed at a very close distance to the patient. Our multi-feature approach contains spectral, statistical, and symbolic-based characteristics of respiratory signals of five patients admitted for a first BSD diagnosis and assesses the performance of different classification algorithms iteratively. The results show a high classification performance ($>$ 98% for binary and $>$ 84% for multi-class classification) for either classification scheme. A robust classification scheme is thus proposed, utilizing the entire content of the recorded respiratory signal. Such a classification scheme leads to a promising result towards the design of portable devices with multi-features for real-time detection of BSD.
{"title":"A New Multi-Feature Classification Scheme for Normal and Abnormal Respiratory Sounds Discrimination","authors":"M. Antonakakis, K. Politof, Georgios A. Klados, Glykeria Sdoukopoulou, S. Schiza, M. Papadogiorgaki, C. Farmaki, M. Pediaditis, M. Zervakis, V. Sakkalis","doi":"10.1109/BIBE52308.2021.9635348","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635348","url":null,"abstract":"During sleep., breathing-related sleep disorders (BSD) are very probable to cause distortions on human health and even be life-threatening. Among the different types of BSD., apnea accounts for one of the most common. Many detection algorithms have been proposed for spotting and classifying apneas, using one feature or being designed for binary classification. Also, many proposed clinical setups for respiratory data acquisition are invasive, making the application to patients a non-trial task. In this study, we aim to propose an easy-to-apply and patient-friendly clinical setup with a BSD detection that utilizes a multi-feature classification scheme for binary (apnea, healthy), as well as multiple classes (healthy, central, mixed, and obstructive apneas and hypopneas). Our clinical setup includes a high-resolution microphone attached to the bed at a very close distance to the patient. Our multi-feature approach contains spectral, statistical, and symbolic-based characteristics of respiratory signals of five patients admitted for a first BSD diagnosis and assesses the performance of different classification algorithms iteratively. The results show a high classification performance ($>$ 98% for binary and $>$ 84% for multi-class classification) for either classification scheme. A robust classification scheme is thus proposed, utilizing the entire content of the recorded respiratory signal. Such a classification scheme leads to a promising result towards the design of portable devices with multi-features for real-time detection of BSD.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116544666","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635492
A. Suwalska, J. Polańska
Mass cytometry as an advanced single-cell analysis technology can produce high-dimensional data consisting of millions of cells and more than 50 features. Therefore the cell subtypes identification is difficult and impossible to be done manually. Each step of the analysis affect the results and may cause a loss of rare sub-populations of interest. One of the first steps in the analysis is pre-gating which involves filtering out unwanted measurements like debris or doublets. The existing semi-automated solutions for pre-gating require some parameters to be set which may lead to different results. Moreover, the tools often use downsampling from millions to thousands of cells. Despite the existing methods, there is still a need for a fully automated tool that will be independent of sample size. In the study, we developed a solution based on Gaussian Mixture Model (GMM) decomposition and grouping of its components into clusters. Based on the clusters we propose filtration criteria that identify measurements to be removed from the analysis. The algorithm was validated on two independent public datasets. The results are promising and reproducible, leaving intact, live cells that can be further analyzed.
{"title":"Preliminary study for a fully automated pre-gating method for high-dimensional mass cytometry data","authors":"A. Suwalska, J. Polańska","doi":"10.1109/BIBE52308.2021.9635492","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635492","url":null,"abstract":"Mass cytometry as an advanced single-cell analysis technology can produce high-dimensional data consisting of millions of cells and more than 50 features. Therefore the cell subtypes identification is difficult and impossible to be done manually. Each step of the analysis affect the results and may cause a loss of rare sub-populations of interest. One of the first steps in the analysis is pre-gating which involves filtering out unwanted measurements like debris or doublets. The existing semi-automated solutions for pre-gating require some parameters to be set which may lead to different results. Moreover, the tools often use downsampling from millions to thousands of cells. Despite the existing methods, there is still a need for a fully automated tool that will be independent of sample size. In the study, we developed a solution based on Gaussian Mixture Model (GMM) decomposition and grouping of its components into clusters. Based on the clusters we propose filtration criteria that identify measurements to be removed from the analysis. The algorithm was validated on two independent public datasets. The results are promising and reproducible, leaving intact, live cells that can be further analyzed.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132528542","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635264
P. Priya, Srinivasan Jayaraman
The distribution of M-cells have always been vital in creating intrinsic spatial heterogeneity thereby acting as a substrate for the development and maintenance of re-entry. Here, a 2D anisotropic transmural tissue made up of endocardial (endo), midmyocardial (mid) and epicardial (epi) layers was constructed by using the ventricular cell model developed by Ten Tusscher et al. Two configurations, the entire column of mid layer and an island within the mid layer of the tissue were considered as M cells. In the latter configuration, slight alterations were introduced in the slow delayed rectifying potassium current and the outward transient current so that the APD is highest in the M-cells followed by the endo, mid and epi cells. The likelihood of reentry generation under conditions of KCNQ1-linked Short QT syndrome type 2 (SQTS2) was then analysed in these two types of tissue configurations. Simulation results show that on including SQTS2 conditions and on pacing the tissue with premature beats in between normal beats, re-entrant waves were generated in the tissue containing a column of M - cells whereas in the tissue including the M-cell island, re-entry was not generated. This study is not in line with those reported earlier due to the variations in the size of the chosen M -cell island as well as the cellular electrophysiological properties. From this investigation, the need for further analysis on the size, location as well as the ionic properties of the M-cells in relation to the neighbouring cells has been emphasized.
{"title":"Influence of M-cells on the generation of re-entry in Short QT Syndrome","authors":"P. Priya, Srinivasan Jayaraman","doi":"10.1109/BIBE52308.2021.9635264","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635264","url":null,"abstract":"The distribution of M-cells have always been vital in creating intrinsic spatial heterogeneity thereby acting as a substrate for the development and maintenance of re-entry. Here, a 2D anisotropic transmural tissue made up of endocardial (endo), midmyocardial (mid) and epicardial (epi) layers was constructed by using the ventricular cell model developed by Ten Tusscher et al. Two configurations, the entire column of mid layer and an island within the mid layer of the tissue were considered as M cells. In the latter configuration, slight alterations were introduced in the slow delayed rectifying potassium current and the outward transient current so that the APD is highest in the M-cells followed by the endo, mid and epi cells. The likelihood of reentry generation under conditions of KCNQ1-linked Short QT syndrome type 2 (SQTS2) was then analysed in these two types of tissue configurations. Simulation results show that on including SQTS2 conditions and on pacing the tissue with premature beats in between normal beats, re-entrant waves were generated in the tissue containing a column of M - cells whereas in the tissue including the M-cell island, re-entry was not generated. This study is not in line with those reported earlier due to the variations in the size of the chosen M -cell island as well as the cellular electrophysiological properties. From this investigation, the need for further analysis on the size, location as well as the ionic properties of the M-cells in relation to the neighbouring cells has been emphasized.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131792099","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635420
N. Draginic, M. Andjic, V. Zivkovic, Kristina Radoman, M. Nikolić, Maja Savić, A. M. Samanovic, S. Bolevich, V. Jakovljevic
Taken into consideration that oxidative stress response to flaxseed (FSO) and evening primrose oil (EPO) has still not been clarified, the aim of this study was to assess the effects of these two oils, rich in omega-3 and omega-6 polyunsaturated fatty acids on systemic redox status in male and female Wistar albino rats. The study was carried out on 60 Wistar albino rats classified into two groups, male and female rats. Both groups were divided into three subgroups according to applied oil. The first subgroup was control group, without treatment. The second and third subgroups included animals treated with FSO or EPO in a dose of 300mg/kg/day and 10mg/kg/day per os, respectively. After 6 weeks of treatment, the animals were sacrificed. Following pro-oxidative markers were measured spectrophotometrically from plasma samples: nitrites (NO2-), superoxide anion radical (02-), hydrogen peroxide (H2O2), index of lipid peroxidation (TBARS). Parameters of antioxidant protection were measured from erythrocyte lysate: superoxide dismutase (SOD), catalase (CAT), and reduced glutathione (GSH). No significant gender specific differences in pro-oxidant markers were noticed in between EPO and FSO groups (p>0.05). Both EPO and FSO significantly increased SOD and GSH compared to CTRL in both genders (p<0.05), while FSO improved CAT values only in males, and EPO only in females. Chronic administration of EPO and FSO omega 3and 6 rich plant oils improved antioxidant defense system with slight gender specific differences in CAT. It's effect on pro-oxidants didn't seem to be protective.
{"title":"The impact of chronic administration of evening primrose oil and flaxseed oil on redox status of male and female Wistar albino rats","authors":"N. Draginic, M. Andjic, V. Zivkovic, Kristina Radoman, M. Nikolić, Maja Savić, A. M. Samanovic, S. Bolevich, V. Jakovljevic","doi":"10.1109/BIBE52308.2021.9635420","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635420","url":null,"abstract":"Taken into consideration that oxidative stress response to flaxseed (FSO) and evening primrose oil (EPO) has still not been clarified, the aim of this study was to assess the effects of these two oils, rich in omega-3 and omega-6 polyunsaturated fatty acids on systemic redox status in male and female Wistar albino rats. The study was carried out on 60 Wistar albino rats classified into two groups, male and female rats. Both groups were divided into three subgroups according to applied oil. The first subgroup was control group, without treatment. The second and third subgroups included animals treated with FSO or EPO in a dose of 300mg/kg/day and 10mg/kg/day per os, respectively. After 6 weeks of treatment, the animals were sacrificed. Following pro-oxidative markers were measured spectrophotometrically from plasma samples: nitrites (NO2-), superoxide anion radical (02-), hydrogen peroxide (H2O2), index of lipid peroxidation (TBARS). Parameters of antioxidant protection were measured from erythrocyte lysate: superoxide dismutase (SOD), catalase (CAT), and reduced glutathione (GSH). No significant gender specific differences in pro-oxidant markers were noticed in between EPO and FSO groups (p>0.05). Both EPO and FSO significantly increased SOD and GSH compared to CTRL in both genders (p<0.05), while FSO improved CAT values only in males, and EPO only in females. Chronic administration of EPO and FSO omega 3and 6 rich plant oils improved antioxidant defense system with slight gender specific differences in CAT. It's effect on pro-oxidants didn't seem to be protective.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134588474","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635557
Nathan Siu, Maxime Ruiz, Sheyla González Garrido, Yu Yan, Dylan Steinecke, Elizabeth Rao, Rachel Choi, S. Robertson, S. Deng, C. Arnold, W. Speier
Limbal stem cell deficiency (LSCD) is a progressive corneal disease that renders the corneal epithelium unable to repair itself, which can lead to the eventual loss of vision. Advances in technology have allowed for the growth of limbal stem cells ex-vivo for the purposes of transplantation. One method used to evaluate the quality of these cultivated cells is cell density, which is typically calculated manually by experts, which is time-consuming and has high inter-rater variability. The goal of this project was to create a tool that automatically calculates cell density from digital images of the cultured cells. Results were compared against annotations from four experts with varying levels of experience. Cell counts had high correlation with expert annotations (r=0.64, p<0.01). When compared to human annotators with lower clinical experience, the algorithm achieved significantly better agreement with highly experienced annotators (r=0.75 vs r=0.19, p<0.01). These results suggest that the automated tool can provide meaningful cell density counts, which can potentially improve annotation consistency and reduce time required for evaluating LSCD cell cultures.
角膜缘干细胞缺乏症(LSCD)是一种进行性角膜疾病，导致角膜上皮无法自我修复，最终导致视力丧失。技术的进步使得角膜缘干细胞的体外生长能够用于移植。用于评估这些培养细胞质量的一种方法是细胞密度，这通常是由专家手动计算的，这是耗时的，并且具有很高的比率变异性。这个项目的目标是创建一个工具，可以从培养细胞的数字图像中自动计算细胞密度。结果与四位不同经验水平的专家的注释进行了比较。细胞计数与专家注释高度相关(r=0.64, p<0.01)。与临床经验较低的人类注释者相比，该算法与经验丰富的注释者的一致性明显更好(r=0.75 vs r=0.19, p<0.01)。这些结果表明，自动化工具可以提供有意义的细胞密度计数，这可以潜在地提高注释一致性并减少评估LSCD细胞培养所需的时间。
{"title":"Automatic Estimation of Limbal Stem Cell Densities in Cultured Epithelial Cell Microscopy Imaging","authors":"Nathan Siu, Maxime Ruiz, Sheyla González Garrido, Yu Yan, Dylan Steinecke, Elizabeth Rao, Rachel Choi, S. Robertson, S. Deng, C. Arnold, W. Speier","doi":"10.1109/BIBE52308.2021.9635557","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635557","url":null,"abstract":"Limbal stem cell deficiency (LSCD) is a progressive corneal disease that renders the corneal epithelium unable to repair itself, which can lead to the eventual loss of vision. Advances in technology have allowed for the growth of limbal stem cells ex-vivo for the purposes of transplantation. One method used to evaluate the quality of these cultivated cells is cell density, which is typically calculated manually by experts, which is time-consuming and has high inter-rater variability. The goal of this project was to create a tool that automatically calculates cell density from digital images of the cultured cells. Results were compared against annotations from four experts with varying levels of experience. Cell counts had high correlation with expert annotations (r=0.64, p<0.01). When compared to human annotators with lower clinical experience, the algorithm achieved significantly better agreement with highly experienced annotators (r=0.75 vs r=0.19, p<0.01). These results suggest that the automated tool can provide meaningful cell density counts, which can potentially improve annotation consistency and reduce time required for evaluating LSCD cell cultures.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133597672","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635556
C. Leung, Thanh Huy Daniel Mai, N. D. Tran, Christine Y. Zhang
Bioinformatics and health informatics-in conjection with data science, data mining and machine learning-have been applied in numerous real-life applications including disease and healthcare analytics, such as predictive analytics of coronavirus disease 2019 (COVID-19). Many of these existing works usually require large volumes of data train the classification and prediction models. However, these data (e.g., computed tomography (CT) scan images, viral/molecular test results) that can be expensive to produce and/or not easily accessible. For instance, partially due to privacy concerns and other factors, the volume of available disease data can be limited. Hence, in this paper, we present a predictive analytics system to support health analytics. Specifically, the system make good use of autoencoder and few-shot learning to train the prediction model with only a few samples of more accessible and less expensive types of data (e.g., serology/antibody test results from blood samples), which helps to support prediction on classification of potential patients (e.g., potential COVID-19 patients). Moreover, the system also provides users (e.g., healthcare providers) with predictions on hospitalization status and clinical outcomes of COVID-19 patients. This provides healthcare administrators and staff with a good estimate on the demand for healthcare support. With this system, users could then focus and provide timely treatment to the true patients, thus preventing them for spreading the disease in the community. The system is helpful, especially for rural areas, when sophisticated equipment (e.g., CT scanners) may be unavailable. Evaluation results on a real-life datasets demonstrate the effectiveness of our digital health system in health analytics, especially in classifying patients and their medical needs.
{"title":"Predictive Analytics to Support Health Informatics on COVID-19 Data","authors":"C. Leung, Thanh Huy Daniel Mai, N. D. Tran, Christine Y. Zhang","doi":"10.1109/BIBE52308.2021.9635556","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635556","url":null,"abstract":"Bioinformatics and health informatics-in conjection with data science, data mining and machine learning-have been applied in numerous real-life applications including disease and healthcare analytics, such as predictive analytics of coronavirus disease 2019 (COVID-19). Many of these existing works usually require large volumes of data train the classification and prediction models. However, these data (e.g., computed tomography (CT) scan images, viral/molecular test results) that can be expensive to produce and/or not easily accessible. For instance, partially due to privacy concerns and other factors, the volume of available disease data can be limited. Hence, in this paper, we present a predictive analytics system to support health analytics. Specifically, the system make good use of autoencoder and few-shot learning to train the prediction model with only a few samples of more accessible and less expensive types of data (e.g., serology/antibody test results from blood samples), which helps to support prediction on classification of potential patients (e.g., potential COVID-19 patients). Moreover, the system also provides users (e.g., healthcare providers) with predictions on hospitalization status and clinical outcomes of COVID-19 patients. This provides healthcare administrators and staff with a good estimate on the demand for healthcare support. With this system, users could then focus and provide timely treatment to the true patients, thus preventing them for spreading the disease in the community. The system is helpful, especially for rural areas, when sophisticated equipment (e.g., CT scanners) may be unavailable. Evaluation results on a real-life datasets demonstrate the effectiveness of our digital health system in health analytics, especially in classifying patients and their medical needs.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133456994","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635321
M. Gacic, Milica Kaplarevic, N. Filipovic
The world stent market has an estimated value of €6.4 billion, of which 37% is generated in the US and 10% in the EU. Coronary stents are now the most commonly implanted medical devices, with more than 1 million implanted annually. Coronary stents are currently the most widely used for treating symptomatic coronary disease. In this study, the traditional approach for today's clinical trials with only 10% success rate was described. Within the EU funded project InSilc (www.insilc.eu) was developed the innovative platform for designing, developing and assessing coronary stents. It consists of separate modules and some of them can be used as a standalone tool. Description of each module was given. Cost-effectiveness analysis described the calculation method of the prices of each module as well as platform as a whole, per one stent simulation. The average cost per patient for the execution of a real clinical trial was calculated. The calculated price for in silico trials is below breakeven point in comparison to real clinical trial.
{"title":"Cost-effectiveness analysis of in silico clinical trials of vascular stents","authors":"M. Gacic, Milica Kaplarevic, N. Filipovic","doi":"10.1109/BIBE52308.2021.9635321","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635321","url":null,"abstract":"The world stent market has an estimated value of €6.4 billion, of which 37% is generated in the US and 10% in the EU. Coronary stents are now the most commonly implanted medical devices, with more than 1 million implanted annually. Coronary stents are currently the most widely used for treating symptomatic coronary disease. In this study, the traditional approach for today's clinical trials with only 10% success rate was described. Within the EU funded project InSilc (www.insilc.eu) was developed the innovative platform for designing, developing and assessing coronary stents. It consists of separate modules and some of them can be used as a standalone tool. Description of each module was given. Cost-effectiveness analysis described the calculation method of the prices of each module as well as platform as a whole, per one stent simulation. The average cost per patient for the execution of a real clinical trial was calculated. The calculated price for in silico trials is below breakeven point in comparison to real clinical trial.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117237692","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635187
P. Dzianok, M. Kołodziej, E. Kublik
The aim of this study was to investigate supervised machine learning approaches for detecting attentive brain states in the electroencephalogram (EEG) signal. EEG was recorded during methodologically similar tasks with different attentional loads: choice-reaction task (CRT) and simple-reaction task (SRT). This approach minimalizes the influence of other cognitive processes or motor preparation on classification results and thus shows the real discrimination of attentive states. We applied a Hilbert transformation to single trial EEG data to extract selected signal features and then compared the effectiveness of three classifiers: Extra Trees (ET), Support vector machines (SVM) and logistic regression; as well as two methods of feature selection: an ANOVA-based method and Sequential backward floating selection (SBFS). ET and SVM classifiers and logistic regression yielded similar classification results. Classification accuracy was up to 100% for individual subjects and 89% was the average classification accuracy for all subjects after SBFS with the use of ET and logistic regression. ET achieved the highest precision (91%) and specificity (91 %), whereas highest sensitivity (89%) was observed for LR.
{"title":"Detecting attention in Hilbert-transformed EEG brain signals from simple-reaction and choice-reaction cognitive tasks","authors":"P. Dzianok, M. Kołodziej, E. Kublik","doi":"10.1109/BIBE52308.2021.9635187","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635187","url":null,"abstract":"The aim of this study was to investigate supervised machine learning approaches for detecting attentive brain states in the electroencephalogram (EEG) signal. EEG was recorded during methodologically similar tasks with different attentional loads: choice-reaction task (CRT) and simple-reaction task (SRT). This approach minimalizes the influence of other cognitive processes or motor preparation on classification results and thus shows the real discrimination of attentive states. We applied a Hilbert transformation to single trial EEG data to extract selected signal features and then compared the effectiveness of three classifiers: Extra Trees (ET), Support vector machines (SVM) and logistic regression; as well as two methods of feature selection: an ANOVA-based method and Sequential backward floating selection (SBFS). ET and SVM classifiers and logistic regression yielded similar classification results. Classification accuracy was up to 100% for individual subjects and 89% was the average classification accuracy for all subjects after SBFS with the use of ET and logistic regression. ET achieved the highest precision (91%) and specificity (91 %), whereas highest sensitivity (89%) was observed for LR.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133317090","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635563
J. Ge, H. Saeidi, M. Kam, J. Opfermann, A. Krieger
Surgical resection is the current clinical standard of care for treating squamous cell carcinoma. Maintaining an adequate tumor resection margin is the key to a good surgical outcome, but tumor edge delineation errors are inevitable with manual surgery due to difficulty in visualization and hand-eye coordination. Surgical automation is a growing field of robotics to relieve surgeon burdens and to achieve a consistent and potentially better surgical outcome. This paper reports a novel robotic supervised autonomous electrosurgery technique for soft tissue resection achieving millimeter accuracy. The tumor resection procedure is decomposed to the subtask level for a more direct understanding and automation. A 4-DOF suction system is developed, and integrated with a 6-DOF electrocautery robot to perform resection experiments. A novel near-infrared fluorescent marker is manually dispensed on cadaver samples to define a pseudotumor, and intraoperatively tracked using a dual-camera system. The autonomous dual-robot resection cooperation workflow is proposed and evaluated in this study. The integrated system achieves autonomous localization of the pseudotumor by tracking the near-infrared marker, and performs supervised autonomous resection in cadaver porcine tongues (N =3). The three pseudotumors were successfully removed from porcine samples. The evaluated average surface and depth resection errors are 1.19 and 1.83mm, respectively. This work is an essential step towards autonomous tumor resections.
{"title":"Supervised Autonomous Electrosurgery for Soft Tissue Resection","authors":"J. Ge, H. Saeidi, M. Kam, J. Opfermann, A. Krieger","doi":"10.1109/BIBE52308.2021.9635563","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635563","url":null,"abstract":"Surgical resection is the current clinical standard of care for treating squamous cell carcinoma. Maintaining an adequate tumor resection margin is the key to a good surgical outcome, but tumor edge delineation errors are inevitable with manual surgery due to difficulty in visualization and hand-eye coordination. Surgical automation is a growing field of robotics to relieve surgeon burdens and to achieve a consistent and potentially better surgical outcome. This paper reports a novel robotic supervised autonomous electrosurgery technique for soft tissue resection achieving millimeter accuracy. The tumor resection procedure is decomposed to the subtask level for a more direct understanding and automation. A 4-DOF suction system is developed, and integrated with a 6-DOF electrocautery robot to perform resection experiments. A novel near-infrared fluorescent marker is manually dispensed on cadaver samples to define a pseudotumor, and intraoperatively tracked using a dual-camera system. The autonomous dual-robot resection cooperation workflow is proposed and evaluated in this study. The integrated system achieves autonomous localization of the pseudotumor by tracking the near-infrared marker, and performs supervised autonomous resection in cadaver porcine tongues (N =3). The three pseudotumors were successfully removed from porcine samples. The evaluated average surface and depth resection errors are 1.19 and 1.83mm, respectively. This work is an essential step towards autonomous tumor resections.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122621471","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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