Pub Date : 2022-12-21DOI: 10.1109/ICBME57741.2022.10052875
Mohammad Hossein Gohari Raouf, A. Fallah, S. Rashidi
biomedical diagnostic tool for the detection of tumors in the brain since it provides detailed and comprehensive information associated with the brain's anatomical structures. The radiologist can detect the existence of malignancies or aberrant cell growths using MRI images. Early-stage brain tumor diagnosis and treatment are greatly aided by MRI image processing. This study inquires about a method for classifying MRI brain images into without tumors and brain tumors to detect tumors using these images. These days, researchers can create reliable Computer-Aided Diagnosis (CAD) systems for identifying tumors and healthy brains thanks to the benefits of machine learning. A crucial stage in any machine-learning model is feature extraction. Time-frequency analysis techniques are more effective for image classification applications since they provide localized information. We suggested using the Discrete Cosine-based Stockwell Transform (DCST) to extract the efficacious features from brain MRI images and create the feature matrix after pre-processing and segmentation. The feature matrix's dimension was decreased using the chi-square test. A Support Vector Machine (SVM) classifies the selected features at the end. We employed a dataset containing 7023 brain MRI images divided into four classes: tumors of the pituitary, glioma, meningioma, and without tumors. For binary classification into brain tumors and no tumors, we attained an accuracy of 97.71%.
{"title":"Use of Discrete Cosine-based Stockwell Transform in the Binary Classification of Magnetic Resonance Images of Brain Tumor","authors":"Mohammad Hossein Gohari Raouf, A. Fallah, S. Rashidi","doi":"10.1109/ICBME57741.2022.10052875","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10052875","url":null,"abstract":"biomedical diagnostic tool for the detection of tumors in the brain since it provides detailed and comprehensive information associated with the brain's anatomical structures. The radiologist can detect the existence of malignancies or aberrant cell growths using MRI images. Early-stage brain tumor diagnosis and treatment are greatly aided by MRI image processing. This study inquires about a method for classifying MRI brain images into without tumors and brain tumors to detect tumors using these images. These days, researchers can create reliable Computer-Aided Diagnosis (CAD) systems for identifying tumors and healthy brains thanks to the benefits of machine learning. A crucial stage in any machine-learning model is feature extraction. Time-frequency analysis techniques are more effective for image classification applications since they provide localized information. We suggested using the Discrete Cosine-based Stockwell Transform (DCST) to extract the efficacious features from brain MRI images and create the feature matrix after pre-processing and segmentation. The feature matrix's dimension was decreased using the chi-square test. A Support Vector Machine (SVM) classifies the selected features at the end. We employed a dataset containing 7023 brain MRI images divided into four classes: tumors of the pituitary, glioma, meningioma, and without tumors. For binary classification into brain tumors and no tumors, we attained an accuracy of 97.71%.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123460087","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 : 2022-12-21DOI: 10.1109/ICBME57741.2022.10052821
Amin Hoseini, S. Hosseini-Zahraei, A. Akbarzadeh
Gait analysis is one of the major topics in rehabilitation and sport. Tracking and determining gait phases can be done using various sensors and methods. In this paper, a fuzzy logic method is proposed to analyze and detect the five phases of a gait cycle using ground reaction force (GRF) and its gradient. The proposed method enables better detection adaptability at different walking speeds and body weights compared with the traditional threshold algorithms. In this algorithm, the GRF, measured by an insole equipped with force sensing resistors (FSR) and GRF gradient, which represent the plantar pressure transmission during a cycle, is passed through a set of fuzzy rules to detect the five gaits. A genetic algorithm (GA) is also applied for optimizing the fuzzy logic membership functions to reach minimum detection delay. A cost function is defined based on the difference between the normal reference gait and the output of the fuzzy logic gait phases. Detected phases are IC (initial contact), LR (loading response), MS (mid-stance), PS (pre-swing), and SW (swing). It is shown that the proposed method reaches a highly reliable performance of phase detection, especially for the initial contact (IC) and toe-off (TO). The average detection delays for the IC and TO phases, using the fuzzy-based method for three walking speeds of 0.4, 0.85, and 1.3 m/s, were -14.3±16.9ms and 1.24±17.0ms, respectively, and the average duration of stance and swing phases are 61.42% and 38.58%, respectively.
{"title":"Fuzzy-Based Gait Events Detection System During Level-Ground Walking Using Wearable Insole","authors":"Amin Hoseini, S. Hosseini-Zahraei, A. Akbarzadeh","doi":"10.1109/ICBME57741.2022.10052821","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10052821","url":null,"abstract":"Gait analysis is one of the major topics in rehabilitation and sport. Tracking and determining gait phases can be done using various sensors and methods. In this paper, a fuzzy logic method is proposed to analyze and detect the five phases of a gait cycle using ground reaction force (GRF) and its gradient. The proposed method enables better detection adaptability at different walking speeds and body weights compared with the traditional threshold algorithms. In this algorithm, the GRF, measured by an insole equipped with force sensing resistors (FSR) and GRF gradient, which represent the plantar pressure transmission during a cycle, is passed through a set of fuzzy rules to detect the five gaits. A genetic algorithm (GA) is also applied for optimizing the fuzzy logic membership functions to reach minimum detection delay. A cost function is defined based on the difference between the normal reference gait and the output of the fuzzy logic gait phases. Detected phases are IC (initial contact), LR (loading response), MS (mid-stance), PS (pre-swing), and SW (swing). It is shown that the proposed method reaches a highly reliable performance of phase detection, especially for the initial contact (IC) and toe-off (TO). The average detection delays for the IC and TO phases, using the fuzzy-based method for three walking speeds of 0.4, 0.85, and 1.3 m/s, were -14.3±16.9ms and 1.24±17.0ms, respectively, and the average duration of stance and swing phases are 61.42% and 38.58%, respectively.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124187001","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 : 2022-12-21DOI: 10.1109/ICBME57741.2022.10052879
A. Sabzevari, H. Sabahi
Montmorillonite (MMT), a nanolayered silicate, is recently used as an oral drug delivery vehicle and also as a functional component in many oral bio-organic drug delivery nanosystems, resulting in increasing the drug bioavailability. This raises concerns about the possible toxic effects of MMT on the intestine and liver as the first and second organs exposed to MMT after oral administration. Here, we investigated the effects of MMT on human intestinal HT-29 (as an enterocyte model) and hepatic HepG2 cells in cellular and molecular levels using MTT assay, flow cytometry and qRT-PCR. The results showed that the tolerable MMT concentrations for HT-29 and HepG2 cells were up to 500 and 300 μg/mL in the presence of serum proteins and reduced to 50 and 25 μg/mL in the absence of serum proteins, respectively, indicating that MMT is much more toxic before absorption into the body. At the higher concentrations, MMT arrested HT-29 and HepG2 cells in G0/G1 and S phases, respectively. Also, MMT induced apoptosis in both HepG2 and HT-29 cells, and necrosis in HT-29 cells. These results suggest that, although MMT over a wide range of concentrations is safe for the intestinal and hepatic cells in the presence of serum proteins, in the intestinal lumen, where serum proteins are absent, high concentrations of MMT may cause cell damage if other free proteins are not present. Also, MMT may cause hepatotoxicity if it is accumulated in the liver following long-term/high-dose administrations.
{"title":"Investigating the Cytotoxicity of Montmorillonite Nanoparticles as a Carrier for Oral Drug Delivery Systems","authors":"A. Sabzevari, H. Sabahi","doi":"10.1109/ICBME57741.2022.10052879","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10052879","url":null,"abstract":"Montmorillonite (MMT), a nanolayered silicate, is recently used as an oral drug delivery vehicle and also as a functional component in many oral bio-organic drug delivery nanosystems, resulting in increasing the drug bioavailability. This raises concerns about the possible toxic effects of MMT on the intestine and liver as the first and second organs exposed to MMT after oral administration. Here, we investigated the effects of MMT on human intestinal HT-29 (as an enterocyte model) and hepatic HepG2 cells in cellular and molecular levels using MTT assay, flow cytometry and qRT-PCR. The results showed that the tolerable MMT concentrations for HT-29 and HepG2 cells were up to 500 and 300 μg/mL in the presence of serum proteins and reduced to 50 and 25 μg/mL in the absence of serum proteins, respectively, indicating that MMT is much more toxic before absorption into the body. At the higher concentrations, MMT arrested HT-29 and HepG2 cells in G0/G1 and S phases, respectively. Also, MMT induced apoptosis in both HepG2 and HT-29 cells, and necrosis in HT-29 cells. These results suggest that, although MMT over a wide range of concentrations is safe for the intestinal and hepatic cells in the presence of serum proteins, in the intestinal lumen, where serum proteins are absent, high concentrations of MMT may cause cell damage if other free proteins are not present. Also, MMT may cause hepatotoxicity if it is accumulated in the liver following long-term/high-dose administrations.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130740704","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 : 2022-12-21DOI: 10.1109/ICBME57741.2022.10052912
Siavash Shams, Sana Motallebi, M. Yazdanpanah
The regions of the brain may be viewed as nodes in a complex network where information is dynamically transferred through synchronization. Synchronization plays an important role in learning, emotions, and motion. However, neurological disorders such as epilepsy are known to result from abnormal brain synchronization. Coupled Kuramoto model with a little integration of the neurological factors can be a suitable model of the brain network. In this paper, we present an open-loop data-driven control strategy to effectively desynchronize the activity of brain regions during a simulated seizure episode without making any assumptions about the dynamics of the brain. In order to quantify the significance of network nodes, we used an energy-based optimization problem. Then, we evaluated our control methods using a genuine connectome with 80 regions and demonstrated that our approach remarkably decreased synchrony between phases of the oscillations of the brain during the epileptic seizure. Finally, we conclude that brain epilepsy synchronization can be controlled by applying external inputs to the chosen optimal set of driver nodes.
{"title":"An Optimal Data-Driven Method for Controlling Epileptic Seizures","authors":"Siavash Shams, Sana Motallebi, M. Yazdanpanah","doi":"10.1109/ICBME57741.2022.10052912","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10052912","url":null,"abstract":"The regions of the brain may be viewed as nodes in a complex network where information is dynamically transferred through synchronization. Synchronization plays an important role in learning, emotions, and motion. However, neurological disorders such as epilepsy are known to result from abnormal brain synchronization. Coupled Kuramoto model with a little integration of the neurological factors can be a suitable model of the brain network. In this paper, we present an open-loop data-driven control strategy to effectively desynchronize the activity of brain regions during a simulated seizure episode without making any assumptions about the dynamics of the brain. In order to quantify the significance of network nodes, we used an energy-based optimization problem. Then, we evaluated our control methods using a genuine connectome with 80 regions and demonstrated that our approach remarkably decreased synchrony between phases of the oscillations of the brain during the epileptic seizure. Finally, we conclude that brain epilepsy synchronization can be controlled by applying external inputs to the chosen optimal set of driver nodes.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129794064","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 : 2022-12-21DOI: 10.1109/ICBME57741.2022.10052954
Arman Beykmohammadi, Zahra Ghanbari, M. Moradi
Post Traumatic Stress Disorder (PTSD) is a chronic mental and behavioral disorder that can develop following being exposed to a traumatic event. PTSD is diagnosed according to self-reports, which is prone to error in children and adults due to the fact that avoidance is one of the major symptoms of PTSD. In this paper, an automatic approach for diagnosing PTSD is proposed. We propose an EEG-based method since it is a low cost easily available imaging modality. Eyes closed resting-state EEG signals are recorded from 15 war-related PTSD and 15 matched control participants. After preprocessing, signals are divided into 1s segments. Time-frequency maps corresponding to each segment are achieved by applying the continuous wavelet transform. RGB images are generated using these time-frequency maps. They are fed to a convolutional neural network. In this paper, we use pre-trained VGG16 with proper modifications in its fully connected and classifier layers. To our best knowledge, this is the first study that uses deep transfer learning for diagnosing PTSD based on EEG signals. Our results suggest that the proposed approach can be an appropriate method for this purpose.
{"title":"PTSD Diagnosis using Deep Transfer Learning: an EEG Study","authors":"Arman Beykmohammadi, Zahra Ghanbari, M. Moradi","doi":"10.1109/ICBME57741.2022.10052954","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10052954","url":null,"abstract":"Post Traumatic Stress Disorder (PTSD) is a chronic mental and behavioral disorder that can develop following being exposed to a traumatic event. PTSD is diagnosed according to self-reports, which is prone to error in children and adults due to the fact that avoidance is one of the major symptoms of PTSD. In this paper, an automatic approach for diagnosing PTSD is proposed. We propose an EEG-based method since it is a low cost easily available imaging modality. Eyes closed resting-state EEG signals are recorded from 15 war-related PTSD and 15 matched control participants. After preprocessing, signals are divided into 1s segments. Time-frequency maps corresponding to each segment are achieved by applying the continuous wavelet transform. RGB images are generated using these time-frequency maps. They are fed to a convolutional neural network. In this paper, we use pre-trained VGG16 with proper modifications in its fully connected and classifier layers. To our best knowledge, this is the first study that uses deep transfer learning for diagnosing PTSD based on EEG signals. Our results suggest that the proposed approach can be an appropriate method for this purpose.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126769539","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 : 2022-12-21DOI: 10.1109/ICBME57741.2022.10052918
Pooya Abdi, B. Vahidi
Topography of extracellular matrix plays a major role in many biological events including tissue healing, morphogenesis and growth. It is known that matrix constitution and mechanical properties are deciding factors in governing the fate of its inhabitant cells. Besides the direct mechanical cues, matrices also facilitate the release and uptake of certain chemicals and participate in cell-cell and cell- ECM crosstalk. Mechanical strains in the matrix are proved to direct endothelial cell migration and elongation leading to angiogenesis, and there is a consensus that matrix stiffness, fiber density and fiber orientation can enhance angiogenesis in the preferred direction of stiffness gradient. In this study, we specifically investigated the role of topography in guidance of endothelial self-reorganization prompted by the effect of fluid flow hindrance and facilitation in certain directions. We adopted our previous model of fluid flow guided angiogenesis for cellular responses. Lattice Boltzmann model of fluid flow was adopted and modified to study the effect of unidirectional and randomly oriented fibers. To study the effect of fiber orientation, we customized a previously proposed model of porosity in lattice Boltzmann to suit this purpose. This model could reproduce the effects of fiber orientations in matrix on endothelial migration and vasculogenesis. Simulations showed better confluency of formed lumens when prescribed flow is in the direction of fiber orientation. These results can have further implications in understanding endothelial complications in certain diseases as well as in tumor angiogenesis and metastasis.
{"title":"Technologies. University of Tehran Flow-induced effect of matrix fiber orientation on endothelial vasculogenesis","authors":"Pooya Abdi, B. Vahidi","doi":"10.1109/ICBME57741.2022.10052918","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10052918","url":null,"abstract":"Topography of extracellular matrix plays a major role in many biological events including tissue healing, morphogenesis and growth. It is known that matrix constitution and mechanical properties are deciding factors in governing the fate of its inhabitant cells. Besides the direct mechanical cues, matrices also facilitate the release and uptake of certain chemicals and participate in cell-cell and cell- ECM crosstalk. Mechanical strains in the matrix are proved to direct endothelial cell migration and elongation leading to angiogenesis, and there is a consensus that matrix stiffness, fiber density and fiber orientation can enhance angiogenesis in the preferred direction of stiffness gradient. In this study, we specifically investigated the role of topography in guidance of endothelial self-reorganization prompted by the effect of fluid flow hindrance and facilitation in certain directions. We adopted our previous model of fluid flow guided angiogenesis for cellular responses. Lattice Boltzmann model of fluid flow was adopted and modified to study the effect of unidirectional and randomly oriented fibers. To study the effect of fiber orientation, we customized a previously proposed model of porosity in lattice Boltzmann to suit this purpose. This model could reproduce the effects of fiber orientations in matrix on endothelial migration and vasculogenesis. Simulations showed better confluency of formed lumens when prescribed flow is in the direction of fiber orientation. These results can have further implications in understanding endothelial complications in certain diseases as well as in tumor angiogenesis and metastasis.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123653298","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 : 2022-12-21DOI: 10.1109/ICBME57741.2022.10052859
Mojgan Azari, H. Rafiei, M. Akbarzadeh-T.
In recent years, wearable exoskeleton robots have been growingly used for rehabilitation or movement assistive purposes. Despite the growing application of these robots in various domains, such as physical therapy, the movement synchronization between robots and human bodies remains a challenging problem. This paper aims to achieve better synchronization by predicting human movement. Although several works have been presented in this domain, the robustness of these predictions has received less attention. This paper aims to provide a robust prediction using Completion-Generative Adversarial Networks (CGAN) that are learned based on the Huber loss function. Specifically, we reshape the 3D-joint-position-time series (jointxaxesxtime) into multivariate time series ((jointxaxes) xtime) and pass them to a CGAN. We use the Huber loss function to improve the GAN performance and offer higher robustness against noise in real-world applications. The proposed method is evaluated on an actual human gait dataset and compared with several recent works in this domain. Results show that the proposed method is superior to the previous works in prediction error, particularly in terms of achieving a better signal-to-noise ratio.
{"title":"Robust Human Movement Prediction by Completion-Generative Adversarial Networks with Huber Loss","authors":"Mojgan Azari, H. Rafiei, M. Akbarzadeh-T.","doi":"10.1109/ICBME57741.2022.10052859","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10052859","url":null,"abstract":"In recent years, wearable exoskeleton robots have been growingly used for rehabilitation or movement assistive purposes. Despite the growing application of these robots in various domains, such as physical therapy, the movement synchronization between robots and human bodies remains a challenging problem. This paper aims to achieve better synchronization by predicting human movement. Although several works have been presented in this domain, the robustness of these predictions has received less attention. This paper aims to provide a robust prediction using Completion-Generative Adversarial Networks (CGAN) that are learned based on the Huber loss function. Specifically, we reshape the 3D-joint-position-time series (jointxaxesxtime) into multivariate time series ((jointxaxes) xtime) and pass them to a CGAN. We use the Huber loss function to improve the GAN performance and offer higher robustness against noise in real-world applications. The proposed method is evaluated on an actual human gait dataset and compared with several recent works in this domain. Results show that the proposed method is superior to the previous works in prediction error, particularly in terms of achieving a better signal-to-noise ratio.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121346078","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 : 2022-12-21DOI: 10.1109/ICBME57741.2022.10052813
Sobhan Honarvar, A. Nourani, A. Yarandi, Fatemeh Farrahi Ghehi
The force applied to the foot is noticeable, and the foot is subjected to the limits of repetitive, prolonged muscular loading on a bone in some activities like sports that are not as heavily loaded in typical activities such as walking. Thus, it is mandatory that optimal footwear be designed with the lowest stress acting on the foot and maximum energy absorbed by the soles. In this study, the effects of some geometric features on the energy absorption of shoe soles were investigated using a finite element model (FEM). Auxetic structures showed some beneficial properties, including improved energy absorption. In addition, different types of holes in the midsoles of shoes were considered to reduce their weight. Therefore, this study investigated the effect of geometry by comparing auxetic shoes with re-entrant structures, shoes with weight-reducing holes with the same geometry as auxetic shoes, auxetic shoes with an auxetic structure similar to Nike RN 2017 shoes in the outsole, and shoes without auxetic structures and weight-reducing holes. A 3D finite element modeling was used to evaluate the effect of geometry on stress, displacement, and energy absorption. It was found that the strain energy of soles with re-entrant auxetic structures and with an auxetic structure in the outsole was 153 and 7% higher, respectively than that of plain soles. Similarly, adding weight-reducing holes increased the strain energy of the sole by almost 157%.
{"title":"Three-dimensional finite element modeling of the shoe sole to investigate the impact of various geometries on foot heel stresses and energy absorption","authors":"Sobhan Honarvar, A. Nourani, A. Yarandi, Fatemeh Farrahi Ghehi","doi":"10.1109/ICBME57741.2022.10052813","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10052813","url":null,"abstract":"The force applied to the foot is noticeable, and the foot is subjected to the limits of repetitive, prolonged muscular loading on a bone in some activities like sports that are not as heavily loaded in typical activities such as walking. Thus, it is mandatory that optimal footwear be designed with the lowest stress acting on the foot and maximum energy absorbed by the soles. In this study, the effects of some geometric features on the energy absorption of shoe soles were investigated using a finite element model (FEM). Auxetic structures showed some beneficial properties, including improved energy absorption. In addition, different types of holes in the midsoles of shoes were considered to reduce their weight. Therefore, this study investigated the effect of geometry by comparing auxetic shoes with re-entrant structures, shoes with weight-reducing holes with the same geometry as auxetic shoes, auxetic shoes with an auxetic structure similar to Nike RN 2017 shoes in the outsole, and shoes without auxetic structures and weight-reducing holes. A 3D finite element modeling was used to evaluate the effect of geometry on stress, displacement, and energy absorption. It was found that the strain energy of soles with re-entrant auxetic structures and with an auxetic structure in the outsole was 153 and 7% higher, respectively than that of plain soles. Similarly, adding weight-reducing holes increased the strain energy of the sole by almost 157%.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115337990","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 : 2022-12-21DOI: 10.1109/ICBME57741.2022.10052964
Mohammad Erfan Hamdi, Rasool Dezhkam, Arman Hajizade, A. Shamloo
Aqueous solubility prediction of drug molecules is essential in drug design pipelines. Due to the availability of a vast amount of high-quality data, deep learning based methods of molecular property prediction methods are gaining more and more attention every day and have achieved outstanding results. Graph Neural Networks is one of the most successful classes in deep learning for this specific task, which can be because of the graph-like nature of molecules. In this paper, we proposed to use a new GNN model called ALIGNN, which has achieved the state of the art performance on QM9 dataset tasks by introducing the line graph concept. Training ALIGNN on the Delaney dataset, we have achieved an RMSE of 0.511.
{"title":"Prediction of Aqueous Solubility of Drug Molecules by Embedding Spatial Conformers Using Graph Neural Networks","authors":"Mohammad Erfan Hamdi, Rasool Dezhkam, Arman Hajizade, A. Shamloo","doi":"10.1109/ICBME57741.2022.10052964","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10052964","url":null,"abstract":"Aqueous solubility prediction of drug molecules is essential in drug design pipelines. Due to the availability of a vast amount of high-quality data, deep learning based methods of molecular property prediction methods are gaining more and more attention every day and have achieved outstanding results. Graph Neural Networks is one of the most successful classes in deep learning for this specific task, which can be because of the graph-like nature of molecules. In this paper, we proposed to use a new GNN model called ALIGNN, which has achieved the state of the art performance on QM9 dataset tasks by introducing the line graph concept. Training ALIGNN on the Delaney dataset, we have achieved an RMSE of 0.511.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115111476","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 : 2022-12-21DOI: 10.1109/ICBME57741.2022.10052890
Zahra Shamsipour Azbari, Mohadese Rajaei Rad, Amirreza Nahvinejad, H. A. Gilakjani, M. Khorsandi
The hip joint is one of the largest joints of the body, which plays an important role in bearing the body's weight. When arthritis progresses to its higher levels, arthroplasty is performed to reduce pain and increase joint range of motion. This surgery is a common treatment worldwide, the quality of which depends on several factors. One of the critical factors is the geometry of the stem, which is implanted into the femur bone and receives support from it. For this aim, in this study, the geometry of three available stems in the orthopedic devices market was investigated by the finite element method (FEM). An accurate model of the proximal part of the femur was generated, and after modeling each stem, the final model was assembled. After developing the FE model, three loading conditions were applied to the stems. Maximum values of stress were observed in the middle part and neck of the stem. Moreover, it was observed that thickness is a key parameter in addition to the offset and neck shaft angle parameters and their effect on the amount and distribution of stress in the implant and bone. The results of this study can help improve the design of hip joint stems.
{"title":"Biomechanical Analysis of Hip Replacement Stem Design: A Finite Element Analysis","authors":"Zahra Shamsipour Azbari, Mohadese Rajaei Rad, Amirreza Nahvinejad, H. A. Gilakjani, M. Khorsandi","doi":"10.1109/ICBME57741.2022.10052890","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10052890","url":null,"abstract":"The hip joint is one of the largest joints of the body, which plays an important role in bearing the body's weight. When arthritis progresses to its higher levels, arthroplasty is performed to reduce pain and increase joint range of motion. This surgery is a common treatment worldwide, the quality of which depends on several factors. One of the critical factors is the geometry of the stem, which is implanted into the femur bone and receives support from it. For this aim, in this study, the geometry of three available stems in the orthopedic devices market was investigated by the finite element method (FEM). An accurate model of the proximal part of the femur was generated, and after modeling each stem, the final model was assembled. After developing the FE model, three loading conditions were applied to the stems. Maximum values of stress were observed in the middle part and neck of the stem. Moreover, it was observed that thickness is a key parameter in addition to the offset and neck shaft angle parameters and their effect on the amount and distribution of stress in the implant and bone. The results of this study can help improve the design of hip joint stems.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131395948","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：