Pub Date : 2022-07-04Print Date: 2022-10-26DOI: 10.1515/bmt-2021-0307
Micha Bischofberger, Stephan Böhringer, Erik Schkommodau
This paper proposes a conceptual method to calculate the pose of a stereo-vision camera relative to an artificial mandible without additional markers. The general method for marker-free navigation has four steps: 1) parallel image acquisition by a stereo-vision camera, 2) automatic identification of 2d point pairs (landmark pairs) in a left and a right image, 3) calculation of related 3d points in the joint camera coordinate system and 4) matching of 3d points generated to a preoperative 3d model (i.e., CT data based). To identify and compare landmarks in the acquired stereo images, well-known algorithms for landmark detection, description and matching were compared within the developed approach. Finally, the BRISK algorithm (Leutenegger S, Chli M, Siegwart RY. BRISK: Binary Robust invariant scalable keypoints. Proceedings of the IEEE International Conference on Computer Vision; 2011: 2548-2555) was used. The proposed method was implemented in MATLAB® and validated in vitro with one artificial mandible. The accuracy evaluation of the camera positions calculated resulted in an average deviation error of 1.45 mm ± 0.76 mm to the real camera displacement. This value was calculated using only stereo images with over 100 reconstructed landmark pairs each. This provides the basis for marker-free navigation.
本文提出了一种不需要附加标记来计算立体视觉相机相对于人工下颌骨的姿态的概念方法。无标记导航的一般方法有四个步骤:1)由立体视觉相机获取平行图像，2)自动识别左右图像中的二维点对(地标对)，3)在联合相机坐标系中计算相关的三维点，4)将生成的三维点与术前三维模型(即基于CT数据)进行匹配。为了识别和比较获得的立体图像中的地标，在开发的方法中比较了已知的地标检测，描述和匹配算法。Leutenegger S, Chli M, Siegwart RY. BRISK:二值鲁棒不变可伸缩关键点。IEEE计算机视觉国际会议论文集;2011: 2548-2555)。在MATLAB®中实现了该方法，并在一个人工下颌骨上进行了体外验证。计算得到的摄像机位置精度评价结果与摄像机实际位移的平均偏差为1.45 mm±0.76 mm。该值仅使用具有100多个重建地标对的立体图像来计算。这为无标记导航提供了基础。
{"title":"Automatic landmark identification for surgical 3d-navigation - A proposed method for marker-free dental surgical navigation systems.","authors":"Micha Bischofberger, Stephan Böhringer, Erik Schkommodau","doi":"10.1515/bmt-2021-0307","DOIUrl":"https://doi.org/10.1515/bmt-2021-0307","url":null,"abstract":"<p><p>This paper proposes a conceptual method to calculate the pose of a stereo-vision camera relative to an artificial mandible without additional markers. The general method for marker-free navigation has four steps: 1) parallel image acquisition by a stereo-vision camera, 2) automatic identification of 2d point pairs (landmark pairs) in a left and a right image, 3) calculation of related 3d points in the joint camera coordinate system and 4) matching of 3d points generated to a preoperative 3d model (i.e., CT data based). To identify and compare landmarks in the acquired stereo images, well-known algorithms for landmark detection, description and matching were compared within the developed approach. Finally, the BRISK algorithm (Leutenegger S, Chli M, Siegwart RY. BRISK: Binary Robust invariant scalable keypoints. Proceedings of the IEEE International Conference on Computer Vision; 2011: 2548-2555) was used. The proposed method was implemented in MATLAB<sup>®</sup> and validated <i>in vitro</i> with one artificial mandible. The accuracy evaluation of the camera positions calculated resulted in an average deviation error of 1.45 mm ± 0.76 mm to the real camera displacement. This value was calculated using only stereo images with over 100 reconstructed landmark pairs each. This provides the basis for marker-free navigation.</p>","PeriodicalId":8900,"journal":{"name":"Biomedical Engineering / Biomedizinische Technik","volume":"67 5","pages":"411-417"},"PeriodicalIF":1.7,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40479492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-28Print Date: 2022-10-26DOI: 10.1515/bmt-2021-0418
Li Ji, Chen Zhang, Haiwei Li, Ningning Zhang, Peng Zheng, Changhao Guo, Yong Zhang, Xiaoyu Tang
The take-off and landing phases are considered the critical stages of aircraft flight. To ensure flight efficiency and safety in the critical stages, this research proposes a method for analyzing and monitoring pilot flight status by β-wave. The focus of the study is β potential changes on the EEG map. First, the proportion of β-wave in the electroencephalogram (EEG) of pilots during take-off and landing increases significantly. Second, the EEG map accurately and intuitively reflects the spatial distribution of potential changes in brain regions. Finally, correlation and machine learning are used for further research of β-wave. The conclusions show that the significant changes in the β-wave caused by take-off and landing can be seen in the EEG map to identify and adjust the pilot's state. Therefore, this research provides more accurate and effective reference information (like the EEG map, correlation and machine learning) for efficient and safe flight training in the critical stages.
{"title":"Analysis of pilots' EEG map in take-off and landing tasks.","authors":"Li Ji, Chen Zhang, Haiwei Li, Ningning Zhang, Peng Zheng, Changhao Guo, Yong Zhang, Xiaoyu Tang","doi":"10.1515/bmt-2021-0418","DOIUrl":"https://doi.org/10.1515/bmt-2021-0418","url":null,"abstract":"<p><p>The take-off and landing phases are considered the critical stages of aircraft flight. To ensure flight efficiency and safety in the critical stages, this research proposes a method for analyzing and monitoring pilot flight status by β-wave. The focus of the study is β potential changes on the EEG map. First, the proportion of β-wave in the electroencephalogram (EEG) of pilots during take-off and landing increases significantly. Second, the EEG map accurately and intuitively reflects the spatial distribution of potential changes in brain regions. Finally, correlation and machine learning are used for further research of β-wave. The conclusions show that the significant changes in the β-wave caused by take-off and landing can be seen in the EEG map to identify and adjust the pilot's state. Therefore, this research provides more accurate and effective reference information (like the EEG map, correlation and machine learning) for efficient and safe flight training in the critical stages.</p>","PeriodicalId":8900,"journal":{"name":"Biomedical Engineering / Biomedizinische Technik","volume":"67 5","pages":"345-356"},"PeriodicalIF":1.7,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40408695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Supervised automatic sleep scoring algorithms are usually trained using sleep stage labels manually annotated on 30 s epochs of PSG data. In this study, we investigate the impact of using shorter epochs with various PSG input signals for training and testing a Long Short Term Memory (LSTM) neural network. An LSTM model is evaluated on the provided 30 s epoch sleep stage labels from a publicly available dataset, as well as on 10 s subdivisions. Additionally, three independent scorers re-labeled a subset of the dataset on shorter time windows. The automatic sleep scoring experiments were repeated on the re-annotated subset.The highest performance is achieved on features extracted from 30 s epochs of a single channel frontal EEG. The resulting accuracy, precision and recall were of 92.22%, 67.58% and 66.00% respectively. When using a shorter epoch as input, the performance decreased by approximately 20%. Re-annotating a subset of the dataset on shorter time epochs did not improve the results and further altered the sleep stage detection performance. Our results show that our feature-based LSTM classification algorithm performs better on 30 s PSG epochs when compared to 10 s epochs used as input. Future work could be oriented to determining whether varying the epoch size improves classification outcomes for different types of classification algorithms.
有监督的自动睡眠评分算法通常使用人工标注睡眠阶段标签的PSG数据进行训练。在这项研究中，我们研究了使用不同PSG输入信号的较短时间对长短期记忆(LSTM)神经网络的训练和测试的影响。LSTM模型在公共数据集提供的30秒epoch睡眠阶段标签以及10秒细分上进行评估。此外，三个独立的评分者在较短的时间窗口上重新标记数据集的子集。在重新标注的子集上重复自动睡眠评分实验。在单通道额叶脑电图的30 s epoch特征提取上，获得了最高的性能。准确度、精密度和召回率分别为92.22%、67.58%和66.00%。当使用较短的epoch作为输入时，性能下降了大约20%。在较短的时间点上重新标注数据集的子集并没有改善结果，反而进一步改变了睡眠阶段检测性能。结果表明，基于特征的LSTM分类算法在30秒的PSG epoch上的表现优于10秒的PSG epoch。未来的工作可能是确定不同epoch大小是否会改善不同类型分类算法的分类结果。
{"title":"Automatic sleep scoring with LSTM networks: impact of time granularity and input signals","authors":"Alexandra-Maria Tăuțan, A. C. Rossi, B. Ionescu","doi":"10.1515/bmt-2021-0408","DOIUrl":"https://doi.org/10.1515/bmt-2021-0408","url":null,"abstract":"Abstract Supervised automatic sleep scoring algorithms are usually trained using sleep stage labels manually annotated on 30 s epochs of PSG data. In this study, we investigate the impact of using shorter epochs with various PSG input signals for training and testing a Long Short Term Memory (LSTM) neural network. An LSTM model is evaluated on the provided 30 s epoch sleep stage labels from a publicly available dataset, as well as on 10 s subdivisions. Additionally, three independent scorers re-labeled a subset of the dataset on shorter time windows. The automatic sleep scoring experiments were repeated on the re-annotated subset.The highest performance is achieved on features extracted from 30 s epochs of a single channel frontal EEG. The resulting accuracy, precision and recall were of 92.22%, 67.58% and 66.00% respectively. When using a shorter epoch as input, the performance decreased by approximately 20%. Re-annotating a subset of the dataset on shorter time epochs did not improve the results and further altered the sleep stage detection performance. Our results show that our feature-based LSTM classification algorithm performs better on 30 s PSG epochs when compared to 10 s epochs used as input. Future work could be oriented to determining whether varying the epoch size improves classification outcomes for different types of classification algorithms.","PeriodicalId":8900,"journal":{"name":"Biomedical Engineering / Biomedizinische Technik","volume":"53 1","pages":"267 - 281"},"PeriodicalIF":1.7,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87029110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Parkinson’s disease (PD), a slow-progressing neurological disease, affects a large percentage of the world’s elderly population, and this population is expected to grow over the next decade. As a result, early detection is crucial for community health and the future of the globe in order to take proper safeguards and have a less arduous treatment procedure. Recent research has begun to focus on the motor system deficits caused by PD. Because practically most of the PD patients suffer from voice abnormalities, researchers working on automated diagnostic systems investigate vocal impairments. In this paper, we undertake extensive experiments with features extracted from voice signals. We propose a layer Recurrent Neural Network (RNN) based diagnosis for PD. To prove the efficiency of the model, different network models are compared. To the best of our knowledge, several neural network topologies, namely RNN, Cascade Forward Neural Networks (CFNN), and Feed Forward Neural Networks (FFNN), are used and compared for voice-based PD detection for the first time. In addition, the impacts of data normalization and feature selection (FS) are thoroughly examined. The findings reveal that normalization increases classifier performance and Laplacian-based FS outperforms. The proposed RNN model with 300 voice features achieves 99.74% accuracy.
{"title":"Layer recurrent neural network-based diagnosis of Parkinson’s disease using voice features","authors":"Z. Senturk","doi":"10.1515/bmt-2022-0022","DOIUrl":"https://doi.org/10.1515/bmt-2022-0022","url":null,"abstract":"Abstract Parkinson’s disease (PD), a slow-progressing neurological disease, affects a large percentage of the world’s elderly population, and this population is expected to grow over the next decade. As a result, early detection is crucial for community health and the future of the globe in order to take proper safeguards and have a less arduous treatment procedure. Recent research has begun to focus on the motor system deficits caused by PD. Because practically most of the PD patients suffer from voice abnormalities, researchers working on automated diagnostic systems investigate vocal impairments. In this paper, we undertake extensive experiments with features extracted from voice signals. We propose a layer Recurrent Neural Network (RNN) based diagnosis for PD. To prove the efficiency of the model, different network models are compared. To the best of our knowledge, several neural network topologies, namely RNN, Cascade Forward Neural Networks (CFNN), and Feed Forward Neural Networks (FFNN), are used and compared for voice-based PD detection for the first time. In addition, the impacts of data normalization and feature selection (FS) are thoroughly examined. The findings reveal that normalization increases classifier performance and Laplacian-based FS outperforms. The proposed RNN model with 300 voice features achieves 99.74% accuracy.","PeriodicalId":8900,"journal":{"name":"Biomedical Engineering / Biomedizinische Technik","volume":"12 1","pages":"249 - 266"},"PeriodicalIF":1.7,"publicationDate":"2022-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75749149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Although fetal phonocardiogram (fPCG) signals have become a good indicator for discovered heart disease, they may be contaminated by various noises that reduce the signals quality and the final diagnosis decision. Moreover, the noise may cause the risk of the data to misunderstand the heart signal and to misinterpret it. The main objective of this paper is to effectively remove noise from the fPCG signal to make it clinically feasible. So, we proposed a novel noise reduction method based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), wavelet threshold and Crow Search Algorithm (CSA). This noise reduction method, named ICEEMDAN-DWT-CSA, has three major advantages. They were, (i) A better suppress of mode mixing and a minimized number of IMFs, (ii) A choice of wavelet corresponding to the study signal proven by the literature and (iii) Selection of the optimal threshold value. Firstly, the noisy fPCG signal is decomposed into Intrinsic Mode Functions (IMFs) by the (ICEEMDAN). Each noisy IMFs were decomposed by the Discrete Wavelet Transform (DWT). Then, the optimal threshold value using the (CSA) technique is selected and the thresholding function is carried out in the detail’s coefficients. Secondly, each denoised (IMFs) is reconstructed by applying the Inverse Discrete Wavelet Transform (IDWT). Finally, all these denoised (IMFs) are combined to get the denoised fPCG signal. The performance of the proposed method has been evaluated by Signal to Noise Ratio (SNR), Mean Square Error (MSE) and the Correlation Coefficient (COR). The experiment gave a better result than some standard methods.
{"title":"Fetal phonocardiogram signals denoising using improved complete ensemble (EMD) with adaptive noise and optimal thresholding of wavelet coefficients","authors":"Fethi Cheikh, Nasser Edinne Benhassine, S. Sbaa","doi":"10.1515/bmt-2022-0006","DOIUrl":"https://doi.org/10.1515/bmt-2022-0006","url":null,"abstract":"Abstract Although fetal phonocardiogram (fPCG) signals have become a good indicator for discovered heart disease, they may be contaminated by various noises that reduce the signals quality and the final diagnosis decision. Moreover, the noise may cause the risk of the data to misunderstand the heart signal and to misinterpret it. The main objective of this paper is to effectively remove noise from the fPCG signal to make it clinically feasible. So, we proposed a novel noise reduction method based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), wavelet threshold and Crow Search Algorithm (CSA). This noise reduction method, named ICEEMDAN-DWT-CSA, has three major advantages. They were, (i) A better suppress of mode mixing and a minimized number of IMFs, (ii) A choice of wavelet corresponding to the study signal proven by the literature and (iii) Selection of the optimal threshold value. Firstly, the noisy fPCG signal is decomposed into Intrinsic Mode Functions (IMFs) by the (ICEEMDAN). Each noisy IMFs were decomposed by the Discrete Wavelet Transform (DWT). Then, the optimal threshold value using the (CSA) technique is selected and the thresholding function is carried out in the detail’s coefficients. Secondly, each denoised (IMFs) is reconstructed by applying the Inverse Discrete Wavelet Transform (IDWT). Finally, all these denoised (IMFs) are combined to get the denoised fPCG signal. The performance of the proposed method has been evaluated by Signal to Noise Ratio (SNR), Mean Square Error (MSE) and the Correlation Coefficient (COR). The experiment gave a better result than some standard methods.","PeriodicalId":8900,"journal":{"name":"Biomedical Engineering / Biomedizinische Technik","volume":"353 1","pages":"237 - 247"},"PeriodicalIF":1.7,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80079336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Non-woven textile has been largely used as medical implant material over the last decades, especially for scaffold manufacturing purpose. This material presents a large surface area-to-volume ratio, which promotes adequate interaction with biological tissues. However, its strength is limited due to the lack of cohesion between the fibers. The goal of the present work was to investigate if a non-woven substrate can be reinforced by embroidery stitching towards strength increase. Non-woven samples were produced from both melt-blowing and electro-spinning techniques, reinforced with a stitching yarn and tested regarding several performances: ultimate tensile strength, burst strength and strength loss after fatigue stress. Several stitching parameters were considered: distance between stitches, number of stitch lines (1, 2 or 3) and line geometry (horizontal H, vertical L, cross X). The performance values obtained after reinforcement were compared with values obtained for control samples. Results bring out that reinforcement can increase the strength by up to 50% for a melt-blown mat and by up to 100% for an electro-spun mat with an X reinforcement pattern. However, after cyclic loading, the reinforcement yarn tends to degrade the ES mat in particular. Moreover, increasing the number of stitches tends to fragilize the mats.
{"title":"Non-woven textiles for medical implants: mechanical performances improvement","authors":"Amandine Lequeux, B. Mazé, G. Laroche, F. Heim","doi":"10.1515/bmt-2022-0017","DOIUrl":"https://doi.org/10.1515/bmt-2022-0017","url":null,"abstract":"Abstract Non-woven textile has been largely used as medical implant material over the last decades, especially for scaffold manufacturing purpose. This material presents a large surface area-to-volume ratio, which promotes adequate interaction with biological tissues. However, its strength is limited due to the lack of cohesion between the fibers. The goal of the present work was to investigate if a non-woven substrate can be reinforced by embroidery stitching towards strength increase. Non-woven samples were produced from both melt-blowing and electro-spinning techniques, reinforced with a stitching yarn and tested regarding several performances: ultimate tensile strength, burst strength and strength loss after fatigue stress. Several stitching parameters were considered: distance between stitches, number of stitch lines (1, 2 or 3) and line geometry (horizontal H, vertical L, cross X). The performance values obtained after reinforcement were compared with values obtained for control samples. Results bring out that reinforcement can increase the strength by up to 50% for a melt-blown mat and by up to 100% for an electro-spun mat with an X reinforcement pattern. However, after cyclic loading, the reinforcement yarn tends to degrade the ES mat in particular. Moreover, increasing the number of stitches tends to fragilize the mats.","PeriodicalId":8900,"journal":{"name":"Biomedical Engineering / Biomedizinische Technik","volume":"68 1","pages":"317 - 330"},"PeriodicalIF":1.7,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89365592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ayhan Gürbüz, Z. Güçlü, Gonca Deste Gökay, R. Durkan
Abstract The study aimed to evaluate the biomechanical behaviors of different prosthetic materials and posterior implant angles in All-on-4 implant-supported fixed maxillary prostheses with three-dimensional (3D) finite element analysis. The model of complete edentulous maxilla was created using the Rhinoceros and VRMesh Studio programs. Anterior vertical and 17°- and 30°-angled posterior implants were positioned with All-on-4 design. Straigth and angled multi-unit abutments scanned using a 3D scanner. Two different prosthetic superstructures (monolithic zirconia framework and lithium disilicate veneer (ZL) and monolithic zirconia-reinforced lithium silicate (ZLS)) were modeled. Four models designed according to the prosthetic structure and posterior implant angles. Posterior vertical bilateral loading and frontal oblique loading was performed. The principal stresses (bone tissues-Pmax and Pmin) and von Mises equivalent stresses (implant and prosthetic structures) were analyzed. In all models, the highest Pmax stress values were calculated under posterior bilateral loading in cortical bone. The highest von Mises stress levels occured in the posterior implants under posterior bilateral load (260.33 and 219.50 MPa) in the ZL-17 and ZL-30 models, respectively. Under both loads, higher stress levels in prosthetic structures were shown in the ZLS models compared with ZL models. There was no difference between posterior implant angles on stress distribution occurred in implant material and alveolar bone tissue. ZLS and ZL prosthetic structures can be reliably used in maxillary All-on-4 rehabilitation.
{"title":"Biomechanical comparison of different prosthetic materials and posterior implant angles in all-on-4 treatment concept by three-dimensional finite element analysis","authors":"Ayhan Gürbüz, Z. Güçlü, Gonca Deste Gökay, R. Durkan","doi":"10.1515/bmt-2022-0109","DOIUrl":"https://doi.org/10.1515/bmt-2022-0109","url":null,"abstract":"Abstract The study aimed to evaluate the biomechanical behaviors of different prosthetic materials and posterior implant angles in All-on-4 implant-supported fixed maxillary prostheses with three-dimensional (3D) finite element analysis. The model of complete edentulous maxilla was created using the Rhinoceros and VRMesh Studio programs. Anterior vertical and 17°- and 30°-angled posterior implants were positioned with All-on-4 design. Straigth and angled multi-unit abutments scanned using a 3D scanner. Two different prosthetic superstructures (monolithic zirconia framework and lithium disilicate veneer (ZL) and monolithic zirconia-reinforced lithium silicate (ZLS)) were modeled. Four models designed according to the prosthetic structure and posterior implant angles. Posterior vertical bilateral loading and frontal oblique loading was performed. The principal stresses (bone tissues-Pmax and Pmin) and von Mises equivalent stresses (implant and prosthetic structures) were analyzed. In all models, the highest Pmax stress values were calculated under posterior bilateral loading in cortical bone. The highest von Mises stress levels occured in the posterior implants under posterior bilateral load (260.33 and 219.50 MPa) in the ZL-17 and ZL-30 models, respectively. Under both loads, higher stress levels in prosthetic structures were shown in the ZLS models compared with ZL models. There was no difference between posterior implant angles on stress distribution occurred in implant material and alveolar bone tissue. ZLS and ZL prosthetic structures can be reliably used in maxillary All-on-4 rehabilitation.","PeriodicalId":8900,"journal":{"name":"Biomedical Engineering / Biomedizinische Technik","volume":"1 1","pages":"307 - 315"},"PeriodicalIF":1.7,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84007664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The incidence of vision impairment is rapidly increasing. Diagnosis and classifying retinal abnormalities in ophthalmological applications is a significant challenge. Using Optical Coherence Tomography (OCT), the study aims to develop a computer aided diagnosis system for detecting and classifying retinal disorders. Choroidal neovascularization, diabetic macular edema, drusen, and normal cases are the investigated groups. Both deep learning and machine learning are combined to build the system. The SqueezeNet neural network was modified to extract features. The Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Decision Tree (DT), and Ensemble Model (EM) algorithms were used for disorder classification. The Bayesian optimization technique was also used to determine the best hyperparameters for each model. The model’ performance was evaluated through nine criteria using 12,000 OCT images. The results have demonstrated accuracies of 97.39, 97.47, 96.98, and 95.25% for the SVM, K-NN, DT, and EM, respectively. When results are compared to relevant studies in terms of accuracy and tested samples, they show superior performance. As a result, a novel computer-aided diagnosis system for detecting and classifying retinal diseases has been developed, reducing human error while also saving time.
{"title":"Computer-aided diagnosis system for retinal disorder classification using optical coherence tomography images","authors":"N. Saleh, Manal Abdel Wahed, A. M. Salaheldin","doi":"10.1515/bmt-2021-0330","DOIUrl":"https://doi.org/10.1515/bmt-2021-0330","url":null,"abstract":"Abstract The incidence of vision impairment is rapidly increasing. Diagnosis and classifying retinal abnormalities in ophthalmological applications is a significant challenge. Using Optical Coherence Tomography (OCT), the study aims to develop a computer aided diagnosis system for detecting and classifying retinal disorders. Choroidal neovascularization, diabetic macular edema, drusen, and normal cases are the investigated groups. Both deep learning and machine learning are combined to build the system. The SqueezeNet neural network was modified to extract features. The Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Decision Tree (DT), and Ensemble Model (EM) algorithms were used for disorder classification. The Bayesian optimization technique was also used to determine the best hyperparameters for each model. The model’ performance was evaluated through nine criteria using 12,000 OCT images. The results have demonstrated accuracies of 97.39, 97.47, 96.98, and 95.25% for the SVM, K-NN, DT, and EM, respectively. When results are compared to relevant studies in terms of accuracy and tested samples, they show superior performance. As a result, a novel computer-aided diagnosis system for detecting and classifying retinal diseases has been developed, reducing human error while also saving time.","PeriodicalId":8900,"journal":{"name":"Biomedical Engineering / Biomedizinische Technik","volume":"27 1","pages":"283 - 294"},"PeriodicalIF":1.7,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80097886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Philipp Jungk, Matthias Wienke, Christoph Schiefer, U. Hartmann, Volker Harth, C. Terschüren, Carsten Alteköster, D. Friemert
Abstract The functionality of smart glasses includes the possibility of wireless communication. For this purpose, WiFi or Bluetooth modules are integrated into the glasses. They emit electromagnetic radiation in the vicinity of the user’s head. This simulation study investigates the impact of varying positions, frequencies, and antenna types of the embedded WiFi or Bluetooth modules on different tissue types in the human head. The absorption of electromagnetic energy causes the main impact on the tissue in the head. This physical process is best described by the specific absorption rate SAR. To investigate the effects of position, frequency, and antenna type on the simulated SAR values multiple simulations have been carried out considering real-world applications of smart glasses. The results show that the type of antenna has little effect on the SAR values of the different tissues. The maximum regulated output powers explain the frequencies’ impact on the exposure. According to our findings, the greatest influence on the SAR values can be attributed to the placement of the antenna. Finally, our study reveals that positioning the antenna at the front side of the head is optimal for most tissues because of its maximal distance to the head tissues.
{"title":"Investigation of the impact of electromagnetic fields emitted close to the head by smart glasses","authors":"Philipp Jungk, Matthias Wienke, Christoph Schiefer, U. Hartmann, Volker Harth, C. Terschüren, Carsten Alteköster, D. Friemert","doi":"10.1515/bmt-2021-0301","DOIUrl":"https://doi.org/10.1515/bmt-2021-0301","url":null,"abstract":"Abstract The functionality of smart glasses includes the possibility of wireless communication. For this purpose, WiFi or Bluetooth modules are integrated into the glasses. They emit electromagnetic radiation in the vicinity of the user’s head. This simulation study investigates the impact of varying positions, frequencies, and antenna types of the embedded WiFi or Bluetooth modules on different tissue types in the human head. The absorption of electromagnetic energy causes the main impact on the tissue in the head. This physical process is best described by the specific absorption rate SAR. To investigate the effects of position, frequency, and antenna type on the simulated SAR values multiple simulations have been carried out considering real-world applications of smart glasses. The results show that the type of antenna has little effect on the SAR values of the different tissues. The maximum regulated output powers explain the frequencies’ impact on the exposure. According to our findings, the greatest influence on the SAR values can be attributed to the placement of the antenna. Finally, our study reveals that positioning the antenna at the front side of the head is optimal for most tissues because of its maximal distance to the head tissues.","PeriodicalId":8900,"journal":{"name":"Biomedical Engineering / Biomedizinische Technik","volume":"151 1","pages":"219 - 226"},"PeriodicalIF":1.7,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77239888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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