Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference最新文献_第9页

Proxy-based Masking Module for Revealing Relevance of Characteristics in Motor Imagery. 基于代理的掩蔽模块揭示运动意象中相关特征。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782698

Hyeon-Taek Han, Sung-Jin Kim, Dae-Hyeok Lee, Seong-Whan Lee

{"title":"Proxy-based Masking Module for Revealing Relevance of Characteristics in Motor Imagery.","authors":"Hyeon-Taek Han, Sung-Jin Kim, Dae-Hyeok Lee, Seong-Whan Lee","doi":"10.1109/EMBC53108.2024.10782698","DOIUrl":"10.1109/EMBC53108.2024.10782698","url":null,"abstract":"Brain-computer interface (BCI) has been developed for communication between users and external devices by reflecting users' status and intentions. Motor imagery (MI) is one of the BCI paradigms for controlling external devices by imagining muscle movements. MI-based EEG signals generally tend to contain signals with sparse MI characteristics (sparse MI signals). When conducting domain adaptation (DA) on MI signals with sparse MI signals, it could interrupt the training process. In this paper, we proposed the proxy-based masking module (PMM) for masking sparse MI signals within MI signals. The proposed module was designed to suppress the amplitude of sparse MI signals using the negative similarity-based mask generated between the proxy of rest signals and the feature vectors of MI signals. We attached our proposed module to the conventional DA methods (i.e., the DJDAN, the MAAN, and the DRDA) to verify the effectiveness in the cross-subject environment on dataset 2a of BCI competition IV. When our proposed module was attached to each conventional DA method, the average accuracy was improved by much as 4.67 %, 0.76 %, and 1.72 %, respectively. Hence, we demonstrated that our proposed module could emphasize the information related to MI characteristics. The code of our implementation is accessible on GitHub.1.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559899","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}

引用次数: 0

3D-Printed Scaffold Mimicking IBD Gut Microenvironments: An In Vitro Model for Bacterial Bioink Growth. 3d打印支架模拟IBD肠道微环境：细菌生物链接生长的体外模型。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782731

Alejandro Garcia-Gonzalez, Mariana Jaquez-Sanchez, Axel Maya-Morales, Mariana S Flores-Jimenez, Yocanxochitl Perfecto-Avalos, Isaac Chairez-Oria, Abel Gutierrez-Vilchis, Ricardo Garcia-Gamboa

{"title":"3D-Printed Scaffold Mimicking IBD Gut Microenvironments: An In Vitro Model for Bacterial Bioink Growth.","authors":"Alejandro Garcia-Gonzalez, Mariana Jaquez-Sanchez, Axel Maya-Morales, Mariana S Flores-Jimenez, Yocanxochitl Perfecto-Avalos, Isaac Chairez-Oria, Abel Gutierrez-Vilchis, Ricardo Garcia-Gamboa","doi":"10.1109/EMBC53108.2024.10782731","DOIUrl":"10.1109/EMBC53108.2024.10782731","url":null,"abstract":"Inflammatory bowel disease (IBD), a chronic inflammatory condition of the gastrointestinal tract, affects millions worldwide and is linked to altered gut microbiota. This study explored the feasibility of a 3D-bioprinting scaffold containing Lactococcus lactis using an alginate-agar-soy trypticase bioink. The bioink exhibited high water absorption and adequate rheology, enabling successful bioprinting of scaffolds with robust structures. The scaffolds remained stable for 24 hours, allowing prolonged bacterial growth. L. lactis viability was confirmed by confocal microscopy, which revealed green fluorescence indicative of live bacteria even after 8 hours of culture within the scaffold. This suggests a supportive microenvironment for bacterial survival and potential proliferation. Compared to a 2D model, the 3D scaffold increased the number of colony-forming units (CFUs), indicating a more supportive environment for L. lactis growth. Overall, this study emphasizes the potential of 3D-printed bacterial scaffolds as a platform culture to assess the factors influencing the microbiota in various diseases.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559003","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}

引用次数: 0

Assessment of Force Feedback Models in a Haptic Device Using Alignment Accuracy and Brain Activity. 利用对准精度和大脑活动评估触觉装置中的力反馈模型。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781934

Harutake Nagai, Satoshi Miura

{"title":"Assessment of Force Feedback Models in a Haptic Device Using Alignment Accuracy and Brain Activity.","authors":"Harutake Nagai, Satoshi Miura","doi":"10.1109/EMBC53108.2024.10781934","DOIUrl":"10.1109/EMBC53108.2024.10781934","url":null,"abstract":"When controlling a robot's velocity, it is necessary to provide force feedback to the user, which shows the amount of input from the neutral position. In this paper, we introduce three distinct force feedback models, each with one or two parameters, and investigate the influence on the operability of the robot and brain activity according to the changes of the force feedback model using a haptic device. Participants in the alignment task performed tasks in which they aligned an object with a target position in virtual space using our developed interface while we measured the participant's operational performance and brain activation using functional near-infrared spectroscopy. We performed fitting using quadratic functions with the parameters of each model as design variables. The results of the alignment task demonstrated that two models achieved higher alignment performance depending on the position of the target from the neutral position and, for one model, brain activation changed significantly as the parameters changed.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559080","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}

引用次数: 0

Deep Learning Method for Estimating Germ-layer Regions of Early Differentiated Human Induced Pluripotent Stem Cells on Micropattern Using Bright-field Microscopy Image. 基于明场显微图像的深度学习方法估计早期分化人诱导多能干细胞胚层区域

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782655

Slo-Li Chu, Hideo Yokota, Pai-Ting Wang, Kuniya Abe, Yohei Hayashi, Dooseon Cho, Ming-Dar Tsai

{"title":"Deep Learning Method for Estimating Germ-layer Regions of Early Differentiated Human Induced Pluripotent Stem Cells on Micropattern Using Bright-field Microscopy Image.","authors":"Slo-Li Chu, Hideo Yokota, Pai-Ting Wang, Kuniya Abe, Yohei Hayashi, Dooseon Cho, Ming-Dar Tsai","doi":"10.1109/EMBC53108.2024.10782655","DOIUrl":"10.1109/EMBC53108.2024.10782655","url":null,"abstract":"Live cell staining is expensive and may bring potential safety issues in downstream clinical applications, bright-field images rather than staining images should be more suitable to reveal time-series changes of differentiating hiPSCs (human induced pluripotent stem cells) and three-germ layers differentiated from the hiPSCs. This study proposed a deep learning method for estimating immunofluorescence regions on a bright-field microscopy images. The networks trained by multiple types of fluorescence images can estimate the types of fluorescence images from a bright-field image. The estimated pseudo Hoechst image is used to segment hiPSCs, and the others classify the segmented hiPSCs as respective germ-layer cells. The experimental results show over 75% correct rates for the segmentation and classification were achieved, indicating the proposed method can be useful tool in evaluating pluripotency of hiPSC and delineating the germ layer formation process without cell staining.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559243","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}

引用次数: 0

Continuous Optimization of a Hierarchical Bayesian Network for Friedreich's Ataxia Severity Classification. 层次贝叶斯网络对friedrich共济失调严重程度分类的连续优化。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781628

Sahan Dissanayake, Ragil Krishna, Pubudu N Pathirana, Malcolm K Horne, David J Smulewicz, Louise A Corben

{"title":"Continuous Optimization of a Hierarchical Bayesian Network for Friedreich's Ataxia Severity Classification.","authors":"Sahan Dissanayake, Ragil Krishna, Pubudu N Pathirana, Malcolm K Horne, David J Smulewicz, Louise A Corben","doi":"10.1109/EMBC53108.2024.10781628","DOIUrl":"10.1109/EMBC53108.2024.10781628","url":null,"abstract":"Machine learning algorithms for rare disorders, such as Friedreich's Ataxia (FRDA), often suffer from a lack of data. Therefore, the ability for continuous optimization of an objective assessment model would be very useful as a clinical decision support system. In this study, we propose a Bayesian Network(BN) system for FRDA severity estimation that incorporates a Bayesian Statistical updating system to continuously improve the predictive ability while providing an easily interpretable graphical model. This can work to improve the understanding of the model by the clinician, thus creating trust in the machine learning process. Furthermore, we demonstrate that by using the updating mechanism, the BN model gives a goodness-of-fit score of 0.95, a root mean square error of 9.35 and a mean absolute error of 6.72, which outperforms other regression approaches as well as improves upon the base BN by 2% in goodness of fit, roughly 1% in RMSE and 6% in MAE.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143557738","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}

引用次数: 0

Improving Bioimpedance-based Tissue Identification with Frequency Response Similarity Metrics. 基于频率响应相似度量改进生物阻抗的组织识别。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782337

Jacob Search, Sabino Zani, Brian P Mann

{"title":"Improving Bioimpedance-based Tissue Identification with Frequency Response Similarity Metrics.","authors":"Jacob Search, Sabino Zani, Brian P Mann","doi":"10.1109/EMBC53108.2024.10782337","DOIUrl":"10.1109/EMBC53108.2024.10782337","url":null,"abstract":"Tissue identification is essential for surgeons to properly perform procedures and make informed decisions to minimize potential harm to patients. Minimally invasive surgery (MIS) offers enhanced patient safety and outcomes at the cost of lost information due to restricted vision and loss of touch, among other factors. This makes it more difficult to quickly and consistently identify tissues correctly. Bioimpedance spectroscopy (BIS) offers the potential to identify tissues using rapid measurements that leverage differences in electrical properties between tissues. However, using BIS to differentiate large sets of tissues in a singular anatomical area, such as the gastrointestinal (GI) tract, has remained a significant challenge because of the overlap of similar tissues' responses and variability between measurements. This work proposes the application of frequency response function (FRF) similarity metrics as a signal processing technique to extract new features from BIS measurements on porcine tissues. These features are then used as inputs to machine learning (ML) models that are trained on an ex vivo dataset for identification of eight different in vivo porcine abdominal tissues. The ML models using similarity metric inputs performed on par or better than models using raw measurement inputs, except for the support vector machine (SVM) models. A neural network (NN) model using a similarity metric input performed best by achieving a mean accuracy of 70.3% and F-measure of 0.716. More importantly, the similarity metrics enhanced the ability of the models to identify all tissues rather than considering tissues from similar anatomical areas as the same. Ultimately, the FRF similarity metrics are a novel approach for extracting features from BIS measurements that improved identification performance when considering both accuracy and capability of differentiating all tissues in the dataset.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559621","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}

引用次数: 0

Methodological Considerations in the Analysis of Acoustically Evoked Neural Signals: A Comparative Study of Active EEG, Passive EEG and MEG. 声诱发神经信号分析的方法学考虑：主动脑电图、被动脑电图和脑磁图的比较研究。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782081

Nikola Kolbl, Konstantin Tziridis, Patrick Krauss, Achim Schilling

{"title":"Methodological Considerations in the Analysis of Acoustically Evoked Neural Signals: A Comparative Study of Active EEG, Passive EEG and MEG.","authors":"Nikola Kolbl, Konstantin Tziridis, Patrick Krauss, Achim Schilling","doi":"10.1109/EMBC53108.2024.10782081","DOIUrl":"10.1109/EMBC53108.2024.10782081","url":null,"abstract":"Analyzing and deciphering brain signals on a single trial base is the main goal of brain-computer interface (BCI) research as well as neurolinguistics. In the present study, we have evaluated the efficacy of three neuroimaging techniques-active electroencephalography (EEG), passive EEG, and magnetoencephalography (MEG)-in capturing and evaluating brain activity in response to auditory stimuli. The main goals of our research included two primary components: first, to identify ROIs, and second, to determine the appropriate number of stimulus samples needed to achieve a meaningful level of reliability. To estimate this number of measurement repetitions we performed step-wise sub-sampling combined with permutation testing. This involved a detailed comparison of event-related potentials resp. fields (ERPs, ERFs) elicited by auditory stimuli such as acoustic clicks and continuous speech. Our results show that active EEG outperformed passive EEG and MEG in sensor space. However, MEG demonstrated superior signal localization in source space. These results also highlight the complexity of developing real-time speech BCIs.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559763","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}

引用次数: 0

The Effects of Robotic Exoskeleton Gait Training on Improving Walking Adaptability in Persons with MS. 机器人外骨骼步态训练对提高多发性硬化症患者行走适应能力的影响

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781725

Ghaith J Androwis, Alfonse Gaite, Amanda Engler, Guang H Yue, John DeLuca

{"title":"The Effects of Robotic Exoskeleton Gait Training on Improving Walking Adaptability in Persons with MS.","authors":"Ghaith J Androwis, Alfonse Gaite, Amanda Engler, Guang H Yue, John DeLuca","doi":"10.1109/EMBC53108.2024.10781725","DOIUrl":"10.1109/EMBC53108.2024.10781725","url":null,"abstract":"The goal of the present pilot investigation is to examine the effects of 8 weeks of supervised, over-ground gait training using a robotic exoskeleton (RE) compared with a control condition (conventional gait therapy, CGT) in persons with MS with ambulatory. Four female subjects (mean age=53 years) with relapsing-remitting MS (RRMS) participated in this study and completed a total of sixteen sessions (1-hour/session) gait training in a standard therapy gym either using a RE supervised by a physical therapist (PT) trained with RE therapy (2 subjects) or with the CGT (2 subjects) supervised by a PT. Outcome measures (obstacle avoidance, ability to track augmented cues, and average walking speed while completing these dual-tasks) were measured for both groups on a smart, instrumented treadmill (C-Mill, Motekforce, Netherland) pre- and post-intervention without the RE. Overall, individuals with MS who underwent training with RE demonstrated improved walking adaptability (obstacle avoidance and augmented cues tracking) with no adverse events during the study, and improved average walking speed post training compared to baseline.Clinical Relevance- These preliminary results from four individuals with MS suggest that gait training with robotic exoskeleton may present an effective method for improving walking adaptability and average walking speed.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560043","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}

引用次数: 0

Towards Personalized Inhalation Therapy by Correlating Chest CT Imaging and Pulmonary Function Test Features Using Machine Learning. 利用机器学习将胸部CT成像和肺功能测试特征相关联，实现个性化吸入治疗。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781590

Ethan O'Connor, Emmanuel Yangue, Yu Feng, Huimin Wu, Chenang Liu

{"title":"Towards Personalized Inhalation Therapy by Correlating Chest CT Imaging and Pulmonary Function Test Features Using Machine Learning.","authors":"Ethan O'Connor, Emmanuel Yangue, Yu Feng, Huimin Wu, Chenang Liu","doi":"10.1109/EMBC53108.2024.10781590","DOIUrl":"10.1109/EMBC53108.2024.10781590","url":null,"abstract":"Inhalation therapy is the predominant method of treatment for a variety of respiratory diseases. The effectiveness of such treatment is dependent on the accuracy of medication delivery. Thus, personalized inhalation therapy wherein inhaler designs are specifically suited to the patient's needs is highly desirable. Although computational fluid-particle dynamics (CFPD)-based simulation has demonstrated potential in advancing personalized inhalation therapy, it still requires a 3D model of the patient's respiratory system. Such a model could be constructed with computed tomography (CT) images; however, CT scans are costly and have a high risk of radiation exposure. This concern motivates this study to bridge chest CT images and pulmonary function test (PFT) data, which is noninvasive and easy to obtain. To achieve this goal, an autoencoder is leveraged to find a lower dimensional representation of the CT image; PFT data is then mapped to the encoded image using partial least squares (PLS) regression. Using the decoder in the trained autoencoder, a CT image can be reconstructed by the encoded image predicted by PFT data. This method would allow for greater accessibility to chest CT imaging without exposing patients to the potential negative effects of CT scans, significantly advancing personalized inhalation therapy for respiratory diseases. The results of preliminary experiments using a real-world dataset demonstrate promising performance with our proposed approach.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560295","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}

引用次数: 0

3D Multi-feature fusion convolutional network for Alzheimer's disease diagnosis. 三维多特征融合卷积网络用于阿尔茨海默病诊断。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782006

Jiao Jiao Feng, Mao Wen Ba, Nan Li, Gang Wang

{"title":"3D Multi-feature fusion convolutional network for Alzheimer's disease diagnosis.","authors":"Jiao Jiao Feng, Mao Wen Ba, Nan Li, Gang Wang","doi":"10.1109/EMBC53108.2024.10782006","DOIUrl":"10.1109/EMBC53108.2024.10782006","url":null,"abstract":"The cognitive decline caused by Alzheimer's disease (AD) is closely related to the structural changes in the hippocampus captured by structural magnetic resonance imaging (sMRI). However, current deep model research on the morphological analysis of hippocampus is mainly based on 2D MRI slices, lacking a comprehensive description of the 3D surface morphology and complex textures of the hippocampus. For this reason, we propose a two-stream multi features deep learning model that establishes a descriptive system for 3D spatial structure and morphological atrophy features on the triangular mesh of left and right hippocampus. First, we encode the triangular mesh data into the spatial structural features of the hippocampal surface. Second, considering the tubular structure of the hippocampus and the inhomogeneous morphological changes caused by AD, we introduce the thickness features and Heat Kernel Signature (HKS) features for the morphological atrophy features encoding. Third, we integrate the encoded features of adjacent faces from a macroscopic perspective into the discriminative morphological features induced by AD. Finally, driven by classification tasks, the deep learning model parameters and the discriminative features are continuously optimized, thereby improving the accuracy of AD diagnosis. Our method is evaluated based on the T 1 weighted sMRI baseline data of 269 Aβ+ AD and 437 Aβ-normal cognitively(NC) subjects collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The classification accuracy of this method for AD and NC subjects is 93.4%, the sensitivity and specificity are 92.4% and 93.8%, respectively, and the area under the ROC curve (AUC) is 98.3%.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558995","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}

引用次数: 0