Pub Date : 2024-02-26DOI: 10.1109/JTEHM.2024.3369764
Shuanglin Li;Rajesh Nair;Syed Mohsen Naqvi
Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder commonly seen in childhood that leads to behavioural changes in social development and communication patterns, often continues into undiagnosed adulthood due to a global shortage of psychiatrists, resulting in delayed diagnoses with lasting consequences on individual’s well-being and the societal impact. Recently, machine learning methodologies have been incorporated into healthcare systems to facilitate the diagnosis and enhance the potential prediction of treatment outcomes for mental health conditions. In ADHD detection, the previous research focused on utilizing functional magnetic resonance imaging (fMRI) or Electroencephalography (EEG) signals, which require costly equipment and trained personnel for data collection. In recent years, speech and text modalities have garnered increasing attention due to their cost-effectiveness and non-wearable sensing in data collection. In this research, conducted in collaboration with the Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust, we gathered audio data from both ADHD patients and normal controls based on the clinically popular Diagnostic Interview for ADHD in adults (DIVA). Subsequently, we transformed the speech data into text modalities through the utilization of the Google Cloud Speech API. We extracted both acoustic and text features from the data, encompassing traditional acoustic features (e.g., MFCC), specialized feature sets (e.g., eGeMAPS), as well as deep-learned linguistic and semantic features derived from pre-trained deep learning models. These features are employed in conjunction with a support vector machine for ADHD classification, yielding promising outcomes in the utilization of audio and text data for effective adult ADHD screening. Clinical impact: This research introduces a transformative approach in ADHD diagnosis, employing speech and text analysis to facilitate early and more accessible detection, particularly beneficial in areas with limited psychiatric resources. Clinical and Translational Impact Statement: The successful application of machine learning techniques in analyzing audio and text data for ADHD screening represents a significant advancement in mental health diagnostics, paving the way for its integration into clinical settings and potentially improving patient outcomes on a broader scale.
{"title":"Acoustic and Text Features Analysis for Adult ADHD Screening: A Data-Driven Approach Utilizing DIVA Interview","authors":"Shuanglin Li;Rajesh Nair;Syed Mohsen Naqvi","doi":"10.1109/JTEHM.2024.3369764","DOIUrl":"10.1109/JTEHM.2024.3369764","url":null,"abstract":"Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder commonly seen in childhood that leads to behavioural changes in social development and communication patterns, often continues into undiagnosed adulthood due to a global shortage of psychiatrists, resulting in delayed diagnoses with lasting consequences on individual’s well-being and the societal impact. Recently, machine learning methodologies have been incorporated into healthcare systems to facilitate the diagnosis and enhance the potential prediction of treatment outcomes for mental health conditions. In ADHD detection, the previous research focused on utilizing functional magnetic resonance imaging (fMRI) or Electroencephalography (EEG) signals, which require costly equipment and trained personnel for data collection. In recent years, speech and text modalities have garnered increasing attention due to their cost-effectiveness and non-wearable sensing in data collection. In this research, conducted in collaboration with the Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust, we gathered audio data from both ADHD patients and normal controls based on the clinically popular Diagnostic Interview for ADHD in adults (DIVA). Subsequently, we transformed the speech data into text modalities through the utilization of the Google Cloud Speech API. We extracted both acoustic and text features from the data, encompassing traditional acoustic features (e.g., MFCC), specialized feature sets (e.g., eGeMAPS), as well as deep-learned linguistic and semantic features derived from pre-trained deep learning models. These features are employed in conjunction with a support vector machine for ADHD classification, yielding promising outcomes in the utilization of audio and text data for effective adult ADHD screening. Clinical impact: This research introduces a transformative approach in ADHD diagnosis, employing speech and text analysis to facilitate early and more accessible detection, particularly beneficial in areas with limited psychiatric resources. Clinical and Translational Impact Statement: The successful application of machine learning techniques in analyzing audio and text data for ADHD screening represents a significant advancement in mental health diagnostics, paving the way for its integration into clinical settings and potentially improving patient outcomes on a broader scale.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"359-370"},"PeriodicalIF":3.4,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10445184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139980103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-20DOI: 10.1109/JTEHM.2024.3368291
James Skoric;Yannick D’Mello;David V. Plant
Wearable sensing has become a vital approach to cardiac health monitoring, and seismocardiography (SCG) is emerging as a promising technology in this field. However, the applicability of SCG is hindered by motion artifacts, including those encountered in practice of which the strongest source is walking. This holds back the translation of SCG to clinical settings. We therefore investigated techniques to enhance the quality of SCG signals in the presence of motion artifacts. To simulate ambulant recordings, we corrupted a clean SCG dataset with real-walking-vibrational noise. We decomposed the signal using several empirical-mode-decomposition methods and the maximum overlap discrete wavelet transform (MODWT). By combining MODWT, time-frequency masking, and nonnegative matrix factorization, we developed a novel algorithm which leveraged the vertical axis accelerometer to reduce walking vibrations in dorsoventral SCG. The accuracy and applicability of our method was verified using heart rate estimation. We used an interactive selection approach to improve estimation accuracy. The best decomposition method for reduction of motion artifact noise was the MODWT. Our algorithm improved heart rate estimation from 0.1 to 0.8 r-squared at −15 dB signal-to-noise ratio (SNR). Our method reduces motion artifacts in SCG signals up to a SNR of −19 dB without requiring any external assistance from electrocardiography (ECG). Such a standalone solution is directly applicable to the usage of SCG in daily life, as a content-rich replacement for other wearables in clinical settings, and other continuous monitoring scenarios. In applications with higher noise levels, ECG may be incorporated to further enhance SCG and extend its usable range. This work addresses the challenges posed by motion artifacts, enabling SCG to offer reliable cardiovascular insights in more difficult scenarios, and thereby facilitating wearable monitoring in daily life and the clinic.
{"title":"A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography","authors":"James Skoric;Yannick D’Mello;David V. Plant","doi":"10.1109/JTEHM.2024.3368291","DOIUrl":"10.1109/JTEHM.2024.3368291","url":null,"abstract":"Wearable sensing has become a vital approach to cardiac health monitoring, and seismocardiography (SCG) is emerging as a promising technology in this field. However, the applicability of SCG is hindered by motion artifacts, including those encountered in practice of which the strongest source is walking. This holds back the translation of SCG to clinical settings. We therefore investigated techniques to enhance the quality of SCG signals in the presence of motion artifacts. To simulate ambulant recordings, we corrupted a clean SCG dataset with real-walking-vibrational noise. We decomposed the signal using several empirical-mode-decomposition methods and the maximum overlap discrete wavelet transform (MODWT). By combining MODWT, time-frequency masking, and nonnegative matrix factorization, we developed a novel algorithm which leveraged the vertical axis accelerometer to reduce walking vibrations in dorsoventral SCG. The accuracy and applicability of our method was verified using heart rate estimation. We used an interactive selection approach to improve estimation accuracy. The best decomposition method for reduction of motion artifact noise was the MODWT. Our algorithm improved heart rate estimation from 0.1 to 0.8 r-squared at −15 dB signal-to-noise ratio (SNR). Our method reduces motion artifacts in SCG signals up to a SNR of −19 dB without requiring any external assistance from electrocardiography (ECG). Such a standalone solution is directly applicable to the usage of SCG in daily life, as a content-rich replacement for other wearables in clinical settings, and other continuous monitoring scenarios. In applications with higher noise levels, ECG may be incorporated to further enhance SCG and extend its usable range. This work addresses the challenges posed by motion artifacts, enabling SCG to offer reliable cardiovascular insights in more difficult scenarios, and thereby facilitating wearable monitoring in daily life and the clinic.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"348-358"},"PeriodicalIF":3.4,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10440609","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139953785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-19DOI: 10.1109/JTEHM.2024.3366504
Satya P. Singh;Sukrit Gupta;Jagath C. Rajapakse
Brain state classification by applying deep learning techniques on neuroimaging data has become a recent topic of research. However, unlike domains where the data is low dimensional or there are large number of available training samples, neuroimaging data is high dimensional and has few training samples. To tackle these issues, we present a sparse feedforward deep neural architecture for encoding and decoding the structural connectome of the human brain. We use a sparsely connected element-wise multiplication as the first hidden layer and a fixed transform layer as the output layer. The number of trainable parameters and the training time is significantly reduced compared to feedforward networks. We demonstrate superior performance of this architecture in encoding the structural connectome implicated in Alzheimer’s disease (AD) and Parkinson’s disease (PD) from DTI brain scans. For decoding, we propose recursive feature elimination (RFE) algorithm based on DeepLIFT, layer-wise relevance propagation (LRP), and Integrated Gradients (IG) algorithms to remove irrelevant features and thereby identify key biomarkers associated with AD and PD. We show that the proposed architecture reduces 45.1% and 47.1% of the trainable parameters compared to a feedforward DNN with an increase in accuracy by 2.6 % and 3.1% for cognitively normal (CN) vs AD and CN vs PD classification, respectively. We also show that the proposed RFE method leads to a further increase in accuracy by 2.1% and 4% for CN vs AD and CN vs PD classification, while removing approximately 90% to 95% irrelevant features. Furthermore, we argue that the biomarkers (i.e., key brain regions and connections) identified are consistent with previous literature. We show that relevancy score-based methods can yield high discriminative power and are suitable for brain decoding. We also show that the proposed approach led to a reduction in the number of trainable network parameters, an increase in classification accuracy, and a detection of brain connections and regions that were consistent with earlier studies.
在神经影像数据上应用深度学习技术进行大脑状态分类已成为近期的研究课题。然而，与数据维度低或有大量可用训练样本的领域不同，神经影像数据维度高且训练样本少。为了解决这些问题，我们提出了一种用于编码和解码人脑结构连接组的稀疏前馈深度神经架构。我们使用稀疏连接的元素相乘作为第一隐层，使用固定变换层作为输出层。与前馈网络相比，可训练参数的数量和训练时间大大减少。我们从 DTI 脑扫描中提取了与阿尔茨海默病（AD）和帕金森病（PD）有关的结构连接组，证明了这种架构在编码方面的卓越性能。在解码方面，我们提出了基于 DeepLIFT、层相关性传播（LRP）和集成梯度（IG）算法的递归特征消除（RFE）算法，以去除不相关的特征，从而识别出与 AD 和 PD 相关的关键生物标记物。我们的研究表明，与前馈 DNN 相比，所提出的架构减少了 45.1% 和 47.1% 的可训练参数，在认知正常 (CN) vs AD 和 CN vs PD 分类中的准确率分别提高了 2.6% 和 3.1%。我们还表明，在去除约 90% 到 95% 的无关特征的同时，所提出的 RFE 方法还能将 CN vs AD 和 CN vs PD 分类的准确率进一步提高 2.1% 和 4%。此外，我们还认为所识别的生物标志物（即关键脑区和连接）与之前的文献一致。我们表明，基于相关性得分的方法可以产生很高的判别能力，适用于大脑解码。我们还表明，所提出的方法减少了可训练网络参数的数量，提高了分类准确性，并发现了与先前研究一致的大脑连接和区域。
{"title":"Sparse Deep Neural Network for Encoding and Decoding the Structural Connectome","authors":"Satya P. Singh;Sukrit Gupta;Jagath C. Rajapakse","doi":"10.1109/JTEHM.2024.3366504","DOIUrl":"10.1109/JTEHM.2024.3366504","url":null,"abstract":"Brain state classification by applying deep learning techniques on neuroimaging data has become a recent topic of research. However, unlike domains where the data is low dimensional or there are large number of available training samples, neuroimaging data is high dimensional and has few training samples. To tackle these issues, we present a sparse feedforward deep neural architecture for encoding and decoding the structural connectome of the human brain. We use a sparsely connected element-wise multiplication as the first hidden layer and a fixed transform layer as the output layer. The number of trainable parameters and the training time is significantly reduced compared to feedforward networks. We demonstrate superior performance of this architecture in encoding the structural connectome implicated in Alzheimer’s disease (AD) and Parkinson’s disease (PD) from DTI brain scans. For decoding, we propose recursive feature elimination (RFE) algorithm based on DeepLIFT, layer-wise relevance propagation (LRP), and Integrated Gradients (IG) algorithms to remove irrelevant features and thereby identify key biomarkers associated with AD and PD. We show that the proposed architecture reduces 45.1% and 47.1% of the trainable parameters compared to a feedforward DNN with an increase in accuracy by 2.6 % and 3.1% for cognitively normal (CN) vs AD and CN vs PD classification, respectively. We also show that the proposed RFE method leads to a further increase in accuracy by 2.1% and 4% for CN vs AD and CN vs PD classification, while removing approximately 90% to 95% irrelevant features. Furthermore, we argue that the biomarkers (i.e., key brain regions and connections) identified are consistent with previous literature. We show that relevancy score-based methods can yield high discriminative power and are suitable for brain decoding. We also show that the proposed approach led to a reduction in the number of trainable network parameters, an increase in classification accuracy, and a detection of brain connections and regions that were consistent with earlier studies.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"371-381"},"PeriodicalIF":3.4,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10440083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139953798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.1109/JTEHM.2024.3355962
Busenur Ceyhan;Parisa Nategh;Mehrnoosh Neghabi;Jacob A. LaMar;Shalaka Konjalwar;Peter Rodriguez;Maureen K. Hahn;Matthew Gross;Gregory Grumbar;Kenneth J. Salleng;Randy D. Blakely;Mahsa Ranji
Objective: Metabolic changes have been extensively documented in neurodegenerative brain disorders, including Parkinson’s disease and Alzheimer’s disease (AD). Mutations in the C. elegans swip-10 gene result in dopamine (DA) dependent motor dysfunction accompanied by DA neuron degeneration. Recently, the putative human ortholog of swip-10 (MBLAC1) was implicated as a risk factor in AD, a disorder that, like PD, has been associated with mitochondrial dysfunction. Interestingly, the AD risk associated with MBLAC1 arises in subjects with cardiovascular morbidity, suggesting a broader functional insult arising from reduced MBLAC1 protein expression and one possibly linked to metabolic alterations. Methods: Our current studies, utilizing Mblac1 knockout (KO) mice, seek to determine whether mitochondrial respiration is affected in the peripheral tissues of these mice. We quantified the levels of mitochondrial coenzymes, NADH, FAD, and their redox ratio (NADH/FAD, RR) in livers and kidneys of wild-type (WT) mice and their homozygous KO littermates of males and females, using 3D optical cryo-imaging. Results: Compared to WT, the RR of livers from KO mice was significantly reduced, without an apparent sex effect, driven predominantly by significantly lower NADH levels. In contrast, no genotype and sex differences were observed in kidney samples. Serum analyses of WT and KO mice revealed significantly elevated glucose levels in young and aged KO adults and diminished cholesterol levels in the aged KOs, consistent with liver dysfunction. Discussion/Conclusion: As seen with C. elegans swip-10 mutants, loss of MBLAC1 protein results in metabolic changes that are not restricted to neural cells and are consistent with the presence of peripheral comorbidities accompanying neurodegenerative disease in cases where MBLAC1 expression changes impact risk.
目的：在包括帕金森病和阿尔茨海默病（AD）在内的脑神经退行性疾病中，代谢变化已被广泛记录。elegans swip-10 基因突变会导致多巴胺（DA）依赖性运动功能障碍，并伴有 DA 神经元变性。最近，swip-10 的推测人类直向同源物（MBLAC1）被认为是 AD 的一个风险因素，AD 和 PD 一样，都与线粒体功能障碍有关。有趣的是，与 MBLAC1 相关的注意力缺失症风险出现在心血管疾病患者身上，这表明 MBLAC1 蛋白表达减少会导致更广泛的功能损伤，而且可能与代谢改变有关。方法：我们目前的研究利用 Mblac1 基因敲除（KO）小鼠，试图确定这些小鼠外周组织的线粒体呼吸是否受到影响。我们利用三维光学冷冻成像技术量化了野生型（WT）小鼠及其同源基因 KO 小鼠雌雄肝脏和肾脏中线粒体辅酶 NADH、FAD 的水平及其氧化还原比率（NADH/FAD，RR）。结果发现与 WT 小鼠相比，KO 小鼠肝脏的 RR 显著降低，且无明显性别效应，这主要是由于 NADH 水平显著降低所致。相比之下，肾脏样本中未观察到基因型和性别差异。对 WT 和 KO 小鼠的血清分析表明，年轻和年老的 KO 成年小鼠体内葡萄糖水平明显升高，而年老的 KO 小鼠体内胆固醇水平降低，这与肝功能异常一致。讨论/结论：正如在 C. elegans swip-10 突变体中看到的那样，MBLAC1 蛋白缺失导致的代谢变化并不局限于神经细胞，在 MBLAC1 表达变化影响风险的情况下，这与神经退行性疾病伴随的外周合并症的存在是一致的。
{"title":"Optical Imaging Demonstrates Tissue-Specific Metabolic Perturbations in Mblac1 Knockout Mice","authors":"Busenur Ceyhan;Parisa Nategh;Mehrnoosh Neghabi;Jacob A. LaMar;Shalaka Konjalwar;Peter Rodriguez;Maureen K. Hahn;Matthew Gross;Gregory Grumbar;Kenneth J. Salleng;Randy D. Blakely;Mahsa Ranji","doi":"10.1109/JTEHM.2024.3355962","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3355962","url":null,"abstract":"Objective: Metabolic changes have been extensively documented in neurodegenerative brain disorders, including Parkinson’s disease and Alzheimer’s disease (AD). Mutations in the C. elegans swip-10 gene result in dopamine (DA) dependent motor dysfunction accompanied by DA neuron degeneration. Recently, the putative human ortholog of swip-10 (MBLAC1) was implicated as a risk factor in AD, a disorder that, like PD, has been associated with mitochondrial dysfunction. Interestingly, the AD risk associated with MBLAC1 arises in subjects with cardiovascular morbidity, suggesting a broader functional insult arising from reduced MBLAC1 protein expression and one possibly linked to metabolic alterations. Methods: Our current studies, utilizing Mblac1 knockout (KO) mice, seek to determine whether mitochondrial respiration is affected in the peripheral tissues of these mice. We quantified the levels of mitochondrial coenzymes, NADH, FAD, and their redox ratio (NADH/FAD, RR) in livers and kidneys of wild-type (WT) mice and their homozygous KO littermates of males and females, using 3D optical cryo-imaging. Results: Compared to WT, the RR of livers from KO mice was significantly reduced, without an apparent sex effect, driven predominantly by significantly lower NADH levels. In contrast, no genotype and sex differences were observed in kidney samples. Serum analyses of WT and KO mice revealed significantly elevated glucose levels in young and aged KO adults and diminished cholesterol levels in the aged KOs, consistent with liver dysfunction. Discussion/Conclusion: As seen with C. elegans swip-10 mutants, loss of MBLAC1 protein results in metabolic changes that are not restricted to neural cells and are consistent with the presence of peripheral comorbidities accompanying neurodegenerative disease in cases where MBLAC1 expression changes impact risk.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"298-305"},"PeriodicalIF":3.4,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10436707","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139744728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-12DOI: 10.1109/JTEHM.2024.3365305
John A. Mccullough;Brandon T. Peterson;Alexander M. Upfill-Brown;Thomas J. Hardin;Jonathan B. Hopkins;Nelson F. Soohoo;Tyler R. Clites
The longevity of current joint replacements is limited by aseptic loosening, which is the primary cause of non-infectious failure for hip, knee, and ankle arthroplasty. Aseptic loosening is typically caused either by osteolysis from particulate wear, or by high shear stresses at the bone-implant interface from over-constraint. Our objective was to demonstrate feasibility of a compliant intramedullary stem that eliminates over-constraint without generating particulate wear. The compliant stem is built around a compliant mechanism that permits rotation about a single axis. We first established several models to understand the relationship between mechanism geometry and implant performance under a given angular displacement and compressive load. We then used a neural network to identify a design space of geometries that would support an expected 100-year fatigue life inside the body. We additively manufactured one representative mechanism for each of three anatomic locations, and evaluated these prototypes on a KR-210 robot. The neural network predicts maximum stress and torsional stiffness with 2.69% and 4.08% error respectively, relative to finite element analysis data. We identified feasible design spaces for all three of the anatomic locations. Simulated peak stresses for the three stem prototypes were below the fatigue limit. Benchtop performance of all three prototypes was within design specifications. Our results demonstrate the feasibility of designing patient- and joint-specific compliant stems that address the root causes of aseptic loosening. Guided by these results, we expect the use of compliant intramedullary stems in joint reconstruction technology to increase implant lifetime.
{"title":"Compliant Intramedullary Stems for Joint Reconstruction","authors":"John A. Mccullough;Brandon T. Peterson;Alexander M. Upfill-Brown;Thomas J. Hardin;Jonathan B. Hopkins;Nelson F. Soohoo;Tyler R. Clites","doi":"10.1109/JTEHM.2024.3365305","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3365305","url":null,"abstract":"The longevity of current joint replacements is limited by aseptic loosening, which is the primary cause of non-infectious failure for hip, knee, and ankle arthroplasty. Aseptic loosening is typically caused either by osteolysis from particulate wear, or by high shear stresses at the bone-implant interface from over-constraint. Our objective was to demonstrate feasibility of a compliant intramedullary stem that eliminates over-constraint without generating particulate wear. The compliant stem is built around a compliant mechanism that permits rotation about a single axis. We first established several models to understand the relationship between mechanism geometry and implant performance under a given angular displacement and compressive load. We then used a neural network to identify a design space of geometries that would support an expected 100-year fatigue life inside the body. We additively manufactured one representative mechanism for each of three anatomic locations, and evaluated these prototypes on a KR-210 robot. The neural network predicts maximum stress and torsional stiffness with 2.69% and 4.08% error respectively, relative to finite element analysis data. We identified feasible design spaces for all three of the anatomic locations. Simulated peak stresses for the three stem prototypes were below the fatigue limit. Benchtop performance of all three prototypes was within design specifications. Our results demonstrate the feasibility of designing patient- and joint-specific compliant stems that address the root causes of aseptic loosening. Guided by these results, we expect the use of compliant intramedullary stems in joint reconstruction technology to increase implant lifetime.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"314-327"},"PeriodicalIF":3.4,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10433175","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139942667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-12DOI: 10.1109/JTEHM.2024.3365300
Jan-Willem Klok;Jessica Groenewegen;Olivier Temmerman;Niels Van Straten;Bart Van Straten;Jenny Dankelman;Tim Horeman
Objective: Cemented total hip arthroplasty (THA) demonstrates superior survival rates compared to uncemented procedures. Nevertheless, most younger patients opt for uncemented THA, as removing well-fixed bone cement in the femur during revisions is complex, particularly the distal cement plug. This removal procedure often increases the risk of femoral fracture or perforation, haemorrhage and weakening bone due to poor drill control and positioning. Aim of this study was to design a novel drill guide to improve drill positioning. Methods and procedures: A novel orthopaedic drill guide was developed, featuring a compliant centralizer activated by a drill guide actuator. Bone models were prepared to assess centralizing performance. Three conditions were tested: drilling without guidance, guided drilling with centralizer activation held, and guided drilling with centralizer activation released. Deviations from the bone centre were measured at the entry and exit point of the drill. Results: In the centralizing performance test, the drill guide significantly reduced drill hole deviations in both entry and exit points compared to the control ( $p < 0.05$ ). The absolute deviation on the exit side of the cement plug was 10.59mm (SD 1.56) for the ‘No drill guide‘ condition, 3.02mm (SD 2.09) for ‘Drill guide – hold‘ and 2.12mm (SD 1.71) for ‘Drill guide – release‘. The compliant drill guide centralizer significantly lowered the risk of cortical bone perforation during intramedullary canal drilling in the bone models due to better control of the cement drill position. Clinical and Translational Impact Statement: The drill guide potentially reduces perioperative risks in cemented femoral stem revision. Future research should identify optimal scenarios for its application.
{"title":"Design and In Vitro Validation of an Orthopaedic Drill Guide for Femoral Stem Revision in Total Hip Arthroplasty","authors":"Jan-Willem Klok;Jessica Groenewegen;Olivier Temmerman;Niels Van Straten;Bart Van Straten;Jenny Dankelman;Tim Horeman","doi":"10.1109/JTEHM.2024.3365300","DOIUrl":"10.1109/JTEHM.2024.3365300","url":null,"abstract":"Objective: Cemented total hip arthroplasty (THA) demonstrates superior survival rates compared to uncemented procedures. Nevertheless, most younger patients opt for uncemented THA, as removing well-fixed bone cement in the femur during revisions is complex, particularly the distal cement plug. This removal procedure often increases the risk of femoral fracture or perforation, haemorrhage and weakening bone due to poor drill control and positioning. Aim of this study was to design a novel drill guide to improve drill positioning. Methods and procedures: A novel orthopaedic drill guide was developed, featuring a compliant centralizer activated by a drill guide actuator. Bone models were prepared to assess centralizing performance. Three conditions were tested: drilling without guidance, guided drilling with centralizer activation held, and guided drilling with centralizer activation released. Deviations from the bone centre were measured at the entry and exit point of the drill. Results: In the centralizing performance test, the drill guide significantly reduced drill hole deviations in both entry and exit points compared to the control (\u0000<inline-formula> <tex-math>$p < 0.05$ </tex-math></inline-formula>\u0000). The absolute deviation on the exit side of the cement plug was 10.59mm (SD 1.56) for the ‘No drill guide‘ condition, 3.02mm (SD 2.09) for ‘Drill guide – hold‘ and 2.12mm (SD 1.71) for ‘Drill guide – release‘. The compliant drill guide centralizer significantly lowered the risk of cortical bone perforation during intramedullary canal drilling in the bone models due to better control of the cement drill position. Clinical and Translational Impact Statement: The drill guide potentially reduces perioperative risks in cemented femoral stem revision. Future research should identify optimal scenarios for its application.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"340-347"},"PeriodicalIF":3.4,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10433182","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139953799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-31DOI: 10.1109/JTEHM.2024.3360865
Maksym A. Jopek;Krzysztof Pastuszak;Sebastian Cygert;Myron G. Best;Thomas Wurdinger;Jacek Jassem;Anna J. Żaczek;Anna Supernat
The field of cancer diagnostics has been revolutionized by liquid biopsies, which offer a bridge between laboratory research and clinical settings. These tests are less invasive than traditional biopsies and more convenient than routine imaging methods. Liquid biopsies allow studying of tumor-derived markers in bodily fluids, enabling the development of more precise cancer diagnostic tests for screening, disease monitoring, and therapy personalization. This study presents a multiclass approach based on deep learning to analyze and classify diseases based on blood platelet RNA. Its primary objective is to enhance cancer-type diagnosis in clinical settings by leveraging the power of deep learning combined with high-throughput sequencing of liquid biopsy. Ultimately, the study demonstrates the potential of this approach to accurately identify the patient’s type of cancer. Methods: The developed method classifies patients using heatmap images, generated based on gene expression arranged according to the Kyoto Encyclopedia of Genes and Genomes pathways. The images represent samples of patients with ovarian cancer, endometrial cancer, glioblastoma, non-small cell lung cancer, and sarcoma, as well as cancer patients with brain metastasis. Results: Our deep learning-based models reached 66.51% balanced accuracy when distinguishing between those 6 sites of cancer origin and 90.5% balanced accuracy on a location-specific dataset where cancer types from close locations were grouped. The developed models were further investigated with an explainable artificial intelligence-based approach (XAI) - SHAP. They returned a set of 60 genes with the highest impact on the models’ decision-making process. Conclusions: Our results show that deep-learning methods are a promising opportunity for cancer detection and could support clinicians’ decision-making process in finding the solution for the black-box problem. Clinical and Translational Impact Statement— Utilizing TEPs-based liquid biopsies and deep learning, our study offers a novel approach to early cancer detection, highlighting cancer origin. The integration of Explainable AI reinforces trust in predictive outcomes. Category: Early/Pre-Clinical Research.
{"title":"Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data","authors":"Maksym A. Jopek;Krzysztof Pastuszak;Sebastian Cygert;Myron G. Best;Thomas Wurdinger;Jacek Jassem;Anna J. Żaczek;Anna Supernat","doi":"10.1109/JTEHM.2024.3360865","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3360865","url":null,"abstract":"The field of cancer diagnostics has been revolutionized by liquid biopsies, which offer a bridge between laboratory research and clinical settings. These tests are less invasive than traditional biopsies and more convenient than routine imaging methods. Liquid biopsies allow studying of tumor-derived markers in bodily fluids, enabling the development of more precise cancer diagnostic tests for screening, disease monitoring, and therapy personalization. This study presents a multiclass approach based on deep learning to analyze and classify diseases based on blood platelet RNA. Its primary objective is to enhance cancer-type diagnosis in clinical settings by leveraging the power of deep learning combined with high-throughput sequencing of liquid biopsy. Ultimately, the study demonstrates the potential of this approach to accurately identify the patient’s type of cancer. Methods: The developed method classifies patients using heatmap images, generated based on gene expression arranged according to the Kyoto Encyclopedia of Genes and Genomes pathways. The images represent samples of patients with ovarian cancer, endometrial cancer, glioblastoma, non-small cell lung cancer, and sarcoma, as well as cancer patients with brain metastasis. Results: Our deep learning-based models reached 66.51% balanced accuracy when distinguishing between those 6 sites of cancer origin and 90.5% balanced accuracy on a location-specific dataset where cancer types from close locations were grouped. The developed models were further investigated with an explainable artificial intelligence-based approach (XAI) - SHAP. They returned a set of 60 genes with the highest impact on the models’ decision-making process. Conclusions: Our results show that deep-learning methods are a promising opportunity for cancer detection and could support clinicians’ decision-making process in finding the solution for the black-box problem. Clinical and Translational Impact Statement— Utilizing TEPs-based liquid biopsies and deep learning, our study offers a novel approach to early cancer detection, highlighting cancer origin. The integration of Explainable AI reinforces trust in predictive outcomes. Category: Early/Pre-Clinical Research.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"306-313"},"PeriodicalIF":3.4,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10418148","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139738978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.1109/JTEHM.2024.3357287
Pasquale Arpaia;Renato Cuocolo;Allegra Fullin;Ludovica Gargiulo;Francesca Mancino;Nicola Moccaldi;Ersilia Vallefuoco;Paolo De Blasiis
Executive functions (EFs) are neurocognitive processes planning and regulating daily life actions. Performance of two simultaneous tasks, requiring the same cognitive resources, lead to a cognitive fatigue. Several studies investigated cognitive-motor task and the interference during walking, highlighting an increasing risk of falls especially in elderly and people with neurological diseases. A few studies instrumentally explored relationship between activation-no-activation of two EFs (working memory and inhibition) and spatial-temporal gait parameters. Aim of our study was to detect activation of inhibition and working memory during progressive difficulty levels of cognitive tasks and spontaneous walking using, respectively, wireless electroencephalography (EEG) and 3D-gait analysis. Thirteen healthy subjects were recruited. Two cognitive tasks were performed, activating inhibition (Go-NoGo) and working memory (N-back). EEG features (absolute and relative power in different bands) and kinematic parameters (7 spatial-temporal ones and Gait Variable Score for 9 range of motion of lower limbs) were analyzed. A significant decrease of stride length and an increase of external-rotation of foot progression were found during dual task with Go-NoGo. Moreover, a significant correlation was found between the relative power in the delta band at channels Fz, C4 and progressive difficulty levels of Go-NoGo (activating inhibition) during walking, whereas working memory showed no correlation. This study reinforces the hypothesis of the prevalent involvement of inhibition with respect to working memory during dual task walking and reveals specific kinematic adaptations. The foundations for EEG-based monitoring of cognitive processes involved in gait are laid. Clinical and Translational Impact Statement: Clinical and instrumental evaluation and training of executive functions (as inhibition), during cognitive-motor task, could be useful for rehabilitation treatment of gait disorder in elderly and people with neurological disease.
{"title":"Executive Functions Assessment Based on Wireless EEG and 3D Gait Analysis During Dual-Task: A Feasibility Study","authors":"Pasquale Arpaia;Renato Cuocolo;Allegra Fullin;Ludovica Gargiulo;Francesca Mancino;Nicola Moccaldi;Ersilia Vallefuoco;Paolo De Blasiis","doi":"10.1109/JTEHM.2024.3357287","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3357287","url":null,"abstract":"Executive functions (EFs) are neurocognitive processes planning and regulating daily life actions. Performance of two simultaneous tasks, requiring the same cognitive resources, lead to a cognitive fatigue. Several studies investigated cognitive-motor task and the interference during walking, highlighting an increasing risk of falls especially in elderly and people with neurological diseases. A few studies instrumentally explored relationship between activation-no-activation of two EFs (working memory and inhibition) and spatial-temporal gait parameters. Aim of our study was to detect activation of inhibition and working memory during progressive difficulty levels of cognitive tasks and spontaneous walking using, respectively, wireless electroencephalography (EEG) and 3D-gait analysis. Thirteen healthy subjects were recruited. Two cognitive tasks were performed, activating inhibition (Go-NoGo) and working memory (N-back). EEG features (absolute and relative power in different bands) and kinematic parameters (7 spatial-temporal ones and Gait Variable Score for 9 range of motion of lower limbs) were analyzed. A significant decrease of stride length and an increase of external-rotation of foot progression were found during dual task with Go-NoGo. Moreover, a significant correlation was found between the relative power in the delta band at channels Fz, C4 and progressive difficulty levels of Go-NoGo (activating inhibition) during walking, whereas working memory showed no correlation. This study reinforces the hypothesis of the prevalent involvement of inhibition with respect to working memory during dual task walking and reveals specific kinematic adaptations. The foundations for EEG-based monitoring of cognitive processes involved in gait are laid. Clinical and Translational Impact Statement: Clinical and instrumental evaluation and training of executive functions (as inhibition), during cognitive-motor task, could be useful for rehabilitation treatment of gait disorder in elderly and people with neurological disease.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"268-278"},"PeriodicalIF":3.4,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10411910","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139676113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objective: A change in handwriting is an early sign of Parkinson’s disease (PD). However, significant inter-person differences in handwriting make it difficult to identify pathological handwriting, especially in the early stages. This paper reports the testing of NeuroDiag, a software-based medical device, for the automated detection of PD using handwriting patterns. NeuroDiag is designed to direct the user to perform six drawing and writing tasks, and the recordings are then uploaded onto a server for analysis. Kinematic information and pen pressure of handwriting are extracted and used as baseline parameters. NeuroDiag was trained based on 26 PD patients in the early stage of the disease and 26 matching controls. Methods: Twenty-three people with PD (PPD) in their early stage of the disease, 25 age-matched healthy controls (AMC), and 7 young healthy controls were recruited for this study. Under the supervision of a consultant neurologist or their nurse, the participants used NeuroDiag. The reports were generated in real-time and tabulated by an independent observer. Results: The participants were able to use NeuroDiag without assistance. The handwriting data was successfully uploaded to the server where the report was automatically generated in real-time. There were significant differences in the writing speed between PPD and AMC (P<0.001). NeuroDiag showed 86.96% sensitivity and 76.92% specificity in differentiating PPD from those without PD. Conclusion: In this work, we tested the reliability of NeuroDiag in differentiating between PPD and AMC for real-time applications. The results show that NeuroDiag has the potential to be used to assist neurologists and for telehealth applications. Clinical and Translational Impact Statement — This pre-clinical study shows the feasibility of developing a community-wide screening program for Parkinson’s disease using automated handwriting analysis software, NeuroDiag.
{"title":"NeuroDiag: Software for Automated Diagnosis of Parkinson’s Disease Using Handwriting","authors":"Quoc Cuong Ngo;Nicole McConnell;Mohammod Abdul Motin;Barbara Polus;Arup Bhattacharya;Sanjay Raghav;Dinesh Kant Kumar","doi":"10.1109/JTEHM.2024.3355432","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3355432","url":null,"abstract":"Objective: A change in handwriting is an early sign of Parkinson’s disease (PD). However, significant inter-person differences in handwriting make it difficult to identify pathological handwriting, especially in the early stages. This paper reports the testing of NeuroDiag, a software-based medical device, for the automated detection of PD using handwriting patterns. NeuroDiag is designed to direct the user to perform six drawing and writing tasks, and the recordings are then uploaded onto a server for analysis. Kinematic information and pen pressure of handwriting are extracted and used as baseline parameters. NeuroDiag was trained based on 26 PD patients in the early stage of the disease and 26 matching controls. Methods: Twenty-three people with PD (PPD) in their early stage of the disease, 25 age-matched healthy controls (AMC), and 7 young healthy controls were recruited for this study. Under the supervision of a consultant neurologist or their nurse, the participants used NeuroDiag. The reports were generated in real-time and tabulated by an independent observer. Results: The participants were able to use NeuroDiag without assistance. The handwriting data was successfully uploaded to the server where the report was automatically generated in real-time. There were significant differences in the writing speed between PPD and AMC (P<0.001). NeuroDiag showed 86.96% sensitivity and 76.92% specificity in differentiating PPD from those without PD. Conclusion: In this work, we tested the reliability of NeuroDiag in differentiating between PPD and AMC for real-time applications. The results show that NeuroDiag has the potential to be used to assist neurologists and for telehealth applications. Clinical and Translational Impact Statement — This pre-clinical study shows the feasibility of developing a community-wide screening program for Parkinson’s disease using automated handwriting analysis software, NeuroDiag.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"291-297"},"PeriodicalIF":3.4,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10403837","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139704516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-04DOI: 10.1109/JTEHM.2024.3349916
Mieli Luukinen;Henna Pitkänen;Timo Leppänen;Juha Töyräs;Anna Sigridur Islind;Samu Kainulainen;Henri Korkalainen
Objective: The aim of this study was to assess how the photoplethysmogram frequency and amplitude responses to arousals from sleep differ between arousals caused by apneas and hypopneas with and without blood oxygen desaturations, and spontaneous arousals. Stronger arousal causes were hypothesized to lead to larger and faster responses. Methods and procedures: Photoplethysmogram signal segments during and around respiratory and spontaneous arousals of 876 suspected obstructive sleep apnea patients were analyzed. Logistic functions were fit to the mean instantaneous frequency and instantaneous amplitude of the signal to detect the responses. Response intensities and timings were compared between arousals of different causes. Results: The majority of the studied arousals induced photoplethysmogram responses. The frequency response was more intense ( ${p} < 0.001$ ) after respiratory than spontaneous arousals, and after arousals caused by apneas compared to those caused by hypopneas. The amplitude response was stronger ( ${p} < 0.001$ ) following hypopneas associated with blood oxygen desaturations compared to those that were not. The delays of these responses relative to the electroencephalogram arousal start times were the longest ( ${p} < 0.001$ ) after arousals caused by apneas and the shortest after spontaneous arousals and arousals caused by hypopneas without blood oxygen desaturations. Conclusion: The presence and type of an airway obstruction and the presence of a blood oxygen desaturation affect the intensity and the timing of photoplethysmogram responses to arousals from sleep. Clinical impact: The photoplethysmogram responses could be used for detecting arousals and assessing their intensity, and the individual variation in the response intensity and timing may hold diagnostically significant information.
{"title":"Variation in the Photoplethysmogram Response to Arousal From Sleep Depending on the Cause of Arousal and the Presence of Desaturation","authors":"Mieli Luukinen;Henna Pitkänen;Timo Leppänen;Juha Töyräs;Anna Sigridur Islind;Samu Kainulainen;Henri Korkalainen","doi":"10.1109/JTEHM.2024.3349916","DOIUrl":"10.1109/JTEHM.2024.3349916","url":null,"abstract":"Objective: The aim of this study was to assess how the photoplethysmogram frequency and amplitude responses to arousals from sleep differ between arousals caused by apneas and hypopneas with and without blood oxygen desaturations, and spontaneous arousals. Stronger arousal causes were hypothesized to lead to larger and faster responses. Methods and procedures: Photoplethysmogram signal segments during and around respiratory and spontaneous arousals of 876 suspected obstructive sleep apnea patients were analyzed. Logistic functions were fit to the mean instantaneous frequency and instantaneous amplitude of the signal to detect the responses. Response intensities and timings were compared between arousals of different causes. Results: The majority of the studied arousals induced photoplethysmogram responses. The frequency response was more intense (\u0000<inline-formula> <tex-math>${p} < 0.001$ </tex-math></inline-formula>\u0000) after respiratory than spontaneous arousals, and after arousals caused by apneas compared to those caused by hypopneas. The amplitude response was stronger (\u0000<inline-formula> <tex-math>${p} < 0.001$ </tex-math></inline-formula>\u0000) following hypopneas associated with blood oxygen desaturations compared to those that were not. The delays of these responses relative to the electroencephalogram arousal start times were the longest (\u0000<inline-formula> <tex-math>${p} < 0.001$ </tex-math></inline-formula>\u0000) after arousals caused by apneas and the shortest after spontaneous arousals and arousals caused by hypopneas without blood oxygen desaturations. Conclusion: The presence and type of an airway obstruction and the presence of a blood oxygen desaturation affect the intensity and the timing of photoplethysmogram responses to arousals from sleep. Clinical impact: The photoplethysmogram responses could be used for detecting arousals and assessing their intensity, and the individual variation in the response intensity and timing may hold diagnostically significant information.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"328-339"},"PeriodicalIF":3.4,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10380637","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139953793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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