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Outlook on Industry-Academia-Government Collaborations Impacting Medical Device Innovation. 影响医疗器械创新的产学研合作展望。
Pub Date : 2024-05-01 Epub Date: 2023-10-09 DOI: 10.1115/1.4063464
Martin L Tanaka, Orlando Lopez

The nature of collaborations between industry, academic, and government entities are discussed by the authors who together have significant experience in all three of these sectors. This article examines the intricacies and coordination needed between different stakeholder environments toward successful medical device innovation. The value of different types of collaboration models is illustrated through examples and the author's perspectives on current opportunities, challenges, and future outlook.

作者讨论了行业、学术和政府实体之间合作的性质,他们在这三个领域都有丰富的经验。本文探讨了不同利益相关者环境之间为成功的医疗器械创新所需的复杂性和协调性。通过实例以及作者对当前机遇、挑战和未来前景的看法,说明了不同类型合作模式的价值。
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引用次数: 0
Cervical Column and Cord and Column Responses in Whiplash With Stenosis: A Finite Element Modeling Study. 颈部颈柱和颈索及颈柱在狭窄性鞭笞中的反应:有限元建模研究。
Pub Date : 2024-05-01 Epub Date: 2023-10-03 DOI: 10.1115/1.4063250
Narayan Yoganandan, Balaji Harinathan, Aditya Vedantam

Spine degeneration is a normal aging process. It may lead to stenotic spines that may have implications for pain and quality of life. The diagnosis is based on clinical symptomatology and imaging. Magnetic resonance images often reveal the nature and degree of stenosis of the spine. Stenosis is concerning to clinicians and patients because of the decreased space in the spinal canal and potential for elevated risk of cord and/or osteoligamentous spinal column injuries. Numerous finite element models of the cervical spine have been developed to study the biomechanics of the osteoligamentous column such as range of motion and vertebral stress; however, spinal cord modeling is often ignored. The objective of this study was to determine the external column and internal cord and disc responses of stenotic spines using finite element modeling. A validated model of the subaxial spinal column was used. The osteoligamentous column was modified to include the spinal cord. Mild, moderate, and severe degrees of stenosis commonly identified in civilian populations were simulated at C5-C6. The column-cord model was subjected to postero-anterior acceleration at T1. The range of motion, disc pressure, and cord stress-strain were obtained at the index and superior and inferior adjacent levels of the stenosis. The external metric representing the segmental motion was insensitive while the intrinsic disc and cord variables were more sensitive, and the index level was more affected by stenosis. These findings may influence surgical planning and patient education in personalized medicine.

脊椎退化是一个正常的衰老过程。它可能会导致脊椎狭窄,这可能会影响疼痛和生活质量。诊断是基于临床症状和影像学。磁共振图像经常显示脊柱狭窄的性质和程度。狭窄是临床医生和患者关注的问题,因为椎管内的空间减少,脊髓和/或骨韧带性脊柱损伤的风险可能增加。已经开发了许多颈椎的有限元模型来研究骨韧带柱的生物力学,例如运动范围和脊椎应力;然而,脊髓建模往往被忽视。本研究的目的是使用有限元模型确定狭窄脊柱的外柱、内索和椎间盘反应。使用经验证的亚轴脊柱模型。对骨韧带柱进行了改造,使其包括脊髓。在C5-C6中模拟了平民人群中常见的轻度、中度和重度狭窄。柱索模型在T1时受到前后加速度。在狭窄的指数和上下相邻水平处获得运动范围、椎间盘压力和脊髓应力-应变。代表节段运动的外部指标是不敏感的,而固有的椎间盘和脊髓变量更敏感,指数水平更受狭窄的影响。这些发现可能会影响个性化医疗中的手术计划和患者教育。
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引用次数: 0
Neural Network Modeling of an SLA Printed Mesostructure SLA 印刷中间结构的神经网络建模
Pub Date : 2024-04-10 DOI: 10.1115/1.4065291
Anne Schmitz
This paper addresses the scarcity of comprehensive studies on the collective impact of various parametric lattice designs on mesostructure functionality. Focusing on optimizing the energy absorption of a serpentine mesostructure made using SLA, this research leverages a feedforward neural network to explore the interplay between line width, number of turns, and material properties on the energy absorbed by the structure. Compression simulations using a finite element model, covering a range of configurations, provided the dataset for neural network training. The resulting network was used to probe correlations between geometric variables, material, and energy absorption. Additionally, a neural network sensitivity analysis explored the impact of hidden layers and number of neurons on the network's performance, demonstrating the network's robustness. The optimized mesostructure configuration, identified by the neural network, maximized energy absorption. Using foundational mechanics of materials concepts, the discussion explains the how the geometry and material of the cellular mesostructure affects structural stiffness.
关于各种参数化晶格设计对介质结构功能的共同影响的综合研究十分匮乏,本文针对这一问题进行了研究。本研究以优化使用 SLA 制造的蛇形介观结构的能量吸收为重点,利用前馈神经网络探索线宽、圈数和材料特性之间对结构能量吸收的相互影响。使用有限元模型进行的压缩模拟涵盖了一系列配置,为神经网络训练提供了数据集。由此产生的网络用于探究几何变量、材料和能量吸收之间的相关性。此外,神经网络灵敏度分析探索了隐藏层和神经元数量对网络性能的影响,证明了网络的鲁棒性。神经网络确定的优化中间结构配置最大限度地吸收了能量。讨论利用材料力学的基本概念,解释了细胞介质结构的几何形状和材料如何影响结构刚度。
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引用次数: 0
Pulmonary Nodule Segmentation Network Based On RkcU-Net 基于 RkcU-Net 的肺结节分割网络
Pub Date : 2024-04-03 DOI: 10.1115/1.4065245
Yi Luo, Miao Cao, Xu Chang
U-Net network is widely used in the field of medical image segmentation. The automatic segmentation and detection of lung nodules can help in the early detection of lung cancer. Therefore, in this paper, to solve the problems of small proportion of nodules in CT images, complex features and insufficient segmentation accuracy, an improved U-Net network based on residual network and attention mechanism was proposed. The feature extraction part of RkcU-Net network is based on Res2net, a variant of Resnet, and on which a feature extraction module with automatic selection of convolution kernel size is designed to perform multi-scale convolution inside the feature layer to form perceptual fields of different sizes. This module selects the appropriate convolution kernel size to extract lung nodule features in the face of different fine-grained lung nodules. Secondly, the Contextual Supplementary (CS) Block is designed to use the information of adjacent upper and lower layers to correct for the upper layer features, eliminating the discrepancy in the fusion of features at different levels. In this paper, the LUNA16 dataset was selected as the basis for lung nodule segmentation experiments. The method used in this dataset can achieve a iou of 80.59% and a DSC score of 89.25%. The network effectively improves the accuracy of lung nodule segmentation compared with other models. The results show that the method enhances the feature extraction ability of the network and improves the segmentation effect. In addition, the contribution of jump connections to information recovery should be noted.
U-Net 网络被广泛应用于医学图像分割领域。肺结节的自动分割和检测有助于肺癌的早期发现。因此,本文针对 CT 图像中结节比例小、特征复杂、分割精度不高等问题,提出了一种基于残差网络和注意力机制的改进型 U-Net 网络。RkcU-Net 网络的特征提取部分基于 Resnet 的变种 Res2net,在此基础上设计了一个自动选择卷积核大小的特征提取模块,在特征层内进行多尺度卷积,形成不同大小的感知场。面对不同细粒度的肺结节,该模块会选择合适的卷积核大小来提取肺结节特征。其次,设计了上下文补充(Contextual Supplementary,CS)块,利用相邻上下两层的信息对上层特征进行校正,消除了不同层次特征融合的差异。本文选择 LUNA16 数据集作为肺结节分割实验的基础。该方法在该数据集中的 iou 得分为 80.59%,DSC 得分为 89.25%。与其他模型相比,该网络有效地提高了肺结节分割的准确性。结果表明,该方法增强了网络的特征提取能力,提高了分割效果。此外,还应注意跳跃连接对信息恢复的贡献。
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引用次数: 0
Unaligned Hip Radiograph Assessment Utilizing Convolutional Neural Networks for the Assessment of Developmental Dysplasia of the Hip 利用卷积神经网络评估髋关节发育不良的髋关节X光片不对齐情况
Pub Date : 2024-03-01 DOI: 10.1115/1.4064988
Sheridan Perry, Matthew Folkman, Takara O’Brien, Lauren Wilson, Eric Coyle, Raymond W. Liu, Charles T. Price, Victor Huayamave
Developmental dysplasia of the hip (DDH) is a condition in which the acetabular socket inadequately contains the femoral head. If left untreated, DDH can result in degenerative changes in the hip joint. Several imaging techniques are used for DDH assessment. In radiographs, the acetabular index, center-edge angle, Sharp's angle, and migration percentage metrics are used to assess DDH. Determining these metrics is time-consuming and repetitive. This study uses a convolutional neural network (CNN) to identify radiographic measurements and improve traditional methods of identifying DDH. The dataset consisted of 60 subject radiographs rotated along the craniocaudal and mediolateral axes 25 times, generating 1500 images. A CNN detection algorithm was used to identify key radiographic metrics for the diagnosis of DDH. The algorithm was able to detect the metrics with reasonable accuracy in comparison to the manually computed metrics. The CNN performed well on images with high contrast margins between bone and soft tissues. In comparison, the CNN was not able to identify some critical points for metric calculation on a few images that had poor definition due to low contrast between bone and soft tissues. This study shows that CNNs can efficiently measure clinical parameters to assess DDH on radiographs with high contrast margins between bone and soft tissues with purposeful rotation away from an ideal image. Results from this study could help inform and broaden the existing bank of information on using CNNs for radiographic measurement and medical condition prediction.
髋关节发育不良(DDH)是指髋臼不能充分容纳股骨头。如果不及时治疗,DDH 可导致髋关节退行性病变。有几种成像技术可用于 DDH 评估。在X光片中,髋臼指数、中心边缘角、夏普角和移位百分比指标被用于评估DDH。确定这些指标既耗时又重复。本研究使用卷积神经网络(CNN)来识别放射学测量结果,并改进识别 DDH 的传统方法。数据集包括 60 个受试者的 X 光片,沿头颅外侧轴和内侧轴旋转 25 次,共生成 1500 张图像。采用 CNN 检测算法来识别诊断 DDH 的关键放射学指标。与人工计算的指标相比,该算法能够以合理的准确度检测到这些指标。CNN 在骨与软组织之间具有高对比度边缘的图像上表现良好。相比之下,在一些因骨与软组织对比度低而清晰度较差的图像上,CNN 无法识别一些关键点进行度量计算。这项研究表明,CNN 可以有效地测量临床参数,以评估骨与软组织之间对比度边缘较高的 X 光片上的 DDH,并有目的地旋转理想图像。这项研究的结果有助于为使用 CNN 进行放射学测量和医疗状况预测提供信息,并扩大现有的信息库。
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引用次数: 0
Experimental Investigation of Excitation Strategies for Erosion by Cavitation Histotripsy 空化组织切削术侵蚀激发策略的实验研究
Pub Date : 2024-02-19 DOI: 10.1115/1.4064769
Yufeng Zhou
Cavitation histotripsy has been applied to the disintegration on the surface of soft tissue in a well-controlled manner. Its performance was assumed to be determined by the acoustic pressure alone. Long pulse duration with low pulse repetition frequency (PRF) can also successfully generate erosion. This study was designed to investigate the excitation strategies for cavitation histotripsy-induced erosion. The erosion area and volumes produced by cavitation histotripsy on the alginate gel phantom using single-frequency, dual-frequency, and two pulsed excitations at the same power output at the PRF of 1 Hz and 200 Hz were compared. Dual-frequency excitation can improve the erosion at all PRFs, while pulsed excitations decrease it at the PRF of 200 Hz. Using both pulsed and dual-frequency excitations has more erosion areas than using single-frequency at a PRF of 1 Hz. In comparison, although the induced erosion areas using the pulsed excitations are larger than those of single-frequency at the PRF of 200 Hz, the erosion volumes are much lower than that of dual-frequency excitation. It suggests that a sufficient long pulse duration is another important factor for the performance of cavitation histotripsy. Dual-frequency excitation or amplitude modulation by the low-frequency sinusoidal envelope can achieve more erosion than that produced by single-frequency excitation at the same power output in a wide range of PRFs.
空化组织切削术已被很好地应用于软组织表面的崩解。其性能被认为仅由声压决定。长脉冲持续时间和低脉冲重复频率(PRF)也能成功产生侵蚀。本研究旨在探讨空化组学诱导侵蚀的激发策略。比较了单频、双频以及在相同功率输出、PRF 为 1 Hz 和 200 Hz 的两种脉冲激励下,空化组织切削术在藻酸盐凝胶模型上产生的侵蚀面积和体积。双频激励可以改善所有 PRF 下的侵蚀情况,而脉冲激励则会降低 PRF 为 200 Hz 时的侵蚀情况。在 PRF 为 1 Hz 时,使用脉冲和双频激励比使用单频激励的侵蚀面积更大。相比之下,虽然在 PRF 为 200 Hz 时使用脉冲激励的诱导侵蚀面积比使用单频激励的诱导侵蚀面积大,但侵蚀体积却比使用双频激励的侵蚀体积小得多。这表明足够长的脉冲持续时间是空化组织切削术性能的另一个重要因素。双频激励或低频正弦包络的振幅调制可以在很宽的 PRF 范围内以相同的功率输出获得比单频激励更大的侵蚀量。
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引用次数: 0
A New Method for Assessing Total Cardiovascular Stiffness—Preliminary Data 评估心血管总硬度的新方法--初步数据
Pub Date : 2024-02-09 DOI: 10.1115/1.4064287
Maggie Oliver, Senthil Kumar, Gregory F. Petroski, Noah Manring
This paper demonstrates a new method for assessing total cardiovascular stiffness using the following five hemodynamic parameters gathered during a routine echocardiogram: (1) left ventricular stroke volume, (2) left ventricular ejection period, (3) heart rate, (4) systolic blood pressure, and (5) diastolic blood pressure. This study uses eight volunteer patients undergoing a routine echocardiogram at the University of Missouri Hospitals. Pulse wave velocity (PWV) data was taken immediately after the echocardiogram and compared to the cardiovascular stiffness result obtained from the echocardiogram data. The R2 value for this comparison was 0.8499 which shows a good correlation. We hypothesize that our new method for assessing total cardiovascular stiffness may be considered equivalent to that of the PWV method.
本文展示了一种利用常规超声心动图中收集的以下五个血液动力学参数评估心血管总硬度的新方法:(1) 左心室每搏量;(2) 左心室射血期;(3) 心率;(4) 收缩压;(5) 舒张压。这项研究使用了在密苏里大学医院接受常规超声心动图检查的八名志愿者患者。脉搏波速度(PWV)数据在超声心动图检查后立即采集,并与超声心动图数据得出的心血管僵化结果进行比较。这一比较的 R2 值为 0.8499,显示出良好的相关性。我们假设,我们评估心血管总僵硬度的新方法可被视为等同于脉搏波速度法。
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引用次数: 0
Deep Learning Methods for Diagnosing Thyroid Cancer 诊断甲状腺癌的深度学习方法
Pub Date : 2024-02-09 DOI: 10.1115/1.4064705
Gurmanik Kaur Mann, R. Busi, Satyanarayana Talam, Krishna Marlapalli
One of the prevalent, life-threatening disorders that have been on the rise in recent years is thyroid nodule. A frequent diagnostic technique for locating and identifying thyroid nodules is ultrasound imaging. However, it takes time and presents difficulties for the specialists to evaluate all of the slide images. Automated, reliable, and objective methods are required for accurately evaluating ultrasound images. Recent developments in deep learning have completely changed several facets of image analysis and computer-aided diagnostic (CAD) techniques that deal with the issue of identifying thyroid nodules. We reviewed the literature on the potential, constraints, and present applications of deep learning in thyroid cancer imaging and discussed the study's goals. We provided an overview of latest developments in the diagnosis of thyroid cancer using deep learning techniques and addressed about numerous difficulties and practical issues that can restrict the development of deep learning and its incorporation into healthcare setting.
近年来,甲状腺结节成为威胁生命的常见疾病之一。定位和识别甲状腺结节的常用诊断技术是超声波成像。然而,对专家来说,评估所有切片图像既费时又费力。准确评估超声图像需要自动化、可靠和客观的方法。深度学习的最新发展彻底改变了图像分析和计算机辅助诊断(CAD)技术的多个方面,从而解决了甲状腺结节的识别问题。我们回顾了有关深度学习在甲状腺癌成像中的潜力、限制和当前应用的文献,并讨论了本研究的目标。我们概述了使用深度学习技术诊断甲状腺癌的最新进展,并讨论了可能限制深度学习的发展及其融入医疗环境的众多困难和实际问题。
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引用次数: 0
Deep Learning Methods for Diagnosing Thyroid Cancer 诊断甲状腺癌的深度学习方法
Pub Date : 2024-02-09 DOI: 10.1115/1.4064705
Gurmanik Kaur Mann, R. Busi, Satyanarayana Talam, Krishna Marlapalli
One of the prevalent, life-threatening disorders that have been on the rise in recent years is thyroid nodule. A frequent diagnostic technique for locating and identifying thyroid nodules is ultrasound imaging. However, it takes time and presents difficulties for the specialists to evaluate all of the slide images. Automated, reliable, and objective methods are required for accurately evaluating ultrasound images. Recent developments in deep learning have completely changed several facets of image analysis and computer-aided diagnostic (CAD) techniques that deal with the issue of identifying thyroid nodules. We reviewed the literature on the potential, constraints, and present applications of deep learning in thyroid cancer imaging and discussed the study's goals. We provided an overview of latest developments in the diagnosis of thyroid cancer using deep learning techniques and addressed about numerous difficulties and practical issues that can restrict the development of deep learning and its incorporation into healthcare setting.
近年来,甲状腺结节成为威胁生命的常见疾病之一。定位和识别甲状腺结节的常用诊断技术是超声波成像。然而,对专家来说,评估所有切片图像既费时又费力。准确评估超声图像需要自动化、可靠和客观的方法。深度学习的最新发展彻底改变了图像分析和计算机辅助诊断(CAD)技术的多个方面,从而解决了甲状腺结节的识别问题。我们回顾了有关深度学习在甲状腺癌成像中的潜力、限制和当前应用的文献,并讨论了本研究的目标。我们概述了使用深度学习技术诊断甲状腺癌的最新进展,并讨论了可能限制深度学习的发展及其融入医疗环境的众多困难和实际问题。
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引用次数: 0
A New Method for Assessing Total Cardiovascular Stiffness—Preliminary Data 评估心血管总硬度的新方法--初步数据
Pub Date : 2024-02-09 DOI: 10.1115/1.4064287
Maggie Oliver, Senthil Kumar, Gregory F. Petroski, Noah Manring
This paper demonstrates a new method for assessing total cardiovascular stiffness using the following five hemodynamic parameters gathered during a routine echocardiogram: (1) left ventricular stroke volume, (2) left ventricular ejection period, (3) heart rate, (4) systolic blood pressure, and (5) diastolic blood pressure. This study uses eight volunteer patients undergoing a routine echocardiogram at the University of Missouri Hospitals. Pulse wave velocity (PWV) data was taken immediately after the echocardiogram and compared to the cardiovascular stiffness result obtained from the echocardiogram data. The R2 value for this comparison was 0.8499 which shows a good correlation. We hypothesize that our new method for assessing total cardiovascular stiffness may be considered equivalent to that of the PWV method.
本文展示了一种利用常规超声心动图中收集的以下五个血液动力学参数评估心血管总硬度的新方法:(1) 左心室每搏量;(2) 左心室射血期;(3) 心率;(4) 收缩压;(5) 舒张压。这项研究使用了在密苏里大学医院接受常规超声心动图检查的八名志愿者患者。脉搏波速度(PWV)数据在超声心动图检查后立即采集,并与超声心动图数据得出的心血管僵化结果进行比较。这一比较的 R2 值为 0.8499,显示出良好的相关性。我们假设,我们评估心血管总僵硬度的新方法可被视为等同于脉搏波速度法。
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引用次数: 0
期刊
Journal of engineering and science in medical diagnostics and therapy
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