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22 条记录 (耗时 0.022 秒)

    Combining Predictive Capabilities of Transcranial Doppler (TCD) with Electrocardiogram (ECG) to Predict Hemorrhagic Shock
    1.
    发明申请
    Combining Predictive Capabilities of Transcranial Doppler (TCD) with Electrocardiogram (ECG) to Predict Hemorrhagic Shock 失效
    将经颅多普勒（TCD）与心电图（ECG）的预测能力结合预测出血性休克

    公开(公告)号：US20120136224A1

    公开(公告)日：2012-05-31

    申请号：US13255549

    申请日：2010-03-17

    申请人： Kayvan Najarian ,  Kevin R. Ward ,  Soo-Yeon Ji ,  Roya Hakimzadeh

    发明人： Kayvan Najarian ,  Kevin R. Ward ,  Soo-Yeon Ji ,  Roya Hakimzadeh

    IPC分类号： A61B5/0402 ,  A61B5/021 ,  A61B5/053 ,  A61B8/06

    CPC分类号： A61B5/0402 ,  A61B5/026 ,  A61B5/0295 ,  A61B5/04014 ,  A61B5/7203 ,  A61B5/726 ,  A61B5/7267 ,  A61B5/7275 ,  A61B8/06 ,  A61B8/0808 ,  G06F19/00 ,  G06K9/00523 ,  G16H50/20

    摘要： A real-time decision-support system predicts hemorrhagic shock of a patient by analysis of electrocardiogram (ECG) signals and transcranial Doppler (TCD) signals from the patient. These signals are subject to signal decomposition using Discrete Wavelet Transform (DWT) to sets of wavelet coefficients and selecting significant signal features. Machine learning is applied to the significant features to evaluate and classify hypovolemia severity based on the input ECG and TCD signals from the patient. The classification of blood loss severity is displayed in real-time. An extension of the decision-support system integrates Arterial Blood Pressure (ABP) signals and thoracic electrical bio-impedance (DZT) signals with the ECG and TCD signals from the patient to evaluate severity of hypovolemia.

    摘要翻译： 实时决策支持系统通过分析来自患者的心电图（ECG）信号和经颅多普勒（TCD）信号来预测患者的出血性休克。 这些信号经过信号分解，使用离散小波变换（DWT）对小波系数的集合和选择显着的信号特征。 机器学习应用于显着特征，以根据患者的输入ECG和TCD信号评估和分类血容量不足严重程度。 失血严重程度分类显示实时显示。 决策支持系统的扩展将动脉血压（ABP）信号和胸部电生物阻抗（DZT）信号与患者的ECG和TCD信号相结合，以评估血容量不足的严重程度。

    Combining predictive capabilities of Transcranial Doppler (TCD) with Electrocardiogram (ECG) to predict hemorrhagic shock
    2.
    发明授权
    Combining predictive capabilities of Transcranial Doppler (TCD) with Electrocardiogram (ECG) to predict hemorrhagic shock 失效
    将经颅多普勒（TCD）与心电图（ECG）的预测能力结合预测出血性休克

    公开(公告)号：US08762308B2

    公开(公告)日：2014-06-24

    申请号：US13255549

    申请日：2010-03-17

    申请人： Kayvan Najarian ,  Kevin R. Ward ,  Soo-Yeon Ji ,  Roya Hakimzadeh

    发明人： Kayvan Najarian ,  Kevin R. Ward ,  Soo-Yeon Ji ,  Roya Hakimzadeh

    IPC分类号： G06N5/00

    CPC分类号： A61B5/0402 ,  A61B5/026 ,  A61B5/0295 ,  A61B5/04014 ,  A61B5/7203 ,  A61B5/726 ,  A61B5/7267 ,  A61B5/7275 ,  A61B8/06 ,  A61B8/0808 ,  G06F19/00 ,  G06K9/00523 ,  G16H50/20

    摘要： A real-time decision-support system predicts hemorrhagic shock of a patient by analysis of electrocardiogram (ECG) signals and transcranial Doppler (TCD) signals from the patient. These signals are subject to signal decomposition using Discrete Wavelet Transform (DWT) to sets of wavelet coefficients and selecting significant signal features. Machine learning is applied to the significant features to evaluate and classify hypovolemia severity based on the input ECG and TCD signals from the patient. The classification of blood loss severity is displayed in real-time. An extension of the decision-support system integrates Arterial Blood Pressure (ABP) signals and thoracic electrical bio-impedance (DZT) signals with the ECG and TCD signals from the patient to evaluate severity of hypovolemia.

    摘要翻译： 实时决策支持系统通过分析来自患者的心电图（ECG）信号和经颅多普勒（TCD）信号来预测患者的出血性休克。 这些信号经过信号分解，使用离散小波变换（DWT）对小波系数的集合和选择显着的信号特征。 机器学习应用于显着特征，以根据患者的输入ECG和TCD信号评估和分类血容量不足严重程度。 失血严重程度分类显示实时显示。 决策支持系统的扩展将动脉血压（ABP）信号和胸部电生物阻抗（DZT）信号与患者的ECG和TCD信号相结合，以评估血容量不足的严重程度。

    Automated Measurement of Brain Injury Indices Using Brain CT Images, Injury Data, and Machine Learning
    5.
    发明申请
    Automated Measurement of Brain Injury Indices Using Brain CT Images, Injury Data, and Machine Learning 审中-公开
    使用脑CT图像，损伤数据和机器学习自动测量脑损伤指数

    公开(公告)号：US20120184840A1

    公开(公告)日：2012-07-19

    申请号：US13383351

    申请日：2010-03-17

    申请人： Kayvan Najarian ,  Wenan Chen ,  Kevin R. Ward

    发明人： Kayvan Najarian ,  Wenan Chen ,  Kevin R. Ward

    IPC分类号： A61B6/00 ,  A61B6/03 ,  G06K9/62 ,  A61B5/055

    CPC分类号： G06K9/629 ,  G06F19/321 ,  G06K2209/055 ,  G06T7/0012 ,  G06T7/42 ,  G06T7/68 ,  G06T2207/10081 ,  G06T2207/20056 ,  G06T2207/20064 ,  G06T2207/30016 ,  G16H50/20 ,  Y02A90/26

    摘要： A decision-support system and computer implemented method automatically measures tee midline shift in a patient's brain using Computed Tomography (CT) images. The decision-support system and computer implemented method applies machine learning methods to features extracted from multiple sources, including midline shift, blood amount, texture pattern and other injury data, to provide a physician an estimate of intracranial pressure (ICP) levels. A hierarchical segmentation method, based on Gaussian Mixture Mode! (GMM), is used. In this approach, first an Magnetic Resonance Image (MRI) ventricle template, as prior knowledge, is used to estimate the region for each ventricle. Then, by matching the ventricle shape it) CT images to fee MRI ventricle template set, the corresponding MRI slice is selected. From the shape matching result, the feature points for midline estimation in CT slices, such as the center edge points of the lateral ventricles, are detected. The amount of shift, along with other information such as brain tissue texture features, volume of blood accumulated in the brain, patient demographics, injury information, and features extracted from physiological signals, are used to train a machine learning method to predict a variety of important clinical factors, such as intracranial pressure (ICP), likelihood of success a particular treatment, and the need and/or dosage of particular drugs.

    摘要翻译： 决策支持系统和计算机实现的方法使用计算机断层摄影（CT）图像自动测量患者大脑中的三通移位。 决策支持系统和计算机实现方法将机器学习方法应用于从多个来源提取的特征，包括中线移位，血量，纹理模式和其他伤害数据，为医师提供颅内压（ICP）水平的估计。 基于高斯混合模式的分层分割方法！ （GMM）。 在这种方法中，首先使用磁共振图像（MRI）心室模板作为现有知识来估计每个心室的区域。 然后，通过匹配心室形状，CT图像以MRI脑室模板设置，选择相应的MRI切片。 从形状匹配结果中，检测CT切片中线估计的特征点，例如侧脑室的中心边缘点。 移动量与脑组织纹理特征，脑中蓄积的血量，患者人口统计学，损伤信息以及从生理信号中提取的特征等其他信息用于训练机器学习方法，以预测各种 重要的临床因素，如颅内压（ICP），特定治疗成功的可能性，以及特定药物的需要和/或剂量。

    Automated measurement of brain injury indices using brain CT images, injury data, and machine learning
    6.
    发明授权
    Automated measurement of brain injury indices using brain CT images, injury data, and machine learning 有权

    公开(公告)号：US10303986B2

    公开(公告)日：2019-05-28

    申请号：US13383351

    申请日：2010-03-17

    申请人： Kayvan Najarian ,  Wenan Chen ,  Kevin R. Ward

    发明人： Kayvan Najarian ,  Wenan Chen ,  Kevin R. Ward

    IPC分类号： A61B5/00 ,  G06K9/62 ,  G06T7/00 ,  G06T7/42 ,  G06T7/68 ,  G06F19/00 ,  G16H50/20

    摘要： A decision-support system and computer implemented method automatically measures the midline shift in a patient's brain using Computed Tomography (CT) images. The decision-support system and computer implemented method applies machine learning methods to features extracted from multiple sources, including midline shift, blood amount, texture pattern and other injury data, to provide a physician an estimate of intracranial pressure (ICP) levels. A hierarchical segmentation method, based on Gaussian Mixture Model (GMM), is used. In this approach, first an Magnetic Resonance Image (MRI) ventricle template, as prior knowledge, is used to estimate the region for each ventricle. Then, by matching the ventricle shape in CT images to the MRI ventricle template set, the corresponding MRI slice is selected. From the shape matching result, the feature points for midline estimation in CT slices, such as the center edge points of the lateral ventricles, are detected. The amount of shift, along with other information such as brain tissue texture features, volume of blood accumulated in the brain, patient demographics, injury information, and features extracted from physiological signals, are used to train a machine learning method to predict a variety of important clinical factors, such as intracranial pressure (ICP), likelihood of success a particular treatment, and the need and/or dosage of particular drugs.

    Accurate Pelvic Fracture Detection for X-Ray and CT Images
    8.
    发明申请
    Accurate Pelvic Fracture Detection for X-Ray and CT Images 有权
    X射线和CT图像的准确骨盆骨折检测

    公开(公告)号：US20120143037A1

    公开(公告)日：2012-06-07

    申请号：US13255542

    申请日：2010-03-17

    申请人： Kayvan Najarian ,  Simina Vasilache ,  Rebecca Smith ,  Kevin R. Ward

    发明人： Kayvan Najarian ,  Simina Vasilache ,  Rebecca Smith ,  Kevin R. Ward

    IPC分类号： A61B6/00 ,  G06F15/18

    CPC分类号： G06T7/0012 ,  A61B6/032 ,  A61B6/505 ,  A61B6/5217 ,  A61B6/5235 ,  G06T7/11 ,  G06T2207/10081 ,  G06T2207/10116 ,  G06T2207/20212 ,  G06T2207/30008

    摘要： Accurate pelvic fracture detection is accomplished with automated X-ray and Computed Tomography (CT) images for diagnosis and recommended therapy. The system combines computational methods to process images from two different modalities, using Active Shape Model (ASM), spline interpolation, active contours, and wavelet transform. By processing both X-ray and CT images, features which may be visible under one modality and not under the other are extracted and validates and confirms information visible in both. The X-ray component uses hierarchical approach based on directed Hough Transform to detect pelvic structures, removing the need for manual initialization. The X-ray component uses cubic spline interpolation to regulate ASM deformation during X-ray image segmentation. Key regions of the pelvis are first segmented and identified, allowing detection methods to be specialized to each structure using anatomical knowledge. The CT processing component is able to distinguish bone from other non-bone objects with similar visual characteristics, such a blood and contrast fluid, permitting detection and quantification of soft tissue hemorrhage. The CT processing component draws attention to slices where irregularities are detected, reducing the time to fully examine a pelvic CT scan. The quantitative measurement of bone displacement and hemorrhage area are used as input for a trauma decision-support system, along with physiological signals, injury details and demographic information.

    摘要翻译： 使用自动X射线和计算机断层扫描（CT）图像进行准确的骨盆骨折检测，用于诊断和推荐治疗。 该系统结合计算方法来处理来自两种不同模态的图像，使用主动形状模型（ASM），样条插值，主动轮廓和小波变换。 通过处理X射线和CT图像，可以提取并验证在一种模式下不可见的特征，并验证两者中可见的信息。 X射线组件使用基于定向霍夫变换的分层方法来检测骨盆结构，消除对手动初始化的需要。 X射线成分使用三次样条插值来调节X射线图像分割过程中的ASM变形。 骨盆的关键区域首先被分割和识别，从而允许使用解剖学知识来检测各种结构的检测方法。 CT处理组件能够将具有类似视觉特征的骨骼与其他具有类似视觉特征的非骨骼物体（如血液和造影剂）区分开，从而允许检测和定量软组织出血。 CT处理组件提请注意检测不规则的切片，减少完全检查盆腔CT扫描的时间。 骨移位和出血面积的定量测量用作创伤决策支持系统的输入，以及生理信号，损伤细节和人口统计信息。

    Accurate pelvic fracture detection for X-ray and CT images
    10.
    发明授权
    Accurate pelvic fracture detection for X-ray and CT images 有权
    X线和CT图像的准确骨盆骨折检测

    公开(公告)号：US08538117B2

    公开(公告)日：2013-09-17

    申请号：US13255542

    申请日：2010-03-17

    申请人： Kayvan Najarian ,  Simina Vasilache ,  Rebecca Smith ,  Kevin R. Ward

    发明人： Kayvan Najarian ,  Simina Vasilache ,  Rebecca Smith ,  Kevin R. Ward

    IPC分类号： G06K9/00 ,  A61B6/00 ,  A61B5/05

    CPC分类号： G06T7/0012 ,  A61B6/032 ,  A61B6/505 ,  A61B6/5217 ,  A61B6/5235 ,  G06T7/11 ,  G06T2207/10081 ,  G06T2207/10116 ,  G06T2207/20212 ,  G06T2207/30008

    摘要： Accurate pelvic fracture detection is accomplished with automated X-ray and Computed Tomography (CT) images for diagnosis and recommended therapy. The system combines computational methods to process images from two different modalities, using Active Shape Model (ASM), spline interpolation, active contours, and wavelet transform. By processing both X-ray and CT images, features which may be visible under one modality and not under the other are extracted and validates and confirms information visible in both. The X-ray component uses hierarchical approach based on directed Hough Transform to detect pelvic structures, removing the need for manual initialization. The X-ray component uses cubic spline interpolation to regulate ASM deformation during X-ray image segmentation. Key regions of the pelvis are first segmented and identified, allowing detection methods to be specialized to each structure using anatomical knowledge. The CT processing component is able to distinguish bone from other non-bone objects with similar visual characteristics, such a blood and contrast fluid, permitting detection and quantification of soft tissue hemorrhage. The CT processing component draws attention to slices where irregularities are detected, reducing the time to fully examine a pelvic CT scan. The quantitative measurement of bone displacement and hemorrhage area are used as input for a trauma decision-support system, along with physiological signals, injury details and demographic information.

    摘要翻译： 使用自动X射线和计算机断层扫描（CT）图像进行准确的骨盆骨折检测，用于诊断和推荐治疗。 该系统结合计算方法来处理来自两种不同模态的图像，使用主动形状模型（ASM），样条插值，主动轮廓和小波变换。 通过处理X射线和CT图像，可以提取并验证在一种模式下不可见的特征，并验证两者中可见的信息。 X射线组件使用基于定向霍夫变换的分层方法来检测骨盆结构，消除对手动初始化的需要。 X射线成分使用三次样条插值来调节X射线图像分割过程中的ASM变形。 骨盆的关键区域首先被分割和识别，从而允许使用解剖学知识来检测各种结构的检测方法。 CT处理组件能够将具有类似视觉特征的骨骼与其他具有类似视觉特征的非骨骼物体（如血液和造影剂）区分开，从而允许检测和定量软组织出血。 CT处理组件提请注意检测不规则的切片，减少完全检查骨盆CT扫描的时间。 骨移位和出血面积的定量测量用作创伤决策支持系统的输入，以及生理信号，损伤细节和人口统计信息。