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1. 发明申请

US20150065815A1 ASSESSMENT AND PREDICTION OF CARDIOVASCULAR STATUS DURING CARDIAC ARREST AND THE POST-RESUSCITATION PERIOD USING SIGNAL PROCESSING AND MACHINE LEARNING 有权
标题翻译：使用信号处理和机器学习在心脏病和心脏恢复期间评估和预测心脏血管状态
公开(公告)号：US20150065815A1
公开(公告)日：2015-03-05
申请号：US14353736
申请日：2012-05-25
申请人： Kayvan Najarian , Sharad Shandilya , Kevin R. Ward
发明人： Kayvan Najarian , Sharad Shandilya , Kevin R. Ward
IPC分类号： A61B5/00 , A61B5/1455 , A61B5/04 , A61B5/0205 , A61B5/0402
CPC分类号： A61B5/7267 , A61B5/0205 , A61B5/04017 , A61B5/0402 , A61B5/0452 , A61B5/053 , A61B5/0836 , A61B5/14551 , A61B5/4836 , A61B5/486 , A61B5/7203 , A61B5/725 , A61B5/726 , A61B2505/01 , A61N1/3925 , G06F19/00 , G16H40/63 , G16H50/50
摘要： Real-time, short-term analysis of ECG, by using multiple signal processing and machine learning techniques, is used to determine counter shock success in defibrillation. Combinations of measures when used with machine learning algorithms readily predict successful resuscitation, guide therapy and predict complications. In terms of guiding resuscitation, they may serve as indicators and when to provide counter shocks and at what energy levels they should be provided as well as to serve as indicators of when certain drugs should be provided (in addition to their doses). For cardiac arrest, the system is meant to run in real time during all current resuscitation procedures including post-resuscitation care to detect deterioration for guiding care such as therapeutic hypothermia.
摘要翻译：通过使用多重信号处理和机器学习技术对ECG进行实时，短期分析，用于确定除颤中的反冲击成功。与机器学习算法一起使用的措施的组合容易预测成功的复苏，指导治疗和预测并发症。在指导性复苏方面，它们可以作为指标，何时提供反击，以及应该提供什么能量级别，以及作为何时应提供某些药物的指标（除了其剂量之外）。对于心脏停搏，该系统旨在在所有当前复苏程序期间实时运行，包括复苏后护理以检测指导护理（例如治疗性低温）的恶化。

2. 发明授权

US09339241B2 Assessment and prediction of cardiovascular status during cardiac arrest and the post-resuscitation period using signal processing and machine learning 有权
标题翻译：使用信号处理和机器学习评估和预测心脏停搏和复苏后的心血管状态
公开(公告)号：US09339241B2
公开(公告)日：2016-05-17
申请号：US14353736
申请日：2012-05-25
申请人： Kayvan Najarian , Sharad Shandilya , Kevin R. Ward
发明人： Kayvan Najarian , Sharad Shandilya , Kevin R. Ward
IPC分类号： A61B5/00 , A61N1/37 , A61B5/083 , G06F19/00 , A61B5/0452 , A61B5/0205 , A61B5/04 , A61B5/053 , A61B5/1455 , A61B5/0402 , A61N1/39
CPC分类号： A61B5/7267 , A61B5/0205 , A61B5/04017 , A61B5/0402 , A61B5/0452 , A61B5/053 , A61B5/0836 , A61B5/14551 , A61B5/4836 , A61B5/486 , A61B5/7203 , A61B5/725 , A61B5/726 , A61B2505/01 , A61N1/3925 , G06F19/00 , G16H40/63 , G16H50/50
摘要： Real-time, short-term analysis of ECG, by using multiple signal processing and machine learning techniques, is used to determine counter shock success in defibrillation. Combinations of measures when used with machine learning algorithms readily predict successful resuscitation, guide therapy and predict complications. In terms of guiding resuscitation, they may serve as indicators and when to provide counter shocks and at what energy levels they should be provided as well as to serve as indicators of when certain drugs should be provided (in addition to their doses). For cardiac arrest, the system is meant to run in real time during all current resuscitation procedures including post-resuscitation care to detect deterioration for guiding care such as therapeutic hypothermia.
摘要翻译：通过使用多重信号处理和机器学习技术对ECG进行实时，短期分析，用于确定除颤中的反冲击成功。与机器学习算法一起使用的措施的组合容易预测成功的复苏，指导治疗和预测并发症。在指导性复苏方面，它们可以作为指标，何时提供反击，以及应该提供什么能量级别，以及作为何时应提供某些药物（除了其剂量）之外的指标。对于心脏停搏，该系统旨在在所有当前复苏程序期间实时运行，包括复苏后护理以检测指导护理（例如治疗性低温）的恶化。

3. 发明授权

US08805051B2 Image processing and machine learning for diagnostic analysis of microcirculation 有权
标题翻译：图像处理和机器学习用于微循环诊断分析
公开(公告)号：US08805051B2
公开(公告)日：2014-08-12
申请号：US13259414
申请日：2010-03-17
申请人： Kayvan Najarian , Rosalyn Stacy Hobson , Kevin R. Ward , Sumeyra Ummuhan Demir Kanik , Nazanin Mirshahi
发明人： Kayvan Najarian , Rosalyn Stacy Hobson , Kevin R. Ward , Sumeyra Ummuhan Demir Kanik , Nazanin Mirshahi
IPC分类号： G06K9/00 , G06T7/00 , A61B5/026 , A61B5/00 , A61B5/02 , G06F19/00 , G06T7/20
CPC分类号： A61B5/0261 , A61B5/02007 , A61B5/412 , A61B5/726 , G06F19/00 , G06F19/321 , G06T7/0012 , G06T7/11 , G06T7/136 , G06T7/174 , G06T7/254 , G06T2207/10016 , G06T2207/20081 , G06T2207/30104 , G16H50/20
摘要： Automated quantitative analysis of microcirculation, such as density of blood vessels and red blood cell velocity, is implemented using image processing and machine learning techniques. Detection and quantification of the microvasculature is determined from images obtained through intravital microscopy. The results of quantitatively monitoring and assessing the changes that occur in microcirculation during resuscitation period assist physicians in making diagnostically and therapeutically important decisions such as determination of the degree of illness as well as the effectiveness of the resuscitation process. Advanced digital image processing methods are applied to provide quantitative assessment of video signals for detection and characterization of the microvasculature (capillaries, venules, and arterioles). The microvasculature is segmented, the presence and velocity of Red Blood Cells (RBCs) is estimated, and the distribution of blood flow in capillaries is identified for a variety of normal and abnormal cases.
摘要翻译：使用图像处理和机器学习技术实现微循环的自动定量分析，例如血管密度和红细胞速度。从通过活体显微镜获得的图像确定微血管的检测和定量。定量监测和评估在复苏期间微循环中发生的变化的结果有助于医生进行诊断和治疗上重要的决定，例如确定疾病程度以及复苏过程的有效性。应用先进的数字图像处理方法来提供视频信号的定量评估，用于检测和表征微血管（毛细血管，小静脉和小动脉）。对微血管进行分段，估计红细胞（RBC）的存在和速度，并针对各种正常和异常病例鉴定毛细血管中血流的分布情况。

4. 发明申请

US20120184840A1 Automated Measurement of Brain Injury Indices Using Brain CT Images, Injury Data, and Machine Learning 审中-公开
标题翻译：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。

5. 发明授权

US10303986B2 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.

6. 发明申请

US20120143037A1 Accurate Pelvic Fracture Detection for X-Ray and CT Images 有权
标题翻译： 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。

7. 发明申请

US20120136224A1 Combining Predictive Capabilities of Transcranial Doppler (TCD) with Electrocardiogram (ECG) to Predict Hemorrhagic Shock 失效
标题翻译：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。

8. 发明授权

US08762308B2 Combining predictive capabilities of Transcranial Doppler (TCD) with Electrocardiogram (ECG) to predict hemorrhagic shock 失效
标题翻译：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。

9. 发明授权

US08538117B2 Accurate pelvic fracture detection for X-ray and CT images 有权
$Accurate pelvic fracture detection for X-ray and CT images$
标题翻译： 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。

10. 发明申请

US20120269420A1 Image Processing and Machine Learning for Diagnostic Analysis of Microcirculation 有权
标题翻译：图像处理和机器学习微循环诊断分析
公开(公告)号：US20120269420A1
公开(公告)日：2012-10-25
申请号：US13259414
申请日：2010-03-17
申请人： Kayvan Najarian , Rosalyn Stacy Hobson , Kevin R. Ward , Sumeyra Ummuhan Demir Kanik , Nazanin Mirshahi
发明人： Kayvan Najarian , Rosalyn Stacy Hobson , Kevin R. Ward , Sumeyra Ummuhan Demir Kanik , Nazanin Mirshahi
IPC分类号： G06K9/62
CPC分类号： A61B5/0261 , A61B5/02007 , A61B5/412 , A61B5/726 , G06F19/00 , G06F19/321 , G06T7/0012 , G06T7/11 , G06T7/136 , G06T7/174 , G06T7/254 , G06T2207/10016 , G06T2207/20081 , G06T2207/30104 , G16H50/20
摘要： Automated quantitative analysis of microcirculation, such as density of blood vessels and red blood cell velocity, is implemented using image processing and machine learning techniques. Detection and quantification of the microvasculature is determined from images obtained through intravital microscopy. The results of quantitatively monitoring and assessing the changes that occur in microcirculation during resuscitation period assist physicians in making diagnostically and therapeutically important decisions such as determination of the degree of illness as well as the effectiveness of the resuscitation process. Advanced digital image processing methods are applied to provide quantitative assessment of video signals for detection and characterization of the microvasculature (capillaries, venules, and arterioles). The microvasculature is segmented, the presence and velocity of Red Blood Cells (RBCs) is estimated, and the distribution of blood flow in capillaries is identified for a variety of normal and abnormal cases.
摘要翻译：使用图像处理和机器学习技术实现微循环的自动定量分析，例如血管密度和红细胞速度。从通过活体显微镜获得的图像确定微血管的检测和定量。定量监测和评估在复苏期间微循环中发生的变化的结果有助于医生进行诊断和治疗上重要的决定，例如确定疾病程度以及复苏过程的有效性。应用先进的数字图像处理方法来提供视频信号的定量评估，用于检测和表征微血管（毛细血管，小静脉和小动脉）。对微血管进行分段，估计红细胞（RBC）的存在和速度，并针对各种正常和异常病例鉴定毛细血管中血流的分布情况。

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