Machine learning-based segmentation and risk modeling for real-time prediction of major arterial bleeding after pelvic fractures

基于机器学习的分割和风险建模，用于实时预测骨盆骨折后大动脉出血

基本信息

批准号：
10471193
负责人：
David Dreizin
金额：
$ 18.62万
依托单位：
UNIVERSITY OF MARYLAND BALTIMORE
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10471193
关键词：
Admission activity Adoption Algorithms Angiography Architecture Area Arterial Injury Award Blunt Trauma Caliber Catheters Cause of Death Clinical Communities Comparative Effectiveness Research Computer Models Computer Vision Systems Computer software Crush Injury Data Data Science Data Set Derivation procedure Detection Development Diagnosis Early Intervention Engineering Environment Extravasation Funding Goals Hematoma Hemorrhage Hospitalization Human Image Industry Intervention Intuition K-Series Research Career Programs Knowledge Label Lead Learning Life Logistic Regressions Machine Learning Manuals Maryland Measurement Measures Medical Imaging Mentors Methods Modeling Modernization Nature Obesity Outcome Patients Pelvis Predictive Value Probability Theory Process Programming Languages Pythons Radiology Specialty Research Research Activity Research Personnel Resources Risk Scanning Sensitivity and Specificity Shorthand Speed Supervision Techniques Terminology Testing Therapeutic Embolization Thinness Time Training Transfusion Translations Trauma Treatment outcome Triage Universities Vehicle crash Work X-Ray Computed Tomography adverse outcome algorithm development artificial neural network automated segmentation base clinical decision-making computer infrastructure convolutional neural network cost deep learning deep learning algorithm experience fall injury hemodynamics heuristics image processing imaging Segmentation improved improved outcome learning strategy machine learning model medical schools mortality multidisciplinary muscle form neural network architecture outcome prediction pelvis fracture personalized predictions predictive modeling prevent primary outcome radiologist random forest real time model risk prediction secondary outcome segmentation algorithm skills standard of care support vector machine temporal measurement tool

项目摘要

PROJECT SUMMARY/ABSTRACT: Arterial hemorrhage after pelvic fractures is a leading reversible cause of death after blunt trauma. Prediction of arterial bleeding risk is difficult, and currently determined using subjective criteria, often based on qualitative results of admission computed tomography (CT). Segmented hematoma and contrast extravasation (CE) volumes predict need for angioembolization, major transfusion, and mortality but cannot be applied in real-time. The ill-defined multi-focal nature of pelvic hematomas and CE prevents reliable estimation using diameter-based measurements. Dr. Dreizin is a trauma radiologist at the University of Maryland School of Medicine. His early work has focused on improving the speed and reliability of volumetric analysis of pelvic hematomas using semi-automated techniques, and derivation of a logistic regression-based prediction tool for major arterial injury after pelvic fractures. Dr. Dreizin’s goal for this four- year K08 mentored career development award proposal is to gain the skills needed to 1) implement deep learning architectures for automated hematoma volume segmentation and 2) develop computational models for outcome prediction after pelvic trauma. These tools could greatly improve the speed and accuracy of clinical decision making in the setting of life-threatening traumatic pelvic bleeding. Fully convolutional neural networks (FCNs) have emerged as the most robust and scalable method for automated medical image segmentation. Intuitive software platforms for training FCN implementations and generating multivariable machine learning models have been developed in the Python programming environment. The training objectives and research activities of this proposal are necessary to provide Dr. Dreizin with new skills and practical experience in Python programming, deep learning software, and computational modeling software. By understanding the principles and computational infrastructure behind modern machine learning, Dr. Dreizin will be able to train and validate state-of-the-art algorithms independently and effectively lead a team of researchers in this area. To achieve his goals, Dr. Dreizin has assembled a multidisciplinary team of mentors, advisors, and collaborators with world-leading expertise in computer vision in medical imaging, probability theory, data science, and comparative effectiveness research. Dr. Dreizin will focus on two specific aims. In Aim 1, he will train and validate deep learning architectures for segmentation of traumatic pelvic hematomas and CE by computing the Dice metric, time effort, and correlation with clinical outcomes. In Aim 2, he will generate and test quantitative models for predicting major arterial bleeding after pelvic trauma based on a rich multi-label dataset of segmented features. The training and pilot data will be necessary for Dr. Dreizin’s long- term goal of research independence and R01 support to develop automated segmentation algorithms for the spectrum of clinically important imaging features after pelvic trauma, as well as fully automated multivariable clinical prediction tools with potential for translation to industry and as an FDA-cleared product.

项目摘要/摘要：骨盆骨折后的动脉出血是可逆的主要原因钝创伤后死亡。预测动脉出血风险是困难的，目前使用主观标准，通常基于入院计算机断层扫描（CT）的定性结果。分段血肿和对比度渗出（CE）体积预测需要血管栓塞，重大输血和死亡率，但不能实时应用。骨盆血肿和CE不确定的多焦点性质使用基于直径的测量结果防止可靠的估计。 Dreizin博士是一名创伤放射科医生马里兰大学医学院。他的早期工作重点是提高使用半自动化技术对骨盆血肿的体积分析，并推导逻辑基于回归的预测工具，用于骨盆骨折后重大动脉损伤。 Dreizin博士的目标是 K08年的指导职业发展奖提案是获得所需的技能1）自动血肿量细分的学习体系结构和2）开发计算模型为了预测骨盆创伤后。这些工具可以大大提高在威胁生命的骨盆出血的情况下，临床决策。完全卷积神经网络（FCN）已成为自动化医学图像的最强大，最可扩展的方法分割。直观的软件平台用于培训FCN实施并生成多变量机器学习模型是在Python编程环境中开发的。培训该提案的目标和研究活动对于为Dreizin博士提供新技能和 Python编程，深度学习软件和计算建模软件方面的实践经验。经过了解现代机器学习背后的原理和计算基础设施，Dreizin博士将能够独立并有效地领导一个团队该领域的研究人员。为了实现他的目标，Dreizin博士组建了一个多学科的导师团队，顾问和合作者在医学成像方面具有世界领先的专业知识，概率理论，数据科学和比较有效性研究。 Dreizin博士将重点关注两个具体目标。在 AIM 1，他将训练和验证深度学习体系结构，以分割创伤性骨盆血肿和CE通过计算骰子指标，时间努力以及与临床结果的相关性。在AIM 2中，他会生成和测试定量模型，用于预测基于丰富的骨盆创伤后主要的动脉出血分段特征的多标签数据集。 Dreizin博士长期需要培训和试点数据研究独立性的术语目标和R01支持，以开发自动分割算法骨盆创伤后的临床重要成像特征的光谱以及全自动的多变量临床预测工具具有转换为行业的潜力和作为FDA清除产品的工具。

项目成果

期刊论文数量（24）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation With Limited Data.

DOI：
10.1109/tmi.2021.3139637
发表时间：
2022-06
期刊：
IEEE transactions on medical imaging
影响因子：
10.6
作者：
通讯作者：

CT of Sacral Fractures: Classification Systems and Management.

DOI：
10.1148/rg.220075
发表时间：
2022-11
期刊：
Radiographics : a review publication of the Radiological Society of North America, Inc
影响因子：
0
作者：
通讯作者：

Accelerating voxelwise annotation of cross-sectional imaging through AI collaborative labeling with quality assurance and bias mitigation.

通过 AI 协作标记加速横截面成像的体素注释，并提供质量保证和偏差缓解。

DOI：
10.3389/fradi.2023.1202412
发表时间：
2023
期刊：
Frontiers in radiology
影响因子：
0
作者：
Dreizin,David;Zhang,Lei;Sarkar,Nathan;Bodanapally,UttamK;Li,Guang;Hu,Jiazhen;Chen,Haomin;Khedr,Mustafa;Khetan,Udit;Campbell,Peter;Unberath,Mathias
通讯作者：
Unberath,Mathias

Pulmonary contusion: automated deep learning-based quantitative visualization.

DOI：
10.1007/s10140-023-02149-2
发表时间：
2023-08
期刊：
EMERGENCY RADIOLOGY
影响因子：
2.2
作者：
Sarkar, Nathan;Zhang, Lei;Campbell, Peter;Liang, Yuanyuan;Li, Guang;Khedr, Mustafa;Khetan, Udit;Dreizin, David
通讯作者：
Dreizin, David

Toward automated interpretable AAST grading for blunt splenic injury.