Human-centered CT-based CADx Tools for Traumatic Torso Hemorrhage
以人为中心、基于 CT 的 CADx 工具,用于治疗躯干外伤出血
基本信息
- 批准号:10566836
- 负责人:
- 金额:$ 37.64万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-01 至 2027-08-31
- 项目状态:未结题
- 来源:
- 关键词:AbdomenAccelerationAcidosisAdmission activityAffectAgeAlgorithmsAmericanAngiographyArtificial IntelligenceBehaviorBig DataBloodBlood Coagulation DisordersBlood PressureBody RegionsCessation of lifeChestClassificationClinicalCollaborationsComputer-Assisted DiagnosisConsumptionDataData SetDatabasesDecision MakingDecision Support SystemsDelphi StudyDetectionDevelopmentDiagnosisDiagnostic ErrorsEarly InterventionEngineeringEnsureExpert OpinionExtravasationFailureFatigueFeedbackFutureGoalsHeart RateHematomaHemorrhageHemostatic AgentsHemothoraxHourHumanImageImage AnalysisInjuryInterventionKnowledgeLabelLacerationLifeLiquid substanceMeasurementMedical ImagingModalityMonitorMulti-Institutional Clinical TrialOperative Surgical ProceduresOrganOutcomePatient TriagePatientsPelvisPerformancePeritonealPhysiologicalPleuralPopulationPopulation HeterogeneityPrevalenceProcessProtocols documentationProviderRadiology SpecialtyReaderReadingRecommendationReportingResearchResourcesRestResuscitationRiskSafetySamplingScreening procedureSeveritiesShockSoftware DesignSolidSourceSpeedStandardizationSystemTask PerformancesTestingTimeTransfusionTraumaTrustUnited StatesValidationVisualizationWorkalgorithm developmentbody systemclassification algorithmclinical decision-makingclinically actionableclinically relevantcohortcomputer aided detectioncomputer centerdesigndiagnostic tooldisorder riskdistractionhemodynamicshuman centered designhuman-in-the-loopimage processingimprovedindexinglarge datasetsmortalitynatural hypothermianovelorgan injurypelvis fracturepersonalized decisionpersonalized diagnosticspoint of carepredict clinical outcomepreferencepreventpreventable deathrapid detectionresiliencescreeningtooltrauma caretrauma centers
项目摘要
Trauma is responsible for 180,000 deaths annually in the United States and accounts for 59% of deaths in the
population younger than 45 years. 86% of preventable deaths are related to sequalae of massive torso
hemorrhage. Rapid precision diagnostic tools are needed to triage patients for early activation of massive
transfusion protocols and urgent surgical or angiographic hemostatic intervention to circumvent the vicious
cycle of acidosis, coagulopathy, hypothermia, and death resulting from exsanguination. Whole-body CT
angiography (WBCTA) is the workhorse screening and surgical planning modality for torso hemorrhage. Lethal
but preventable hemorrhage typically arises from pelvic fractures and solid organ lacerations, manifesting on
WBCTA as foci of contrast extravasation and pooled cavitary hemorrhage (e.g., pelvic hematoma, hemothorax,
or hemoperitoneum). Rapid assessment of WBCTA can result in earlier intervention, with associated survival
benefit, but reader fatigue, study volume, reading room distractions, and injuries involving multiple body
regions remain sources of diagnostic error and interpretation delays. Assessment of organ injury severity,
pelvic fracture severity, and overall hemorrhage burden remains reader dependent and subjective. Clinical
tools including the Shock Index have many confounders that impede forecasting of actionable hemorrhage-
related outcomes. Automated WBCTA computer aided diagnosis (CADx) tools that detect bleeding pelvic
fractures and organ lacerations, classify severity grade, and deliver precise voxelwise volumetric
measurements of multicavitary hemorrhage burden will greatly accelerate and standardize image analysis,
reduce turnaround time for reporting of critical results, improve the accuracy and objectivity of clinical decision
making, and ultimately reduce time to life-saving hemorrhage control interventions. To capitalize on the
benefits of automated point-of-care CT-based CADx tools in the fast-paced, and safety critical trauma care
setting, such tools must be rapid, accurate, generalizable, and elicit a high level of end-user trust. To minimize
bias, ensure clinical utility, and maximize robustness for turn-key deployment in future multicenter clinical trials,
the tools must scale to large diverse populations, and achieve human factors engineering goals established
through expert target user input. Our team will bring to bear combined technical and clinical expertise in trauma
radiology, medical image processing, and human-centered software design to create an orchestrated suite of
rapid, accurate, clinically relevant, and user-centered CADx tools for torso hemorrhage. In Aim 1, we will curate
and annotate a uniquely large dataset of consecutively selected admission trauma WBCTAs. In Aim 2, we will
use this big data approach and human-centered design principles to develop a suite of interactive high-trust
CADx tools. In Aim 3, we will assess generalizability with a large out-of-sample dataset and assess user
acceptance with simulated deployment. The work will result in rapid, robust, and human-centered CADx tools
for detection, precision diagnostics, and personalized decision support for hemorrhage-control interventions.
创伤每年在美国造成18万人死亡,占美国死亡人数的59%
年龄在45岁以下的人口。86%的可预防死亡与巨大躯干的后遗症有关
大出血。需要快速精确的诊断工具来对患者进行分类,以便早期激活大量
输血方案和紧急手术或血管造影止血干预以避免恶性
酸中毒、凝血障碍、体温过低和因失血而死亡的循环。全身CT
血管造影术(WBCTA)是躯干出血的主要筛查和手术计划方法。致死
但可预防的出血通常由骨盆骨折和实体器官撕裂引起,表现为
WBCTA是造影剂渗出和空洞性出血(如盆腔血肿、血胸、
或腹膜积血)。快速评估WBCTA可以导致更早的干预,并伴随着相关的生存
好处,但读者疲劳,学习数量,阅览室分心,以及涉及多个身体的伤害
各区域仍然是诊断错误和口译延误的根源。器官损伤严重程度的评估,
骨盆骨折的严重程度和总体出血负担仍然取决于读者和主观。临床
包括休克指数在内的工具有许多混杂因素,阻碍了对可操作出血的预测-
相关结果。用于检测盆腔出血的自动化WBCTA计算机辅助诊断(CADx)工具
骨折和器官撕裂,分类严重程度,并提供精确的体素体积测量
多腔出血负荷的测量将大大加快和规范图像分析,
缩短报告关键结果的周转时间,提高临床决策的准确性和客观性
制定、最终减少救命时间的出血控制干预措施。要充分利用
基于CT的自动化护理点CADx工具在快节奏和安全的危重创伤护理中的优势
在这种情况下,这些工具必须快速、准确、可推广,并获得高水平的最终用户信任。要最小化
偏差,确保临床实用性,并最大限度地提高在未来多中心临床试验中进行交钥匙部署的健壮性,
这些工具必须扩展到大量不同的人群,并实现既定的人因工程目标
通过专家目标用户输入。我们的团队将结合创伤的技术和临床专业知识
放射学、医学图像处理和以人为中心的软件设计,以创建一套协调的
用于躯干出血的快速、准确、临床相关和以用户为中心的CADx工具。在目标1中,我们将主持
并对连续选择的入院创伤WBCTA的唯一大型数据集进行注释。在目标2中,我们将
使用这种大数据方法和以人为中心的设计原则来开发一套交互式高信任
CADx工具。在目标3中,我们将使用大型样本外数据集评估泛化能力,并评估用户
接受模拟部署。这项工作将产生快速、健壮和以人为中心的CADx工具
用于出血控制干预的检测、精确诊断和个性化决策支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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David Dreizin其他文献
David Dreizin的其他文献
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{{ truncateString('David Dreizin', 18)}}的其他基金
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
- 批准号:
10189581 - 财政年份:2019
- 资助金额:
$ 37.64万 - 项目类别:
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
- 批准号:
10471193 - 财政年份:2019
- 资助金额:
$ 37.64万 - 项目类别:
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