Accurate and Reliable Diagnostics for Injured Children: Machine Learning for Ultrasound

项目摘要/摘要 Aaron Kornblith博士，加州大学旧金山分校的全科和儿科急诊医生 (加州大学旧金山分校)正在将自己确立为以患者为导向的新诊断临床研究的未来研究员 在受伤的儿童身上。这一奖项将使他能够实现以下目标：(1)成为患者方面的专家- 面向儿童腹部创伤的临床研究；(2)开发新的机器学习模型 床边超声应用；(3)实施先进的计算方法来开发、验证和测试 结合床边超声的临床决策规则；以及(4)发展独立的临床研究事业。 为了实现这些目标，科恩布利斯博士组建了一个专家指导团队：主要导师杰弗里博士 加州大学旧金山分校儿科重症监护科主任芬曼(对危重儿童进行临床调查 是早期调查人员职业发展方面的专家)，共同导师Atul Butte博士(专家 医疗保健和数据科学)、James Holmes博士和Nathan Kupmann博士(诊断评估专家 儿科创伤和临床决策规则)，科学顾问John Munan博士，(开发专家， 验证和实施用于成像任务的机器学习)，以及统计顾问余斌博士(专家 在统计理论中，包括准确、可靠和可解释的计算方法，以及隐含的偏差)。 腹部钝性损伤出血是导致儿童死亡的主要原因。辨证腹部 早期出血对于减少延迟或漏诊造成的发病率和死亡率至关重要。这个 参考标准测试，腹部计算机断层扫描(CT)，有缺点，包括辐射风险- 诱发恶变。25年来，CT在儿童中的使用显著增加，不成比例 在结果方面有所改善。创伤的超声聚焦评估(FAST)是一种床边超声 方法对儿童腹部出血进行评估。FAST可帮助临床医生平衡漏诊风险 腹部损伤，不必要地暴露在CT电离辐射中。科恩布利斯博士的研究将集中于 利用机器学习模型提高儿科FAST的准确性和可靠性(目标1)和 开发/验证结合FAST的新临床决策规则以识别极低受伤风险的儿童 谁可以放弃CT(目标2)。Kornblith博士将使用现有的数据集和计算基础设施来开发和 使用来自1,264个儿科FAST研究的210万帧验证机器学习模型，以检测 出血与专家一样准确(目标1)，以及两个预先存在的数据集，以开发和验证新的 结合FAST的临床决策规则，并将其性能与现有临床决策规则(AIM)进行比较 2)。拟议的研究和培训计划将使Kornblith博士具有过渡的跨学科技能 独立提交一份具有竞争力的R01，重点是改进和验证新的临床决策 融合先进计算方法的规则适用于儿童钝性腹部创伤后的FAST。

PROJECT SUMMARY/ABSTRACT Dr. Aaron Kornblith, a general and pediatric emergency physician at the University of California, San Francisco (UCSF) is establishing himself as a future investigator in patient-oriented clinical research of novel diagnostics in injured children. This award will enable him to accomplish the following goals: (1) become an expert at patient- oriented clinical research in pediatric abdominal trauma; (2) develop novel machine learning models for a bedside ultrasound application; (3) implement advanced computational methods to develop, validate, and test clinical decision rules incorporating bedside ultrasound; and (4) develop an independent clinical research career. To achieve these goals, Dr. Kornblith has assembled an expert mentoring team: primary mentor Dr. Jeffrey Fineman, Chief of Pediatric Critical Care at UCSF (conducts clinical investigations in children with critical illness and is an expert in career development of early-stage investigators), co-mentors Dr. Atul Butte, (an expert in healthcare and data science), Drs. James Holmes and Nathan Kuppermann (experts in the diagnostic evaluation of pediatric trauma and clinical decision rules), scientific advisor Dr. John Mongan, (expert in developing, validating, and implementing machine learning for imaging tasks), and statistical advisor Dr. Bin Yu (an expert in statistical theory including accurate, reliable, and interpretable computational methods, and implicit bias). Hemorrhage from blunt intraabdominal injury is a leading cause of death in children. Identifying abdominal hemorrhage early is essential to minimizing morbidity and mortality from delayed or missed diagnoses. The reference standard test, abdominal computed tomography (CT), has drawbacks including risk of radiation- induced malignancy. For 25 years, CT use in children has increased dramatically without proportional improvements in outcomes. Focused Assessment with Sonography for Trauma (FAST) is a bedside ultrasound method to evaluate children for abdominal hemorrhage. FAST may help clinicians balance the risk of missed intraabdominal injury with unnecessary exposure to ionizing radiation from CT. Dr. Kornblith’s research will focus on improving pediatric FAST’s accuracy and reliability using machine learning models (Aim 1) and developing/validating novel clinical decision rules incorporating FAST to identify children at very low risk for injury who can forgo CT (Aim 2). Dr. Kornblith will use an existing dataset and computing infrastructure to develop and validate a machine learning model using >2.1 million frames from 1,264 pediatric FAST studies to detect hemorrhage as accurately as an expert (Aim 1), and two pre-existing datasets to develop and validate novel clinical decision rules incorporating FAST and compare their performance to existing clinical decision rules (Aim 2). The proposed research and training plan will position Dr. Kornblith with cross-disciplinary skills to transition to independence and submit a competitive R01 focused on refinement and validation of novel clinical decision rules integrating advanced computational methods applied to FAST for children after blunt abdominal trauma.