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

为受伤儿童提供准确可靠的诊断：超声机器学习

• 批准号: 10572582
• 负责人: Aaron Edward Kornblith
• 金额: $ 16.24万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-15 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10572582
• 关键词: Abdomen; Abdominal Injuries; Adolescent and Young Adult; Adult; Applied Research; Award; Blunt Trauma; California; Cause of Death; Child; Child Care; Childhood; Childhood Injury; Clinical; Clinical Research; Computing Methodologies; Critical Care; Critical Illness; Data Science; Data Set; Decision Modeling; Detection; Development; Diagnosis; Diagnostic; Diagnostic Imaging; Diagnostic tests; Emergency Care; Emergency Department Physician; Emergency Medicine; Equilibrium; Evaluation; Exposure to; Extramural Activities; Failure; Foundations; Funding; Future; Goals; Healthcare; Hemorrhage; Image; Infant; Infrastructure; Injury; Intervention; Intra-abdominal; Ionizing radiation; Machine Learning; Malignant Neoplasms; Mentors; Methods; Mission; Modeling; Morbidity - disease rate; National Institute of Child Health and Human Development; Outcome; Pediatric Surgical Procedures; Performance; Physicians; Positioning Attribute; Protocols documentation; Radiation; Radiation exposure; Reference Standards; Research; Research Activity; Research Design; Research Personnel; Research Support; Risk; San Francisco; Scanning; Scientist; Specificity; Techniques; Testing; Training; Training and Education; Trauma; Traumatic injury; Ultrasonography; United States; Universities; Validation; Work; X-Ray Computed Tomography; abdominal CT; career; career development; clinical investigation; deep learning; deep learning model; diagnostic accuracy; diagnostic strategy; diagnostic tool; evidence base; experience; health data; implicit bias; improved; improved outcome; injured; innovation; machine learning model; mortality; multidisciplinary; novel; novel diagnostics; patient oriented; pediatric emergency; pediatric trauma; point-of-care diagnostics; preventable death; radiation risk; radiological imaging; secondary analysis; skills; surgical research; theories; traumatized children; ultrasound

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.

项目摘要/摘要 加利福尼亚大学旧金山分校的一般和儿科急诊医师Aaron Kornblith博士 （UCSF）将自己确立为以患者为导向的新型诊断研究的未来研究者 在受伤的孩子中。该奖项将使他能够实现以下目标：（1）成为患者的专家 - 小儿腹部创伤的定向临床研究； （2）开发新的机器学习模型 床边超声应用； （3）实施高级计算方法来开发，验证和测试 临床决策规则包含床边超声； （4）发展独立的临床研究职业。 为了实现这些目标，Kornblith博士组建了一个专家心理团队：主要的精神杰弗里博士 UCSF儿科重症监护官Fineman（对患有疾病的儿童进行临床研究 并且是早期调查员职业发展的专家 医疗保健和数据科学），博士。詹姆斯·霍尔姆斯（James Holmes）和内森·库珀曼（Nathan Kuppermann）（诊断评估专家 科学顾问约翰·蒙根（John Mongan）博士（开发，专家， 验证和实施用于成像任务的机器学习）和统计顾问Bin Yu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和隐性偏见）。 钝性腹腔内损伤出血是儿童死亡的主要原因。识别腹部 早期出血对于最大程度地减少延迟或错过诊断的发病率和死亡率至关重要。 参考标准测试，腹部计算机断层扫描（CT）的缺点，包括辐射风险 诱发恶性肿瘤。 25年来，儿童的CT使用急剧增加而没有成比例 结果的改善。通过超声检查的重点评估（快速）是床边超声 评估儿童腹部出血的方法。快速可以帮助临床医生平衡错过的风险 腹腔内损伤，不必要地暴露于CT的电离辐射。 Kornblith博士的研究将重点 使用机器学习模型（AIM 1）和 制定/验证新颖的临床决策规则快速识别受伤风险非常低的儿童 谁能忘记CT（AIM 2）。 Kornblith博士将使用现有的数据集和计算基础架构来开发和 使用来自1,264个小儿快速研究的210万帧验证机器学习模型以检测 与专家一样准确的出血（AIM 1）和两个已有的已有数据集来开发和验证新颖 临床决策规则纳入快速并将其绩效与现有临床决策规则进行比较（目标 2）。拟议的研究和培训计划将以跨学科技能定位Kornblith博士过渡 独立并提交竞争性R01，重点是改进和验证新颖的临床决策 在钝性腹部创伤后，整合适用于儿童的高级计算方法的规则。