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.

项目概要/摘要 Aaron Kornblith 博士，加州大学旧金山分校的普通科和儿科急诊科医生 （加州大学旧金山分校）正在将自己定位为以患者为导向的新型诊断临床研究的未来研究者 该奖项将使他能够实现以下目标：（1）成为患者方面的专家- 面向儿科腹部创伤的临床研究；(2) 开发新颖的机器学习模型 床旁超声应用；(3) 实施先进的计算方法来开发、验证和测试 纳入床边超声的临床决策规则；(4) 发展独立的临床研究生涯。 为了实现这些目标，Kornblith 博士组建了一个专家指导团队：首席导师 Jeffrey 博士 Fineman，加州大学旧金山分校儿科重症监护主任（对患有危重疾病的儿童进行临床调查） 是早期研究人员职业发展方面的专家），共同导师 Atul Butte 博士（ 医疗保健和数据科学），James Holmes 博士和 Nathan Kuppermann 博士（诊断评估专家） 儿科创伤和临床决策规则），科学顾问 John Mongan 博士（开发、 验证和实施成像任务的机器学习）和统计顾问 Bin Yu 博士（专家 统计理论，包括准确、可靠和可解释的计算方法，以及隐性偏差）。 腹部钝性损伤造成的出血是儿童死亡的主要原因。 早期出血对于最大限度地减少延迟或漏诊造成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），有缺点，包括辐射风险 25 年来，CT 在儿童中的使用急剧增加，不成比例。 创伤超声重点评估 (FAST) 是一种床旁超声检查。 评估儿童腹部出血的方法可能有助于平衡漏诊风险。 Kornblith 博士的研究重点是不必要地暴露于 CT 电离辐射造成的腹内损伤。 使用机器学习模型提高儿科 FAST 的准确性和可靠性（目标 1）以及 开发/验证纳入 FAST 的新型临床决策规则，以识别受伤风险极低的儿童 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 项儿科 FAST 研究的超过 210 万帧来验证机器学习模型 像专家一样准确地测量出血（目标 1），以及两个预先存在的数据集来开发和验证新颖的 纳入 FAST 的临床决策规则，并将其性能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使 Kornblith 博士具备跨学科技能以实现转型 独立并提交一份有竞争力的 R01，重点关注新临床决策的细化和验证 规则整合了应用于腹部钝性创伤后儿童 FAST 的先进计算方法。