Mobile bedside ultrasound for the diagnosis of pediatric pneumonia in resource limited settings

移动床边超声诊断资源有限环境中的小儿肺炎

• 批准号: 10246667
• 负责人: Christopher J GILL
• 金额: $ 1.01万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-27 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10246667
• 关键词: 5 year old; Accident and Emergency department; Algorithms; Antibiotics; Aorta; Artificial Intelligence; Blinded; Bluetooth; Breast Feeding; Breathing; Case Management; Cause of Death; Cellular Phone; Cessation of life; Characteristics; Chest wall structure; Child; Childhood; Classification; Clinical; Clinical Data; Communities; Convulsions; Coughing; Cyanosis; Data; Diagnosis; Diagnostic; Diagnostic radiologic examination; Disease; Enrollment; Estrogen receptor positive; Evaluation; Explosion; Goals; Gold; Head; Heart; Hospitals; Image; Image Analysis; Ionizing radiation; Lethargies; Link; Liver; Lung; Machine Learning; Measures; Pathway interactions; Pediatric Radiologist; Pleura; Pneumococcal Pneumonia; Pneumonia; Predictive Value; Public Health; Radiation; Regional Anatomy; Resources; Respiratory Diaphragm; Roentgen Rays; Sensitivity and Specificity; Specificity; System; Tablets; Teaching Hospitals; Technology; Telephone; Testing; Thoracic Radiography; Tooth structure; Training; Twin Multiple Birth; Ultrasonography; Unconscious State; Universities; Variant; Vomiting; Zambia; aged; base; clinical Diagnosis; drinking; health training; imaging modality; imaging system; improved; innovation; interest; low and middle-income countries; machine learning algorithm; meetings; mortality; portability; radiologist; smartphone Application; tool; wasting

Project Summary In low and middle-income countries (LMICs) pneumonia is by far the leading cause of death among children < 5 years of age. Despite progress in reducing global pneumonia deaths, pneumonia still kills over 1 million children a year. A key factor is the challenge of pneumonia diagnosis. Chest X-Ray is the gold standard for pneumonia diagnoses but exposes children to ionizing radiation and is mainly restricted to hospital settings. Outside of hospitals, pneumonia is diagnosed based on clinical grounds using the WHO’s Integrated Management of Childhood Illness algorithms. However, that approach lacks specificity, resulting in significant overdiagnosis of pneumonia. That wastes precious resources, exposes children to antibiotics unnecessarily, and delays identification of the true cause of a child’s illness. Recent years have seen an explosion of interest in bedside ultrasound as a radiation-sparing alternative to X- Ray. The accuracy of bedside US for diagnosing pneumonia is comparable to X-Ray. However, traditional bedside US suffers the same limitations as X-Rays in terms of portability, and still requires interpretation of images by a trained radiologist. Recent innovations in ultrasound technology and artificial intelligence applications suggest a possible pathway forward. Bluetooth enabled US transponders that connect to a smart phone or tablet, effectively create a truly portable US suite that can fit into one’s pocket. Similarly, advances in artificial intelligence render possible the automated interpretation of mobile bedside US (mBSUS) images on a smart phone, obviating the need for a radiologist. The twin goals of this project are: 1) to compare the accuracy of the mobile, Bluetooth US system, linked to a cell phone, against the gold standard of Chest X-Ray for diagnosis of pneumonia among children aged 1-59 months; and 2) to apply machine learning algorithms to assist in the identification of accurate classification features that reliably identify lobar pneumonia. If successful, this would be a pivotal first step towards the goal of a truly portable yet still highly accurate approach to the diagnosis of pediatric pneumonia that could function autonomously without the need for external evaluation by a skilled radiologist. Such a tool would revolutionize pneumonia case management in resource limited parts of the world and could save the lives of many.

项目摘要 在低收入和中等收入国家（LMIC）肺炎是迄今为止儿童死亡的主要原因<5 年龄。尽管在减少全球肺炎死亡方面进展，但肺炎仍会造成超过100万儿童 一年。一个关键因素是肺炎诊断的挑战。胸部X射线是肺炎的金标准 诊断但使儿童暴露于电离辐射，主要仅限于医院环境。外面 使用WHO的综合管理，根据临床理由诊断出医院，肺炎 儿童疾病算法。但是，这种方法缺乏特异性，导致 肺炎。这会浪费宝贵的资源，使儿童不必要地暴露于抗生素和延误 确定孩子病的真正原因。 近年来，人们对床边超声引起了人们的兴趣，这是X-的辐射替代品 射线。床边我们在诊断性肺炎中的准确性与X射线相当。但是，传统 在可移植性方面 训练有素的放射科医生的图像。超声技术和人工智能的最新创新 应用表明可能会前进。蓝牙使我们能够连接到SMART 电话或平板电脑，有效地创建了一个真正的便携式美国套房，该套房可以安装在口袋里。同样，进步 人工智能使Mobile Bedside US（MBSU）图像的自动解释可能 智能手机，避免了对放射务员的需求。 该项目的双目标是：1）比较移动，蓝牙美国系统的准确性，该系统链接到一个 手机，违反1-59岁儿童中肺炎诊断诊断的胸部X射线标准 月份； 2）应用机器学习算法以帮助识别准确的分类 可靠地识别肺炎肺炎的特征。如果成功，这将是朝着目标迈出的关键第一步 一种真正的便携式但仍然高度准确的小儿肺炎诊断的方法 自主无需熟练的放射科医生进行外部评估。这样的工具会革新 资源有限地区的肺炎病例管理可以挽救许多人的生命。