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，肺炎是5岁以下儿童死亡的主要原因。 岁的尽管在减少全球肺炎死亡方面取得了进展，但肺炎仍导致100多万儿童死亡 一年一个关键因素是肺炎诊断的挑战。胸部X光检查是肺炎的金标准 这类疾病的诊断，但使儿童暴露于电离辐射，并主要限于医院环境。之外 在医院，肺炎是根据临床依据使用世卫组织的综合管理， 儿童疾病算法。然而，这种方法缺乏特异性，导致严重的过度诊断。 肺炎这浪费了宝贵的资源，使儿童不必要地接触抗生素， 找出孩子生病的真正原因。 近年来，人们对床旁超声的兴趣激增，认为它是X-射线的一种无辐射替代品。 ray.床旁超声诊断肺炎的准确性与X线相当。但传统 床旁超声在便携性方面受到与X射线相同的限制，并且仍然需要解释 由训练有素的放射科医生拍摄。超声技术和人工智能的最新创新 应用程序提出了一条可能的前进道路。蓝牙启用美国转发器，连接到一个智能 手机或平板电脑，有效地创建一个真正的便携式美国套件，可以放入一个人的口袋。同样， 人工智能使自动解释移动的床边US（mBSUS）图像成为可能， 智能手机，避免了对放射科医生的需求。 该项目的两个目标是：1）比较移动的，蓝牙美国系统的准确性，连接到一个 手机，对照1 - 59岁儿童肺炎诊断的胸部X光金标准 2）应用机器学习算法，以帮助识别准确的分类 可靠地识别大叶性肺炎的特征。如果成功，这将是实现以下目标的关键的第一步： 一种真正便携但仍然高度准确的诊断小儿肺炎的方法， 自主地进行，而不需要由熟练的放射科医师进行外部评估。这样的工具将彻底改变 在世界上资源有限的地区开展肺炎病例管理，可以挽救许多人的生命。