I-POCUS: An Intelligent Point-of-Care Ultrasound System for Neonatal Intensive Care Units in Canada's Hospitals

I-POCUS：加拿大医院新生儿重症监护病房的智能护理点超声系统

• 批准号: 546302-2019
• 负责人: Guan, Ling
• 金额: $ 16.25万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=700186
• 关键词: POCUS; Intelligent; Point; Care; Ultrasound

Point-of-Care Ultrasound (POCUS) systems help medical practitioners in obtaining fast, accurate, and safe imaging of patients in any scenarios. Ultrasound (US) scan is the principal modality of imaging being used in Neonatal Intensive Care Units (NICUs), since any other invasive imaging procedure is not feasible in neonates. Although the use of POCUS has been recognized to be able to provide accurate information to assist clinicians in diagnosing neonatal heart and lung conditions, currently this technology is mostly applied in large academic centres by a handful of clinicians, due to the barriers of relatively extensive training requirement, and access to a large patient population to maintain skills. Assessment and tracking of cardiac and lung dysfunctions from US images require significant expertise and training so that images of sufficient quality can be obtained. An intelligent assistant system to guide trainees in acquiring quality images is a key component. The development of intelligent algorithms is also required to automate the assessment, diagnosis, and tracking of cardiac and lung dysfunctions over time. Based on our recent successful collaboration with Dapasoft Inc, industrial partner of the proposal, this multidisciplinary team with members in engineering, computer science and medicine propose to investigate methods and techniques for the development of an intelligent POCUS (I-POCUS) system for the training of medical professionals operating in NICUs. Specifically, riding on recent success of artificial intelligence (AI), the target of the project is to develop novel and intelligent methods and algorithms in image and video processing, data analytics and visualization for the training of medical professionals capable of using of the I-POCUS systems in NICUs in order to intelligently and reliably diagnose neonatal heart and lung conditions and provide solutions to key clinical problems.

床旁超声（POCUS）系统可帮助医疗从业者在任何情况下获得快速、准确和安全的患者成像。超声（US）扫描是新生儿重症监护室（NICU）中使用的主要成像方式，因为任何其他侵入性成像程序在新生儿中都不可行。虽然POCUS的使用已被公认能够提供准确的信息，以帮助临床医生诊断新生儿心脏和肺部疾病，但目前这项技术主要由少数临床医生在大型学术中心应用，这是由于相对广泛的培训要求和接触大量患者人群以保持技能的障碍。从US图像评估和跟踪心脏和肺功能障碍需要大量的专业知识和培训，以便可以获得足够质量的图像。一个智能辅助系统，以指导学员在获取高质量的图像是一个关键组成部分。还需要开发智能算法来自动评估，诊断和跟踪心脏和肺功能障碍。 基于我们最近与Dapasoft Inc（该提案的工业合作伙伴）的成功合作，这个由工程，计算机科学和医学成员组成的多学科团队建议研究开发智能POCUS（I-POCUS）系统的方法和技术，用于培训在NICU中操作的医疗专业人员。具体而言，该项目的目标是利用人工智能（AI）最近的成功，开发图像和视频处理、数据分析和可视化方面的新颖智能方法和算法，以培训能够在NICU中使用I-POCUS系统的医疗专业人员，从而智能可靠地诊断新生儿心脏和肺部疾病，并为关键临床问题提供解决方案。