Grant: Alliance Grants

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.