Abstract
Point-of-care ultrasound (POCUS), realized by recent developments in portable ultrasound imaging systems for prompt diagnosis and treatment, has become a major tool in accidents or emergencies. Concomitantly, the number of untrained/unskilled staff not familiar with the operation of the ultrasound system for diagnosis is increasing. By providing an imaging guide to assist clinical decisions and support diagnosis, the risk brought by inexperienced users can be managed. Recently, deep learning has been employed to guide users in ultrasound scanning and diagnosis. However, in a cloud-based ultrasonic artificial intelligence system, the use of POCUS is limited due to information security, network integrity, and significant energy consumption. To address this, we propose (1) a structure that simultaneously provides ultrasound imaging and a mobile device-based ultrasound image guide using deep learning, and (2) a reverse scan conversion (RSC) method for building an ultrasound training dataset to increase the accuracy of the deep learning model. Experimental results show that the proposed structure can achieve ultrasound imaging and deep learning simultaneously at a maximum rate of 42.9 frames per second, and that the RSC method improves the image classification accuracy by more than 3%.
Highlights
Point-of-care ultrasound (POCUS) is an efficient tool for providing diagnostic imaging at the time and place of patient care [1]
We evaluate the effectiveness of building an ultrasound image training dataset using the reverse scan conversion (RSC) method, as well as the performance of the frame asynchronous classification (FAC) structure that executes ultrasound image reconstruction in real-time with deep learning network (DLN) inference
Because the FAC structure can divide the classification pipeline (CP) to operate without delay between IRPs, reconstruction processing with classification is possible on the mobile device in real time
Summary
Point-of-care ultrasound (POCUS) is an efficient tool for providing diagnostic imaging at the time and place of patient care [1]. POCUS has become more convenient to use with mobile device-based ultrasound scanners such as Healcerion, Butterfly iQ (Butterfly Network Inc.), and Clarius (Clarius Mobile Health Corp.). These devices have contributed to the expansion of POCUS applications to deliver novel clinical benefits to patients [2]. As ultrasound imaging is generally performed to obtain diagnostic information in real time, POCUS users must be properly trained or technically supported at the time of ultrasound scanning. This is achievable using computer-aided diagnosis tools
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