From Seeing to Knowing with Artificial Intelligence: A Scoping Review of Point-of-Care Ultrasound in Low-Resource Settings

Author:

Venkatayogi Nethra1,Gupta Maanas1,Gupta Alaukik1,Nallaparaju Shreya2,Cheemalamarri Nithya3,Gilari Krithika4,Pathak Shireen1,Vishwanath Krithik5ORCID,Soney Carel5,Bhattacharya Tanisha6,Maleki Nirvana4,Purkayastha Saptarshi7ORCID,Gichoya Judy Wawira8

Affiliation:

1. Department of Biomedical Engineering, University of Texas at Austin, Austin, TX 78712, USA

2. Department of Biology, University of Texas at Austin, Austin, TX 78712, USA

3. Department of Informatics, University of Texas at Austin, Austin, TX 78712, USA

4. Department of Neuroscience, University of Texas at Austin, Austin, TX 78712, USA

5. Department of Aerospace Engineering and Engineering Mechanics, University of Texas at Austin, Austin, TX 78712, USA

6. Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX 78712, USA

7. Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA

8. Department of Radiology, Emory University School of Medicine, Atlanta, GA 30307, USA

Abstract

The utilization of ultrasound imaging for early visualization has been imperative in disease detection, especially in the first responder setting. Over the past decade, rapid advancements in the underlying technology of ultrasound have allowed for the development of portable point-of-care ultrasounds (POCUS) with handheld devices. The application of POCUS is versatile, as seen by its use in pulmonary, cardiovascular, and neonatal imaging, among many others. However, despite these advances, there is an inherent inability of translating POCUS devices to low-resource settings (LRS). To bridge these gaps, the implementation of artificial intelligence offers an interesting opportunity. Our work reviews recent applications of POCUS devices within LRS from 2016 to 2023, identifying the most commonly utilized clinical applications and areas where further innovation is needed. Furthermore, we pinpoint areas of POCUS technologies that can be improved using state-of-art artificial intelligence technologies, thus enabling the widespread adoption of POCUS devices in low-resource settings.

Funder

US National Science Foundation

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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