Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

• Published in: Procedia computer science Vol. 220; pp. 339 - 347
• Main Authors: Ismail, Leila, Waseem, Muhammad Danish
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 2023; The Author(s). Published by Elsevier B.V
• Subjects: Computer Vision; Deep Learning; eHealth; Image Processing; Machine Learning; Pain Detection; Patient-Centric; Smart healthcare; Pain Detection; Smart healthcare; Image Processing; Patient-Centric; Computer Vision; Machine Learning; Deep Learning; eHealth
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2023.03.044

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.