Comparative Analysis of Advancements in Patient Pain Detection using Deep Learning

This review provides a systematic overview of the current state of sentiment analysis as applied to detecting pain from facial expression A key feature of this work is the employment of an analytical framework informed by PRISMA guidelines, Our research consisted of an extensive search of multiple high-profile databases for all articles published within the last five years. In light of selection criteria which rejected articles devoid of any machine or deep learning techniques for gaining knowledge of pain through facial expressions. To select the approximate papers, our evaluation process applied a strict set of inclusion/exclusion criteria. In addition, focus was placed on methodology, Datasets, feature extraction approaches, classification, evaluation metrics, etc. A critical approach was used that compared the advantages and disadvantages of each of the methodologies. However, to achieve the goal of improving patients’ outcome, one must be aware of the threats listed above and would be prepared to apply the suggested innovations to sentiment analysis for pain detection. Ultimately, this approach is likely to help make this tool much more important for healthcare professionals, which would bring more benefits for pain management and patient care.