Systematic Literature Review: Menilai Tingkat Nyeri Melalui Pola Suara - Kristanti, Inka; Br Sitohang, Sondang Agustina; Margaretha, Yulia; Daya, Onita; HS, Christnatalis (LPPM Universitas Mulia, 2025)
In the medical field, Accurate detection of pain levels is a crucial aspect of healthcare, especially for patient groups who cannot directly communicate their pain, such as infants, individuals in critical condition, or those with neurological dysfunction. This study aims to test the effectiveness of a voice pattern analysis approach in detecting pain levels through a Systematic Literature Review (SLR) method. From 500 articles, 13 relevant inclusion studies were selected based on PRISMA criteria. The review results indicate that sounds such as crying and moaning can serve as objective pain indicators, and have great potential for integration into clinical systems. Supported by artificial intelligence algorithms such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), the accuracy level of pain detection based on sound reaches 83% to 96% depending on the type of data and methods. Although the results are promising, there are several challenges such as limited dataset variability, background noise interference, and the absence of a standardized voice-based pain classification. Therefore, further research is needed for direct validation of the system in clinical environments, development of classification standards, and exploration of multimodality to improve accuracy. This research is expected to serve as a foundation for the development of more objective, adaptive, and inclusive pain assessment technologies for patients with communication limitations.