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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab International Journal of Management Science and Information Technology (IJMSIT)
Andrian Reinaldo Crispin
Universitas Prima Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering

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A Systematic Literature Review of Methods and Technologies for Detecting Pain in Infants Through Facial Expression Analysis Andrian Reinaldo Crispin; Luwis David Mahendra Aritonang; Marco Alfrino; Claudius Pratama Sitompul; Ivandi F. Simanullang
International Journal of Management Science and Information Technology Vol. 6 No. 2 (2026): July - December 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v6i2.7431

Abstract

Pain assessment in infants, particularly neonates, remains a challenging task due to their inability to verbally communicate pain. Therefore, pain evaluation commonly relies on indirect indicators, including facial expressions, crying behavior, and physiological responses. This study aims to review existing methods and technologies for infant pain detection through facial expression analysis using a Systematic Literature Review (SLR) approach based on the PRISMA framework. The review process involved several stages, including identification, screening, eligibility assessment, and inclusion of relevant studies, resulting in 27 articles selected for further analysis. The findings indicate that Deep Learning-based approaches, particularly Convolutional Neural Networks (CNNs), are the most frequently applied techniques for analyzing infant facial expressions in pain detection systems. Furthermore, recent studies have explored multimodal approaches by combining facial expressions with crying sounds and physiological signals to improve detection performance. Despite these advancements, several challenges remain, including limited data availability, lack of standardized public datasets, and difficulties in validating models across different clinical settings. Overall, artificial intelligence-based approaches demonstrate significant potential in supporting more objective and consistent infant pain assessment. This review provides insights into current research trends and future directions for developing reliable pain detection systems to assist healthcare professionals in neonatal care.
TINJAUAN LITERATUR SISTEMATIS TERHADAP DETEKSI NYERI DENGAN MEMANFAATKAN TEKNOLOGI NATURAL LANGUAGE PROCESSING Hari Rejeki Ginting; Andrian Reinaldo Crispin
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8149

Abstract

Pain detection is an important aspect in healthcare services, especially for patients who have difficulty communicating verbally, such as infants, critically ill patients, and individuals with neurological disorders. Advances in Natural Language Processing (NLP) have enabled automated analysis of medical text to support pain assessment more objectively and efficiently. This study aims to analyze the development of NLP methods used in pain detection through a Systematic Literature Review (SLR) approach. The study adopted the PRISMA guideline to identify, screen, and evaluate relevant articles obtained from Google Scholar and Scopus databases. The reviewed studies were published between 2017 and 2025. Based on the PRISMA selection process, 173 final articles met the inclusion criteria and were included in the final analysis. The results indicate that transformer-based models such as BERT, BioBERT, and ClinicalBERT achieved better performance compared to traditional machine learning and conventional deep learning methods. In addition, Electronic Health Records (EHR), clinical notes, and patient reports were identified as the most frequently used datasets in pain detection research. However, several challenges remain, including limited dataset availability, lack of evaluation standardization, and high computational requirements. This study is expected to provide a comprehensive overview of NLP-based pain detection and support future research in intelligent healthcare systems.
Co-Authors Claudius Pratama Sitompul Hari Rejeki Ginting Ivandi F. Simanullang Luwis David Mahendra Aritonang Marco Alfrino