Automated Young Children’s Pain Detection via Facial Expressions with YOLO v11 - Gusgus Ghraha Ramdhanie; Nurdina Widanti; Bambang Aditya Nurgraha; Tomy Abuzairi; Nur Agustini; Dessie Wanda (Ikatan Ahli Informatika Indonesia (IAII), 2026)
This study demonstrates that pain detection in young children using a YOLO v11-based deep learning model can be performed effectively. By utilizing image data taken from video recordings of immunization and IV infusion procedures, then processed into photo frames and labeled using Roboflow, the model is able to provide good evaluation results. The dataset was divided into 70:20:10 for training, validation, and testing. Model performance evaluation uses accuracy, precision, recall, and F1-score metrics, and is visualized through a performance curve and confusion matrix. The results show that YOLO v11 has great potential as a pain detection method, with an mAP@0.5 achievement of 0.893, an accuracy of 78%, a precision of 89.3%, a recall of 97%, and an F1-score of 83%. The high recall value indicates the model's excellent ability to recognize pain expressions, making it relevant for use in clinical contexts to ensure pain symptoms are not overlooked. Overall, this performance demonstrates that YOLO v11 can be a more objective and accurate approach than manual instruments, and has the potential to be developed as a tool for healthcare professionals in pediatric pain assessment.