Tinjauan Sistematis: Teknik eye tracking untuk penyakit Skizofrenia - Saragih, Septua Fujima; Ginting, Ricci Kincahar Bastoto Kevin; Simajuntak, Yusuf Natanael; Nasution, Adli Abdillah; Indra, Evta (Jurnal Media Informatika, 2024)
Eye tracking technology has emerged as an innovative tool for understanding and diagnosing schizophrenia, demonstrating significant potential in revealing different eye movement patterns between patients and healthy individuals. Literature studies indicate that irregular eye fixations and inconsistent saccades in schizophrenia patients may indicate disruptions in visual information processing and attention allocation. Eye tracking metrics, such as gaze duration and fixation stability, provide crucial insights into cognitive functions and emotional states in patients. Integration of eye tracking technology with machine learning techniques, including eXtreme Gradient Boosting (XGB) and Support Vector Machines (SVM), has achieved diagnostic accuracy up to 94%, highlighting its potential to enhance diagnostic precision. Despite these promising advances, challenges such as symptom variability among individuals, patient comfort, and the need for standard protocols remain. The development of non-intrusive eye tracking systems and applications in virtual reality (VR) shows potential for innovative therapies. Further research is needed to address these challenges and ensure effective and consistent implementation of this technology in clinical practice.