20.500.12556/RUL-170748 Machine learning-based detection of cognitive impairment from eye-tracking in smooth pursuit tasks Mild cognitive impairment represents a transitional phase between healthy ageing and dementia, including Alzheimer’s disease. Early detection is essential for timely clinical intervention. This study explores the viability of smooth pursuit eye movements (SPEM) as a non-invasive biomarker for cognitive impairment. A total of 115 participants—62 with cognitive impairment and 53 cognitively healthy controls—underwent comprehensive neuropsychological assessments followed by an eye-tracking task involving smooth pursuit of horizontally and vertically moving stimuli at three different speeds. Quantitative metrics such as tracking accuracy were extracted from the eye movement recordings. These features were used to train machine learning models to distinguish cognitively impaired individuals from controls. The best-performing model achieved an area under the ROC curve (AUC) of approximately 68 %, suggesting that SPEM-based assessment has potential as part of an ensemble of eye-tracking based screening methods for early cognitive decline. Of course, additional paradigms or task designs are required to enhance diagnostic performance. machine learning eye-tracking smooth pursuit non-invasive biomarker cognitive impairment early detection of cognitive decline detection of mild cognitive impairment dementia Alzheimer's disease strojno učenje sledenje očesnim gibom gladko sledenje neinvaziven biomarker kognitivni upad zgodnje odkrivanje kognitivnega upada odkrivanje blage kognitivne motnje demenca Alzheimerjeva bolezen true false true Angleški jezik Slovenski jezik Članek v reviji 2025-07-14 12:42:04 2025-07-14 12:42:07 2025-07-18 03:43:56 0000-00-00 00:00:00 2025 0 0 14 str. iss. 14, art.7785 Vol. 15 Jul. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.85:616.8 2076-3417 10.3390/app15147785 242359043 RAZ_Groznik_Vida_2025.pdf RAZ_Groznik_Vida_2025.pdf 1 075EE363E6E7B472F92B4629FF7C4F07 ad92110fb427e34f3a6beb24cddde955feeae543db7de4dc68fcf979e46b9e4e 157df2c3-609e-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=212918 https://www.mdpi.com/2076-3417/15/14/7785 1 40ffe323-609d-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=212914 Fakulteta za računalništvo in informatiko 0 0 0