Machine learning-based detection of cognitive impairment from eye-tracking in smooth pursuit tasks

Groznik, Vida (author), De Gobbis, Andrea (author), Georgiev, Dejan (author), Semeja, Aleš (author), Sadikov, Aleksander (author)

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.

• Language: English
• Keywords: 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
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: 14 str.
• Numbering: Vol. 15, iss. 14, art.7785
• PID: 20.500.12556/RUL-170748
• UDC: 004.85:616.8
• ISSN on article: 2076-3417
• DOI: 10.3390/app15147785
• COBISS.SI-ID: 242359043
• Publication date in RUL: 14.07.2025

Record is a part of a journal

• Title: Applied sciences
• Shortened title: Appl. sci.
• Publisher: MDPI
• ISSN: 2076-3417
• COBISS.SI-ID: 522979353

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: strojno učenje, sledenje očesnim gibom, gladko sledenje, neinvaziven biomarker, kognitivni upad, zgodnje odkrivanje kognitivnega upada, odkrivanje blage kognitivne motnje, demenca, Alzheimerjeva bolezen

Projects

• Funder: Other - Other funder or multiple funders
• Funding programme: Project NEUS

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: P2-0209
• Name: Umetna inteligenca in inteligentni sistemi