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

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

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.

• Jezik: Angleški jezik
• Ključne besede: 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
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2025
• Št. strani: 14 str.
• Številčenje: Vol. 15, iss. 14, art.7785
• PID: 20.500.12556/RUL-170748
• UDK: 004.85:616.8
• ISSN pri članku: 2076-3417
• DOI: 10.3390/app15147785
• COBISS.SI-ID: 242359043
• Datum objave v RUL: 14.07.2025

Gradivo je del revije

• Naslov: Applied sciences
• Skrajšan naslov: Appl. sci.
• Založnik: MDPI
• ISSN: 2076-3417
• COBISS.SI-ID: 522979353

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: strojno učenje, sledenje očesnim gibom, gladko sledenje, neinvaziven biomarker, kognitivni upad, zgodnje odkrivanje kognitivnega upada, odkrivanje blage kognitivne motnje, demenca, Alzheimerjeva bolezen

Projekti

• Financer: Drugi - Drug financer ali več financerjev
• Program financ.: Project NEUS

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: P2-0209
• Naslov: Umetna inteligenca in inteligentni sistemi