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

Povzetek

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.

Ključne besede

strojno učenje;sledenje očesnim gibom;gladko sledenje;neinvaziven biomarker;kognitivni upad;zgodnje odkrivanje kognitivnega upada;odkrivanje blage kognitivne motnje;demenca;Alzheimerjeva bolezen;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;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FRI - Fakulteta za računalništvo in informatiko
UDK: 004.85:616.8
COBISS: 242359043 Povezava se bo odprla v novem oknu
ISSN: 2076-3417
Št. ogledov: 154
Št. prenosov: 31
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: strojno učenje;sledenje očesnim gibom;gladko sledenje;neinvaziven biomarker;kognitivni upad;zgodnje odkrivanje kognitivnega upada;odkrivanje blage kognitivne motnje;demenca;Alzheimerjeva bolezen;
Vrsta dela (COBISS): Članek v reviji
Strani: str. 1-14
Letnik: ǂVol. ǂ15
Zvezek: ǂiss. ǂ14, [article no.] 7785
Čas izdaje: Jul. 2025
DOI: 10.3390/app15147785
ID: 26828197