Enhancing foveal avascular zone analysis for Alzheimer's diagnosis with AI segmentation and machine learning using multiple radiomic features.
Sci Rep
; 14(1): 1841, 2024 01 22.
Article
en En
| MEDLINE
| ID: mdl-38253722
ABSTRACT
We propose a hybrid technique that employs artificial intelligence (AI)-based segmentation and machine learning classification using multiple features extracted from the foveal avascular zone (FAZ)-a retinal biomarker for Alzheimer's disease-to improve the disease diagnostic performance. Imaging data of optical coherence tomography angiography from 37 patients with Alzheimer's disease and 48 healthy controls were investigated. The presence or absence of brain amyloids was confirmed using amyloid positron emission tomography. In the superficial capillary plexus of the angiography scans, the FAZ was automatically segmented using an AI method to extract multiple biomarkers (area, solidity, compactness, roundness, and eccentricity), which were paired with clinical data (age and sex) as common correction variables. We used a light-gradient boosting machine (a light-gradient boosting machine is a machine learning algorithm based on trees utilizing gradient boosting) to diagnose Alzheimer's disease by integrating the corresponding multiple radiomic biomarkers. Fivefold cross-validation was applied for analysis, and the diagnostic performance for Alzheimer's disease was determined by the area under the curve. The proposed hybrid technique achieved an area under the curve of [Formula see text]%, outperforming the existing single-feature (area) criteria by over 13%. Furthermore, in the holdout test set, the proposed technique exhibited a 14% improvement compared to single features, achieving an area under the curve of 72.0± 4.8%. Based on these facts, we have demonstrated the effectiveness of our technology in achieving significant performance improvements in FAZ-based Alzheimer's diagnosis research through the use of multiple radiomic biomarkers (area, solidity, compactness, roundness, and eccentricity).
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Inteligencia Artificial
/
Enfermedad de Alzheimer
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Sci Rep
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Reino Unido