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Author  |
Blanco, K.; Salcidua, S.; Orellana, P.; Sauma, T.; Leon, T.; Lopez-Steinmetz, L.C.; Ibañez, A.; Duran-Aniotz, C.; De la Cruz, R. | ||||
| Title | Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimers disease | Type | |||
| Year | 2023 | Publication | Alzheimer's Research & Therapy | Abbreviated Journal | Alzheimer's Res. Ther. |
| Volume | Early Access | Issue | Pages | ||
| Keywords | Mild cognitive impairment; Alzheimer� s disease; Fluid biomarker; Machine learning; Artificial intelligence | ||||
| Abstract | Mild cognitive impairment ( AQ1 MCI) is often considered an early stage of dementia, with estimated rates of progression to dementia up to 80�90% after approximately 6 years from the initial diagnosis. Diagnosis of cognitive impairment in dementia is typically based on clinical evaluation, neuropsychological assessments, cerebrospinal fluid (CSF) biomarkers, and neuroimaging. The main goal of diagnosing MCI is to determine its cause, particularly whether it is due to Alzheimer�s disease (AD). However, only a limited percentage of the population has access to etiological confirmation, which has led to the emergence of peripheral fluid biomarkers as a diagnostic tool for dementias, including MCI due to AD. Recent advances in biofluid assays have enabled the use of sophisticated statistical models and multimodal machine learning (ML) algorithms for the diagnosis of MCI based on fluid biomarkers from CSF, peripheral blood, and saliva, among others. This approach has shown promise for identifying specific causes of MCI, including AD. After a PRISMA analysis, 29 articles revealed a trend towards using multimodal algorithms that incorporate additional biomarkers such as neuroimaging, neuropsychological tests, and genetic information. Particularly, neuroimaging is commonly used in conjunction with fluid biomarkers for both crosssectional and longitudinal studies. Our systematic review suggests that cost-effective longitudinal multimodal monitoring data, representative of diverse cultural populations and utilizing white-box ML algorithms, could be a valuable contribution to the development of diagnostic models for AD due to MCI. Clinical assessment and biomarkers, together with ML techniques, could prove pivotal in improving diagnostic tools for MCI due to AD. |
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| ISSN | 1758-9193 | ISBN | Medium | ||
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| Notes | Approved | ||||
| Call Number | UAI @ alexi.delcanto @ | Serial | 1874 | ||
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