| Autor: |
Patricio Andres Donnelly‐Kehoe, Guido Orlando Pascariello, Adolfo M. García, John R. Hodges, Bruce Miller, Howie Rosen, Facundo Manes, Ramon Landin‐Romero, Diana Matallana, Cecilia Serrano, Eduar Herrera, Pablo Reyes, Hernando Santamaria‐Garcia, Fiona Kumfor, Olivier Piguet, Agustin Ibanez, Lucas Sedeño |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, Vol 11, Iss 1, Pp 588-598 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2352-8729 |
| DOI: |
10.1016/j.dadm.2019.06.002 |
| Popis: |
Abstract Introduction Timely diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging because it depends on clinical expertise and potentially ambiguous diagnostic guidelines. Recent recommendations highlight the role of multimodal neuroimaging and machine learning methods as complementary tools to address this problem. Methods We developed an automatic, cross‐center, multimodal computational approach for robust classification of patients with bvFTD and healthy controls. We analyzed structural magnetic resonance imaging and resting‐state functional connectivity from 44 patients with bvFTD and 60 healthy controls (across three imaging centers with different acquisition protocols) using a fully automated processing pipeline, including site normalization, native space feature extraction, and a random forest classifier. Results Our method successfully combined multimodal imaging information with high accuracy (91%), sensitivity (83.7%), and specificity (96.6%). Discussion This multimodal approach enhanced the system's performance and provided a clinically informative method for neuroimaging analysis. This underscores the relevance of combining multimodal imaging and machine learning as a gold standard for dementia diagnosis. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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