A replication study, systematic review and meta-analysis of automated image-based diagnosis in parkinsonism.
Sci Rep
; 12(1): 2763, 2022 02 17.
Article
en En
| MEDLINE
| ID: mdl-35177751
ABSTRACT
Differential diagnosis of parkinsonism early upon symptom onset is often challenging for clinicians and stressful for patients. Several neuroimaging methods have been previously evaluated; however specific routines remain to be established. The aim of this study was to systematically assess the diagnostic accuracy of a previously developed 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) based automated algorithm in the diagnosis of parkinsonian syndromes, including unpublished data from a prospective cohort. A series of 35 patients prospectively recruited in a movement disorder clinic in Stockholm were assessed, followed by systematic literature review and meta-analysis. In our cohort, automated image-based classification method showed excellent sensitivity and specificity for Parkinson Disease (PD) vs. atypical parkinsonian syndromes (APS), in line with the results of the meta-analysis (pooled sensitivity and specificity 0.84; 95% CI 0.79-0.88 and 0.96; 95% CI 0.91 -0.98, respectively). In conclusion, FDG-PET automated analysis has an excellent potential to distinguish between PD and APS early in the disease course and may be a valuable tool in clinical routine as well as in research applications.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Diagnóstico por Computador
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Trastornos Parkinsonianos
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Tomografía de Emisión de Positrones
Tipo de estudio:
Diagnostic_studies
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Systematic_reviews
Límite:
Humans
Idioma:
En
Año:
2022
Tipo del documento:
Article