Your browser doesn't support javascript.
loading
Quantity detection of substantia nigra hyperechogenicity based on digital analysis for diagnosing Parkinson′s disease / 中华神经科杂志
Chinese Journal of Neurology ; (12): 149-156, 2024.
Article em Zh | WPRIM | ID: wpr-1029185
Biblioteca responsável: WPRO
ABSTRACT
Objective:To apply digital analysis to quantify hyperechogenicity of substantia nigra, and explore its clinical value for diagnosis of Parkinson′s disease (PD).Methods:The cross-sectional study included 652 PD patients (PD group) and 99 healthy controls (healthy control group) from November 2017 to October 2020 in Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology. All subjects underwent transcranial sonography. The diagnostic accuracy of substantia nigra hyperechogenicity using digital analysis was compared with that in a manual measurement in PD. Furthermore, the receiver operating characteristic (ROC) curve analysis was applied to explore its diagnosis value in PD.Results:There were 482 subjects including 400 in the PD group and 82 in the healthy control group, whose quantified results of substantia nigra hyperechogenicity could be used for analysis. The ROC analysis showed that the area under the curve of the quantified larger substantia nigra hyperechoic region detection for diagnosing PD was 0.858 (95% CI 0.805-0.910), the sensitivity was 87.8%, and the specificity was 73.2%, consistent with that of doctors (area under the curve: 0.884). Further more, among these PD patients, there was no correlation between larger substantia nigra hyperechogenicity and age, age of onset, course of disease, non-motor symptoms, and motor symptoms (all P>0.05). Conclusions:Digital analysis was used to quantify the changes in substantia nigra hyperechogenicity in this seudy. The results showed that diagnostic accuracy for PD based on digital analysis was consistent with that of experienced clinicians.
Palavras-chave
Texto completo: 1 Índice: WPRIM Idioma: Zh Revista: Chinese Journal of Neurology Ano de publicação: 2024 Tipo de documento: Article
Texto completo: 1 Índice: WPRIM Idioma: Zh Revista: Chinese Journal of Neurology Ano de publicação: 2024 Tipo de documento: Article