Consenso de tromboprofilaxis en cirugía ginecológica

Ginecológica., Grupo Desarrollador del Consenso De Tromboprofilaxis en Cirugía; Ginecología, Federación Colombiana de Obstetricia y

doi:10.18597/rcog.3666

Cited by 3 publications

(4 citation statements)

References 25 publications

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“…In this study, the discriminative brain areas that can be used for PD diagnosis are located in the frontal, temporal, parietal, occipital, and limbic lobes, as well as in the cerebellum and the thalamus, which are consistent with the results of previous PD studies (Szewczyk-Krolikowski et al, 2014;O'Callaghan et al, 2016;Chen et al, 2017;Tuovinen et al, 2018). Many studies have indicated that damage to the corticothalamus-striatum-cortical pathway and the reciprocal striatum-cerebellar loop is the basis of many clinical symptoms of PD, and the thalamus plays an important role in function regulation and conduction (Szewczyk-Krolikowski et al, 2014;O'Callaghan et al, 2016;Tuovinen et al, 2018). In this study, we found that the radiomic features of the bilateral thalamus are helpful in the diagnosis of PD.…”

Section: Discussionsupporting

confidence: 91%

“…In this study, we found that the radiomic features of the bilateral thalamus are helpful in the diagnosis of PD. A previous study (O'Callaghan et al, 2016) showed that multiple brain areas of the cerebellum of PD patients were atrophied, including the bilateral lobules I-IV, VI, VII (crus I and crus II), VIIb, VIIIa, VIIIb, right V, and the cerebellar vermis. The study also indicated that the modulatory relationship of the subthalamic nucleus on intracerebellar connectivity was lost in PD patients and that there were extensive cerebellar-cortical network abnormalities.…”

Section: Discussionmentioning

confidence: 94%

“…It can extract high-throughput quantitative features of medical images and can provide greater features than conventional image analysis methods, thus improving disease diagnosis and prognostic evaluation (Nie et al, 2019;Tang et al, 2019;Ji et al, 2020). It was first used for tumor heterogeneity evaluation, tumor diagnosis and differential diagnosis (Nie et al, 2019), prognostic evaluation (Zhao L. et al, 2020), and tumor recurrence (Tang et al, 2019;Ji et al, 2020), among other uses. Recent studies have also indicated that it has a great application value in neuropsychological diseases (Tang et al, 2017;Péran et al, 2018;Sun et al, 2018;Huang K. et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…For the selected features, the least absolute shrinkage and selection operator (LASSO) logistic regression based on 10-fold cross-validation was used for further feature selection and data dimensionality reduction in the primary set, which used the mean squared error (MSE) and the minimum λ as the feature selection criteria. The LASSO logistic regression is suitable for the regression of high-dimensional data and is widely used in binary classification machine learning studies (Huang et al, 2016;Nie et al, 2019;Ji et al, 2020;Zhao L. et al, 2020), including neuropsychiatric disease classification (Tang et al, 2017;Zhao et al, 2018;Zheng et al, 2019). The LASSO can reduce data dimensionality by compressing the unimportant feature coefficients to zero, and then a formula is generated using a linear combination of selected features that are weighted by their respective LASSO coefficients.…”

Section: Feature Selection Model Construction and Evaluationmentioning

confidence: 99%

See 3 more Smart Citations

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

Shi¹,

Zhang²,

Wang³

et al. 2021

Front. Aging Neurosci.

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This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson’s disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD (n = 59) and healthy controls (HCs; n = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain. Machine learning methods were used in the primary set for data dimensionality reduction, feature selection, model construction, and model performance evaluation. The model performance was further validated in the validation set. After feature data dimension reduction and feature selection, 23 of a total of 1,276 features were entered in the model. The brain regions of the selected features included the frontal, temporal, parietal, occipital, and limbic lobes, as well as the cerebellum and the thalamus. In the primary set, the area under the curve (AUC) of the model was 0.974, the sensitivity was 93.8%, the specificity was 90.6%, and the accuracy was 93.8%. In the validation set, the AUC, sensitivity, specificity, and accuracy were 0.980, 90.9%, 88.9%, and 90.0%, respectively. ALFF-based histogram analysis can be used to classify patients with PD and HCs and to effectively identify abnormal brain function regions in PD patients.

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