Ecografía cerebral en neonatos

López-Azorín, Manuela; Ybarra, Marta; Cabañas, Fernando

doi:10.1016/s1696-2818(12)70092-6

Cited by 1 publication

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For these reasons, MRI is a very effective technology for diagnosing and identifying many neurological and cerebrovascular diseases, such as posterior reversible encephalopathy syndrome, 15 attention deficit disorder, 16 and language, 17 among others. In addition, it has prognostic value by detecting early irreversible sequelae such as cerebral infarction 9,18,19 . MRI is the best imaging modality for early diagnosis 20,21 .…”

Section: Introductionmentioning

confidence: 99%

“…In addition, it has prognostic value by detecting early irreversible sequelae such as cerebral infarction. 9,18,19 MRI is the best imaging modality for early diagnosis. 20,21 Quantitative analysis of MRI studies allows to correlate brain lesions with an increase in lateral ventricular volume in preterm and term infants.…”

Section: Introductionmentioning

confidence: 99%

“…If not detected and intervened early, they can cause functional and neurological abnormalities, interfering with the social, mental, and physical capacity of infants 7 . For this reason, tools such as Magnetic Resonance Imaging (MRI), 8 Cranial Ultrasonography (CUS), Color Flow Doppler (CFDD), 9 and Electroencephalogram (EEG) 10,11 have been some of the most widely used tools in neonatal analysis.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent MRI diagnosis of neurological alterations in infants from 4 to 12 months

Calderon‐Uribe,

Morales‐Hernandez,

De Leo‐Jimenez

et al. 2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Magnetic resonance imaging is an essential tool for the identification of neurological problems since it provides relevant information on brain development. The aim of the present work was the detection of neurological alterations in newborns from 4 to 12 months of age by segmentation and analysis of lateral ventricles in magnetic resonance images. For this purpose, an automated deep approach based on U‐net is proposed to segment the cerebral ventricles of the newborn. Subsequently, for these regions, features were extracted based on the patient's clinical history and on the shape (area, roundness, normalized central moment, among others) and pixel intensity (mean gray value, contrast level, among others). Once the features were extracted, different types of intelligent models (Logistic Regression, k‐Nearest Neighbors (kNN), and a Convolutional Neural Network) were assessed to detect the presence of neurological alterations. The segmentation phase of the system was tested on 50 patients and the classification phase on 28 patients (11 healthy, 17 with neurological changes). The results show a DICE similarity coefficient of 0.89 and a volume ratio of 1.05 for the segmentation stage and an accuracy of 98%, precision of 100%, sensitivity of 92%, and specificity of 100% for the classification stage using kNN. The last one proved to be the most computationally feasible model, due to the time required for training and inference (0.36 s and 35.2e‐4 s, respectively), as well as the consumption of computational resources (0.1 GB RAM CPU). In conclusion, it is possible to detect neurological alterations in newborns aged 4 to 12 months by segmenting and classifying the lateral ventricles in magnetic resonance images, using image processing techniques, the U‐net, as well as the kNN algorithm. This proposed methodology could play an important role in the early diagnosis and treatment of neurological disorders.

show abstract