Dataset Information

Magnetic resonance imaging of the larynx in the pediatric population: A systematic review.

ABSTRACT:

Background

Magnetic Resonance Imaging (MRI) techniques to image the larynx have evolved rapidly into a promising and safe imaging modality, without need for sedation or ionizing radiation. MRI is therefore of great interest to image pediatric laryngeal diseases. Our aim was to review MRI developments relevant for the pediatric larynx and to discuss future imaging options.

Methods

A systematic search was conducted to identify all morphological and diagnostic studies in which MRI was used to image the pediatric larynx, laryngeal disease, or vocal cords.

Results

Fourteen articles were included: three studies on anatomical imaging of the larynx, two studies on Diffusion Weighted Imaging, four studies on vocal cord imaging and five studies on the effect of anaesthesiology on the pediatric larynx. MRI has been used for pediatric laryngeal imaging since 1991. MRI provides excellent soft tissue contrast and good visualization of vascular diseases such as haemangiomas. However, visualization of cartilaginous structures, with varying ossification during childhood, and tissue differentiation remain challenging. The latter has been partly overcome with diffusion weighted imaging (DWI), differentiating between benign and malignant masses with excellent sensitivity (94-94.4%) and specificity (91.2-100%). Vocal cord imaging evolved from static images focused on vocal tract growth to dynamic images able to detect abnormal vocal cord movement.

Conclusion

MRI is promising to evaluate the pediatric larynx, but studies using MRI as diagnostic imaging modality are scarce. New static and dynamic MR imaging techniques could be implemented in the pediatric population. Further research on imaging of pediatric laryngeal diseases should be conducted.

SUBMITTER: Elders BBLJ

PROVIDER: S-EPMC6590591 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

Magnetic resonance imaging of the larynx in the pediatric population: A systematic review.

Elders Bernadette B L J BBLJ Hermelijn Sergei M SM Tiddens Harm A W M HAWM Pullens Bas B Wielopolski Pjotr A PA Ciet Pierluigi P

Pediatric pulmonology 20190125 4

<h4>Background</h4>Magnetic Resonance Imaging (MRI) techniques to image the larynx have evolved rapidly into a promising and safe imaging modality, without need for sedation or ionizing radiation. MRI is therefore of great interest to image pediatric laryngeal diseases. Our aim was to review MRI developments relevant for the pediatric larynx and to discuss future imaging options.<h4>Methods</h4>A systematic search was conducted to identify all morphological and diagnostic studies in which MRI wa ...[more]

PMID: 30680950

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Project description:(1) Background: Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners. Additionally, transfer learning is beneficial for reutilizing machine learning models that were trained to solve different (but related) tasks to the task of interest. The aim of this review is to identify research directions, gaps in knowledge, applications, and widely used strategies among the transfer learning approaches applied in MR brain imaging; (2) Methods: We performed a systematic literature search for articles that applied transfer learning to MR brain imaging tasks. We screened 433 studies for their relevance, and we categorized and extracted relevant information, including task type, application, availability of labels, and machine learning methods. Furthermore, we closely examined brain MRI-specific transfer learning approaches and other methods that tackled issues relevant to medical imaging, including privacy, unseen target domains, and unlabeled data; (3) Results: We found 129 articles that applied transfer learning to MR brain imaging tasks. The most frequent applications were dementia-related classification tasks and brain tumor segmentation. The majority of articles utilized transfer learning techniques based on convolutional neural networks (CNNs). Only a few approaches utilized clearly brain MRI-specific methodology, and considered privacy issues, unseen target domains, or unlabeled data. We proposed a new categorization to group specific, widely-used approaches such as pretraining and fine-tuning CNNs; (4) Discussion: There is increasing interest in transfer learning for brain MRI. Well-known public datasets have clearly contributed to the popularity of Alzheimer's diagnostics/prognostics and tumor segmentation as applications. Likewise, the availability of pretrained CNNs has promoted their utilization. Finally, the majority of the surveyed studies did not examine in detail the interpretation of their strategies after applying transfer learning, and did not compare their approach with other transfer learning approaches.

Dataset Information

Magnetic resonance imaging of the larynx in the pediatric population: A systematic review.

Background

Methods

Results

Conclusion

Publications

Magnetic resonance imaging of the larynx in the pediatric population: A systematic review.

Similar Datasets

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