Dear guest, welcome to this publication database. As an anonymous user, you will probably not have edit rights. Also, the collapse status of the topic tree will not be persistent. If you like to have these and other options enabled, you might ask Admin (Ivan Eggel) for a login account.
 [BibTeX] [RIS]
How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review
Type of publication: Article
Citation: SDM2023
Journal: NeuroImage: Clinical
Volume: 39
Year: 2023
Month: August
Pages: 103491
ISSN: 2213-1582
URL: https://www.sciencedirect.com/...
DOI: https://doi.org/10.1016/j.nicl.2023.103491
Abstract: Introduction: Over the past few years, the deep learning community has developed and validated a plethora of tools for lesion detection and segmentation in Multiple Sclerosis (MS). However, there is an important gap between validating models technically and clinically. To this end, a six-step framework necessary for the development, validation, and integration of quantitative tools in the clinic was recently proposed under the name of the Quantitative Neuroradiology Initiative (QNI). Aims: Investigate to what extent automatic tools in MS fulfill the QNI framework necessary to integrate automated detection and segmentation into the clinical neuroradiology workflow. Methods: Adopting the systematic Cochrane literature review methodology, we screened and summarised published scientific articles that perform automatic MS lesions detection and segmentation. We categorised the retrieved studies based on their degree of fulfillment of QNI’s six-steps, which include a tool’s technical assessment, clinical validation, and integration. Results: We found 156 studies; 146/156 (94%) fullfilled the first QNI step, 155/156 (99%) the second, 8/156 (5%) the third, 3/156 (2%) the fourth, 5/156 (3%) the fifth and only one the sixth. Conclusions: To date, little has been done to evaluate the clinical performance and the integration in the clinical workflow of available methods for MS lesion detection/segmentation. In addition, the socio-economic effects and the impact on patients’ management of such tools remain almost unexplored.
Keywords: Lesion detection, Lesion segmentation, MRI, Multiple sclerosis, Systematic Review
Authors Spagnolo, Federico
Depeursinge, Adrien
Müller, Henning
Akbulut, Aysenur
Barakovic, Muhamed
Melie-Garcia, Lester
Schädelin, Sabine
Granziera, Cristina
Added by: []
Total mark: 0
Attachments
  • manuscript.pdf
Notes
  • []: IF 2022: 4.2
Topics