TY  - JOUR
ID  - SDM2023
T1  - How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review
A1  - Spagnolo, Federico
A1  - Depeursinge, Adrien
A1  - Müller, Henning
A1  - Akbulut, Aysenur
A1  - Barakovic, Muhamed
A1  - Melie-Garcia, Lester
A1  - Schädelin, Sabine
A1  - Granziera, Cristina
JA  - NeuroImage: Clinical
Y1  - 2023
VL  - 39
SP  - 103491
SN  - 2213-1582
UR  - https://www.sciencedirect.com/science/article/pii/S2213158223001821
M2  - doi: https://doi.org/10.1016/j.nicl.2023.103491
KW  - Lesion detection
KW  - Lesion segmentation
KW  - MRI
KW  - Multiple sclerosis
KW  - Systematic Review
N2  - 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.
ER  -