AI supported detection of cerebral multiple sclerosis lesions decreases radiologic reporting times

•Many vendors offer commercial software for the detection of multiple sclerosis lesions in MRI.•Benefit of software support in clinical practice is not proven yet.•AI-based support for image-interpretation significantly decreases reporting times.•Usage of AI-support for image-interpretation can be h...

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Bibliographic Details
Published in:European journal of radiology 2024-09, Vol.178, p.111638, Article 111638
Main Authors: Peters, Sönke, Kellermann, Gesa, Watkinson, Joe, Gärtner, Friederike, Huhndorf, Monika, Stürner, Klarissa, Jansen, Olav, Larsen, Naomi
Format: Article
Language:English
Subjects:
Adult
Artificial Intelligence
Female
FLAIR
Humans
Image Interpretation, Computer-Assisted - methods
Magnetic Resonance Imaging - methods
Male
Middle Aged
MRI
Multiple sclerosis
Multiple Sclerosis - diagnostic imaging
Reproducibility of Results
Software
T2-lesions
Time Factors
Work flow
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Summary:•Many vendors offer commercial software for the detection of multiple sclerosis lesions in MRI.•Benefit of software support in clinical practice is not proven yet.•AI-based support for image-interpretation significantly decreases reporting times.•Usage of AI-support for image-interpretation can be helpful in clinical routine. Multiple Sclerosis (MS) is a common autoimmune disease of the central nervous system. MRI plays a crucial role in diagnosing as well as in disease and treatment monitoring. Therefore, evaluation of cerebral MRI of MS patients is part of daily clinical routine. A growing number of companies offer commercial software to support the reporting with automated lesion detection. Aim of this study was to evaluate the effect of such a software with AI supported lesion detection to the radiologic reporting. Four radiologist each counted MS-lesions in MRI examinations of 50 patients separated by the locations periventricular, cortical/juxtacortical, infrantentorial and unspecific white matter. After at least six weeks they repeated the evaluation, this time using the AI based software mdbrain for lesion detection. In both settings the required time was documented. Further the radiologists evaluated follow-up MRI of 50 MS-patients concerning new and enlarging lesions in the same manner. To determine the lesion-load the average reporting time decreased from 286.85 sec to 196.34 sec (p > 0.001). For the evaluation of the follow-up images the reporting time dropped from 196.17 sec to 120.87 sec (p 
ISSN:0720-048X
1872-7727
1872-7727
DOI:10.1016/j.ejrad.2024.111638