Improved Multiple Sclerosis Lesion Detection using an Intelligent Automation Software

Abstract

Introduction The assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Jazz is a deep-learning based software dedicated to enhance the radiologist in this task. We evaluate Jazz for the assessment of new, slowly expanding, and contrast-enhancing MS lesions in three centers, and compared the reported lesions with the lesions described in the standard report. Methods In three separate centers, 120 MS follow-up MRIs were independently analyzed using Jazz by 2 blinded neuroradiologists. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report. Results A total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2 min 33 sec +/- 1 min 0 sec per case. Discussion The quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using a dedicated software such as Jazz.