Automated assessment of brain MRIs in multiple sclerosis patients significantly reduces reading time

Abstract

INTRODUCTION: Assessment of multiple sclerosis (MS) lesions on magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. We evaluate whether assessment of new, expanding, and contrast-enhancing MS lesions can be done more time-efficiently by radiologists with assistance of artificial intelligence (AI). METHODS: Baseline and three follow-up (FU) MRIs of thirty-five consecutive patients diagnosed with MS were assessed by a radiologist manually, and with assistance of an AI-tool. Results were discussed with a consultant neuroradiologist and time metrics were evaluated. RESULTS: The mean reading time for the resident radiologist was 9.05 min (95CI: 6.85-11:25). With AI-assistance, the reading time was reduced by 2.83 min (95CI: 3.28-2.41, p < 0.001). The reading decreased steadily from baseline to FU3 for the resident radiologist (9.85 min baseline, 9.21 FU1, 8.64 FU2 and 8.44 FU3, p < 0.001). Assistance of AI further remarkably decreased reading times during follow-ups (3.29 min FU1, 3.92 FU2, 3.79 FU3, p < 0.001) but not at baseline (0.26 min, p = 0.96). The baseline reading time of the resident radiologist was 5.04 min (p < 0.001), with each lesion adding 0.14 min (p < 0.001). There was a substantial decrease in the baseline reading time from 5.04 min to 1.59 min (p = 0.23) with AI-assistance. Discussion of the reading results of the resident with the neuroradiology consultant (as usual in clinical routine) was exemplary done for FU-3 MRIs and added another 3 min (CI:2.27-3.76) to the reading time without AI-assistance. CONCLUSION: We found that AI-assisted reading of MRIs of patients with MS may be faster than evaluating these MRIs without AI-assistance.

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