AI Applied to MRI: Promises and Challenges

1. Multiple sclerosis (MS) is a chronic inflammatory disease characterized by demyelinating lesions in the central nervous system. Cross-sectional measurements of acute inflammatory lesion activity are typically obtained by detecting the presence of gadolinium enhancement in lesions, which typically lasts 3-6 weeks. We formulate the novel and clinically relevant task of quantification of recent acute lesion activity from the past 24 weeks (6 months) using single-timepoint conventional brain magnetic resonance imaging (MRI). We develop and compare several deep learning (DL) methods for estimating this brain-level acuteness score and show that a 2D-UNet can accurately predict acute disease activity at the patient-level while outperforming transformers and ensemble approaches. In the context of identifying subjects with acute (less than 6 months-old) lesion activity, our 2D-UNet achieves an area under the receiver-operating curve in the range on independent relapsing-remitting MS cohorts. When used in conjunction with measurements of gadolinium-enhancing lesion activity, our model significantly improves the prognostication of future acute lesion activity (over the next 6 months). This model could thus be leveraged for population recruitment in clinical trials to identify a higher number of patients with acute inflammatory activity than current standard approaches (e.g., gadolinium positivity) with a predictable precision/recall trade-off.

Depending on the task up to 97% accurate

T1 and FLAIR as input

Three representative axial slices from one MS patient showing the CL segmentation results of an automated CL segmentation method (La Rosa et al., 2020). 7 T MP2RAGE (left column) and CL mask (right column) showing true positives (green), false negatives (red), and false positives (blue) of the automated approach with respect to the CL manual segmentation. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Gross et al. employed this method to uncover three groups based on immune cell markers in early MS, with different patterns in CD4, CD8 T cells, natural killer cell compartments, and cytokines[25]. These endotypes showed subtle variations in disease course and treatment response, suggesting potential future clinical applications