AI-based assessment of longitudinal multiple sclerosis MRI: Strengths and weaknesses in clinical practice

Abstract

Objectives

In Multiple Sclerosis (MS) cerebral MRI is essential for disease and treatment monitoring. For this purpose, software solutions are available to support the radiologist with image interpretation and reporting of follow up imaging. Aim of this study was to evaluate an AI based software for longitudinal lesion detection with clinical data and to determine the influence of different MRI machines in such setting.

Methods

The database of a university hospital was screened for all follow up MRI of MS patients performed in 2023. The examinations were categorized in “initial and follow up imaging at the same MRI” or “initial and follow up imaging at different MRI”. The examinations were analysed with the AI based software mdbrain. The results concerning new and enlarging lesions were compared with the clinical radiologic report and with a gold standard reading.

Results

101 MRIs were performed at the same MRI machine and 130 at different scanners. The AI based software had a high sensitivity (1 and 0.786) and an acceptable specificity (0.74 and 0.549) concerning new or enlarging lesions in both settings. The negative predictive value was high (1 and 0.954), whereas the positive predictive value was low due to false positive new or enlarging lesions (0.444 and 0.177). The reasons for false positive lesions differed markedly in both groups.

Conclusion

For the evaluation of follow up MR images of MS patients, an AI-based imaging analysis can be beneficial in clinical routine, especially due to a very high negative predictive value.