Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review)

Abstract

Computer-aided diagnosis systems aim to assist clinicians in the early identification of abnormal signs in order to optimize the interpretation of medical images and increase diagnostic precision. Multiple sclerosis (MS) and clinically isolated syndrome (CIS) are chronic inflammatory, demyelinating diseases affecting the central nervous system. Recent advances in deep learning (DL) techniques have led to novel computational paradigms in MS and CIS imaging designed for automatic segmentation and detection of areas of interest and automatic classification of anatomic structures, as well as optimization of neuroimaging protocols. To this end, there are several publications presenting artificial intelligence-based predictive models aiming to increase diagnostic accuracy and to facilitate optimal clinical management in patients diagnosed with MS and/or CIS. The current study presents a thorough review covering DL techniques that have been applied in MS and CIS during recent years, shedding light on their current advances and limitations.

AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis

“…As MRI has been revolutionizing medical imaging and patient care for at least four decades now, with ever faster, more robust, and specialized acquisition techniques, the patient load and amount of available imaging data is exponentially growing. To still be able to handle the provided big data, radiologists can rely on support of an increasing pool of AI algorithms which promise to help during the image reporting process [ 24 ]. In order to bridge the gap between development of these AI frameworks in controlled research settings and their implementation in real-world clinical practice, studies that validate the algorithms in daily routine are indispensable.…”

“…Innovative AI techniques together with broader availability of digitalized data and advanced computer hardware promise to improve many routine radiological tasks such as acceleration of image acquisition, artifact reduction, and anomaly detection [ 16 – 21 ]. ML-based segmentation algorithms for identification and segmentation of brain lesions have significantly improved [ 12 , 22 – 24 ]. Next to segmentation of white matter lesions on non-contrast MRI [ 25 – 28 ], AI systems allow to particularly detect CE lesions in MS brain tissue [ 10 , 11 , 29 – 31 ].…”

MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies

“…Although promising, such techniques need a further extensive validation. A few studies applied AI methods to DTI data, with the aim to characterize different stages of MS ( Marzullo et al, 2019 , Oladosu et al, 2021 , Kontopodis et al, 2021 ).…”

Advanced spinal cord MRI in multiple sclerosis: Current techniques and future directions

“…Applications of artificial intelligence (AI) in brain MS research include aiding tissue class and lesion segmentation, patient classification (e.g. for differential diagnosis or between subtypes) and future disability and disease progression predictions; see reviews by Afzal et al, 2022 , Kontopodis et al, 2021 . It stands to reason that similar methods may prove equally useful with SC data.…”