Role of artificial intelligence in MS clinical practice

Bonacchi, Raffaello; Filippi, Massimo; Rocca, Maria

doi:10.1016/j.nicl.2022.103065

Cited by 32 publications

(23 citation statements)

References 133 publications

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“…However, MS diagnosis using MRI is time-consuming, tiresome, and susceptible to manual errors. Therefore, artificial intelligence (AI) is being used to automate MS diagnosis using machine learning (ML) and deep learning (DL) techniques [ 12 , 13 ]. ML is a type of AI where computers are given the opportunity to learn without being explicitly programmed, while DL is a subset of ML composed of algorithms permitting the software to train itself to perform tasks by exposing multilayered neural networks to vast amounts of data.…”

Section: Introductionmentioning

confidence: 99%

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Aslam

Khan

Bashamakh

et al. 2022

Sensors

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Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients’ data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization.

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Section: Introductionmentioning

confidence: 99%

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Aslam

Khan

Bashamakh

et al. 2022

Sensors

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“…Recent improvements in technologies and the availability of large amount of data have promoted the application of AI algorithms for the diagnostic-work up of MS [ 7 ]. Using CNN, a model of deep-learning (DL) tool able to automatically select the best problem-solving features, recent AI studies were able to discriminate between MS patients and HC with an accuracy between 70.2 and 98.8% from T2-weighted [ 67 , 68 ], FLAIR [ 69 ], or susceptibility-weighted MRI sequences [ 70 ].…”

Section: The Contribution Of Aimentioning

confidence: 99%

“…These considerations have prompted extensive research in the field of neuroimaging to identify novel MRI features more specific to MS. Moreover, the use of artificial intelligence (AI) has been recently suggested as a new promising tool for MS clinical practice [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Present and future of the diagnostic work-up of multiple sclerosis: the imaging perspective

et al. 2022

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In recent years, the use of magnetic resonance imaging (MRI) for the diagnostic work-up of multiple sclerosis (MS) has evolved considerably. The 2017 McDonald criteria show high sensitivity and accuracy in predicting a second clinical attack in patients with a typical clinically isolated syndrome and allow an earlier diagnosis of MS. They have been validated, are evidence-based, simplify the clinical use of MRI criteria and improve MS patients’ management. However, to limit the risk of misdiagnosis, they should be applied by expert clinicians only after the careful exclusion of alternative diagnoses. Recently, new MRI markers have been proposed to improve diagnostic specificity for MS and reduce the risk of misdiagnosis. The central vein sign and chronic active lesions (i.e., paramagnetic rim lesions) may increase the specificity of MS diagnostic criteria, but further effort is necessary to validate and standardize their assessment before implementing them in the clinical setting. The feasibility of subpial demyelination assessment and the clinical relevance of leptomeningeal enhancement evaluation in the diagnostic work-up of MS appear more limited. Artificial intelligence tools may capture MRI attributes that are beyond the human perception, and, in the future, artificial intelligence may complement human assessment to further ameliorate the diagnostic work-up and patients’ classification. However, guidelines that ensure reliability, interpretability, and validity of findings obtained from artificial intelligence approaches are still needed to implement them in the clinical scenario. This review provides a summary of the most recent updates regarding the application of MRI for the diagnosis of MS.

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“…DL is a class of AI algorithms that uses a neural network to mimic the visual cortex of the brain for segmentation and AIbTC [ 163 ]. It has been found that although DL models are costly in terms of computational time and storage, they are more accurate than ML strategies.…”

Section: Role Of Artificial Intelligence-based Tissue Characterizationmentioning

confidence: 99%

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

Khanna¹,

Maindarkar

Puvvula³

et al. 2022

JCDD

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The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.

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Role of artificial intelligence in MS clinical practice

Cited by 32 publications

References 133 publications

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Present and future of the diagnostic work-up of multiple sclerosis: the imaging perspective

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

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