Computational Intelligence Technique for Prediction of Multiple Sclerosis Based on Serum Cytokines

An analytical review on the use of artificial intelligence and machine learning in diagnosis, prediction, and risk factor analysis of multiple sclerosis

Medical research offers potential for disease prediction, like Multiple Sclerosis (MS). This neurological disorder damages nerve cell sheaths, with treatments focusing on symptom relief. Manual MS detection is time-consuming and error prone. Though MS lesion detection has been studied, limited attention has been paid to clinical analysis and computational risk factor prediction. Artificial intelligence (AI) techniques and Machine Learning (ML) methods offer accurate and effective alternatives to mapping MS progression. However, there are challenges in accessing clinical data and interdisciplinary collaboration. By analyzing 103 papers, we recognize the trends, strengths and weaknesses of AI, ML, and statistical methods applied to MS diagnosis. AI/ML-based approaches are suggested to identify MS risk factors, select significant MS features, and improve the diagnostic accuracy, such as Rule-based Fuzzy Logic (RBFL), Adaptive Fuzzy Inference System (ANFIS), Artificial Neural Network methods (ANN), Support Vector Machine (SVM), and Bayesian Networks (BNs). Meanwhile, applications of the Expanded Disability Status Scale (EDSS) and Magnetic Resonance Imaging (MRI) can enhance MS diagnostic accuracy. By examining established risk factors like obesity, smoking, and education, some research tackled the issue of disease progression. The performance metrics varied across different aspects of MS studies: Diagnosis: Sensitivity ranged from 60 % to 98 %, specificity from 60 % to 98 %, and accuracy from 61 % to 97 %. Prediction: Sensitivity ranged from 76 % to 98 %, specificity from 65 % to 98 %, and accuracy from 62 % to 99 %. Segmentation: Accuracy ranged up to 96.7 %. Classification: Sensitivity ranged from 78 % to 97.34 %, specificity from 65 % to 99.32 %, and accuracy from 71 % to 97.94 %. Furthermore, the literature shows that combining techniques can improve efficiency, exploiting their strengths for better overall performance.

Machine learning-based ensemble approach in prediction of lung cancer predisposition using XRCC1 gene polymorphism

The employment of machine learning approaches has shown promising results in predicting cancer. In the current study, polymorphisms data of five single nucleotide polymorphisms (SNPs) of DNA repair gene XRCC1 (XRCC1 399, XRCC1 194, XRCC1 206, XRCC1 632, XRCC1 280) of the north Indian population along with four smoking status data is considered as an input to the proposed ensemble model to predict the risk of individual susceptibility to the lung cancer. The prediction accuracy of the proposed ensemble model for cancer predisposition was found to be 85%. The model performance is also evaluated using sensitivity, specificity, precision and the Gini index, which is found in the range of 0.83-0.87. The proposed model also outperformed in all evaluation parameters when compared with the individual Model (LM, SVM, RF, KNN and baseline neural net). Collectively, current results suggest the potential of the proposed ensemble model in predicting the risk of cancer based on XRCC1 SNPs data.Communicated by Ramaswamy H. Sarma.

Game of microbes: the battle within - gut microbiota and multiple sclerosis

Multiple sclerosis (MS) is a chronic and progressive autoimmune disease of the central nervous system (CNS), with both genetic and environmental factors contributing to the pathobiology of the disease. While human leukocyte antigen (HLA) genes have emerged as the strongest genetic factor, consensus on environmental risk factors are lacking. Recently, trillions of microbes residing in our gut (microbiome) have emerged as a potential environmental factor linked with the pathobiology of MS as PwMS show gut microbial dysbiosis (altered gut microbiome). Thus, there has been a strong emphasis on understanding the factors (host and environmental) regulating the composition of the gut microbiota and the mechanism(s) through which gut microbes contribute to MS disease, especially through immune system modulation. A better understanding of these interactions will help harness the enormous potential of the gut microbiota as a therapeutic approach to treating MS.

Age-Dependent Effects of Transgenic 2D2 Mice Used to Induce Passive Experimental Autoimmune Encephalomyelitis in C57BL/6 Mice

INTRODUCTION

Multiple sclerosis (MS) is a neurodegenerative autoimmune disease that worsens with age. Here, we examined the influence of age on passive experimental autoimmune encephalomyelitis (P-EAE), a model to study MS, using young and mature adult 2D2 transgenic donor mice to induce pathology in WT C57BL6/J mice.

METHODS

Lymphocytes from young adult (i.e., 10-week-old) or mature adult (i.e., 6-month-old) transgenic donor mice were characterized by flow cytometry prior to injection of cultured leukocytes into adult female WT recipient mice, with a special focus on transgenic T cell phenotypes.

RESULTS

Our findings show age-dependent changes in memory T cell phenotypes correlated with more severe clinical and histological disease when donor cells originated from young as compared to mature adult mice.

CONCLUSION

Not only do these results demonstrate that the age of the 2D2 transgenic donor mice is critical in establishing P-EAE, but the differential effects might also identify age-dependent factors that contribute to EAE and perhaps MS.

A systematic review of the application of machine-learning algorithms in multiple sclerosis

INTRODUCTION

The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years.

OBJECTIVE

We present a systematic review of the application of ML algorithms in MS.

MATERIALS AND METHODS

We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords "machine learning" and "multiple sclerosis." We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected.

CONCLUSIONS

After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.

Innovations in Multiple Sclerosis Care: The Impact of Artificial Intelligence via Machine Learning on Clinical Research and Decision-Making

Artificial intelligence (AI) and its specialized subcomponent machine learning are becoming increasingly popular analytic techniques. With this growth, clinicians and health care professionals should soon expect to see an increase in diagnostic, therapeutic, and rehabilitative technologies and processes that use elements of AI. The purpose of this review is twofold. First, we provide foundational knowledge that will help health care professionals understand these modern algorithmic techniques and their implementation for classification and clustering tasks. The phrases artificial intelligence and machine learning are defined and distinguished, as are the metrics by which they are assessed and delineated. Subsequently, 7 broad categories of algorithms are discussed, and their uses explained. Second, this review highlights several key studies that exemplify advances in diagnosis, treatment, and rehabilitation for individuals with multiple sclerosis using a variety of data sources-from wearable sensors to questionnaires and serology-and elements of AI. This review will help health care professionals and clinicians better understand AI-dependent diagnostic, therapeutic, and rehabilitative techniques, thereby facilitating a greater quality of care.

Cerebrospinal fluid cytokines and chemokines exhibit distinct profiles in bacterial meningitis and viral meningitis

Differential diagnosis of bacterial meningitis (BM) and viral meningitis (VM) is a critical clinical challenge, as the early and accurate identification of the causative agent determines the appropriate treatment regimen and markedly improves patient outcomes. Clinical and experimental studies have demonstrated that the pathogen and the host immune response contribute to mortality and neurological sequelae. As BM is associated with the activation of an inflammatory cascade, the patterns of pro- and anti-inflammatory cytokines/chemokines (CTs/CKs) present in the cerebrospinal fluid (CSF) in response to the immune assault may be useful as sensitive markers for differentiating BM from VM. In the present study, the ability of CTs/CKs in the CSF to differentiate between BM and VM was investigated. For this, biochemical markers and CT/CK profiles were analysed in 145 CSF samples, divided into three groups: BM (n=61), VM (n=58) and the control group (C; n=26) comprising patients with meningism. The CSF concentrations of monocyte chemoattractant protein-1, interleukin (IL)-8, IL-1β, IL-6, macrophage inflammatory protein-1α (MIP-1α), epithelial-neutrophil activating peptide, IL-10, tumour necrosis factor-α (TNF-α), proteins and white blood cells were significantly higher and the CSF glucose level was significantly lower in the BM group compared with the VM and C groups (P<0.01). Correlation analysis identified 28 significant correlations between various CTs/CKs in the BM group (P<0.01), with the strongest positive correlations being for TNF-α/IL-6 (r=0.75), TNF-α/MIP-1α (r=0.69), TNF-α/IL-1β (r=0.64) and IL-1β/MIP-1α (r=0.64). To identify the optimum CT/CK patterns for predicting and classifying BM and VM, a dataset of 119 BM and VM samples was divided into training (n=90) and testing (n=29) subsets for use as input for a Random Forest (RF) machine learning algorithm. For the 29 test samples (15 BM and 14 VM), the RF algorithm correctly classified 28 samples, with 92% sensitivity and 93% specificity. The results show that the patterns of CT/CK levels in the CSF can be used to aid discrimination of BM and VM.

A Deep Learning Approach for Predicting Multiple Sclerosis

This paper proposes a deep learning model based on an artificial neural network with a single hidden layer for predicting the diagnosis of multiple sclerosis. The hidden layer includes a regularization term that prevents overfitting and reduces the model complexity. The purposed learning model achieved higher prediction accuracy and lower loss than four conventional machine learning techniques. A dimensionality reduction method was used to select the most relevant features from 74 gene expression profiles for training the learning models. The analysis of variance test was performed to identify the statistical difference between the mean of the proposed model and the compared classifiers. The experimental results show the effectiveness of the proposed artificial neural network.

Diagnostic performance of artificial intelligence in multiple sclerosis: a systematic review and meta-analysis

BACKGROUND

The expansion of the availability of advanced imaging methods needs more time, expertise, and resources which is in contrast to the primary goal of the imaging techniques. To overcome most of these difficulties, artificial intelligence (AI) can be used. A number of studies used AI models for multiple sclerosis (MS) diagnosis and reported diverse results. Therefore, we aim to perform a comprehensive systematic review and meta-analysis study on the role of AI in the diagnosis of MS.

METHODS

We performed a systematic search using four databases including PubMed, Scopus, Web of Science, and IEEE. Studies that applied deep learning or AI to the diagnosis of MS based on any modalities were considered eligible in our study. The accuracy, sensitivity, specificity, precision, and area under curve (AUC) were pooled with a random-effects model and 95% confidence interval (CI).

RESULTS

After the screening, 41 articles with 5989 individuals met the inclusion criteria and were included in our qualitative and quantitative synthesis. Our analysis showed that the overall accuracy among studies was 94% (95%CI: 93%, 96%). The pooled sensitivity and specificity were 92% (95%CI: 90%, 95%) and 93% (95%CI: 90%, 96%), respectively. Furthermore, our analysis showed 92% precision in MS diagnosis for AI studies (95%CI: 88%, 97%). Also, the overall pooled AUC was 93% (95%CI: 89%, 96%).

CONCLUSION

Overall, AI models can further improve our diagnostic practice in MS patients. Our results indicate that the use of AI can aid the clinicians in accurate diagnosis of MS and improve current diagnostic approaches as most of the parameters including accuracy, sensitivity, specificity, precision, and AUC were considerably high, especially when using MRI data.

Using Machine Learning Methods to Predict the ß-Poly (L-Malic Acid) Production by Different Substrates Addition and Secondary Indexes in Strain Aureobasidium melanogenum

ß-poly (L-malic acid) (PMLA) is a polyester ligated by malate subunits. It has a wide prospective application as an anti-cancer drug carrier, and its malate subunits have a great application in the food industry. The strain Aureoabsidium melanogenum could produce a high amount of PMLA during fermentation, and different substrates addition could influence the production. In this study, we directly added potassium acetate, corn steep liquor, MgSO4, MnSO4, vitamin B1, vitamin B2, and nicotinamide as the fermentation substrate to the basic fermentation medium based on a generated random matrix that represented the added value. The PMLA production and four secondary indexes, pH, biomass, osmotic pressure, and viscosity were measured after 144 h fermentation. Finally, a total of 212 samples were collected as the dataset, by which the machine learning methods were deployed to predict the PMLA production by different substrates’ concentrations and the secondary indexes. The results indicated that PMLA production was negatively correlated with corn steep liquor and betaine and positively correlated with potassium acetate. The PMLA production could be predicted using all different substrates’ concentrations with a Mean Absolute Error (MAE) of 4.164 g/L and with an MAE of 6.556 g/L by different secondary indexes. Finally, the convolutional neural network (CNN) was applied to predict the PMLA production by fermentation medium images, in which the collected images were categorized into three groups, 0–20 g/L, 21–40 g/L, and >41 g/L, based on the PMLA production. The CNN model could predict the production with high accuracy. The methods and results presented in this study provided new insight into evaluating different substrates concentration on PMLA production and demonstrating the possibility of using the convolutional neural network model in the PMLA fermentation industry.

Prophylactic effects of cucurbitacin B in the EAE Model of multiple sclerosis by adjustment of STAT3/IL-23/IL-17 axis and improvement of neuropsychological symptoms

Multiple sclerosis (MS) is an autoimmune disease that affects the central nervous system. Although remarkable progress has been made in treating MS, current therapies are less effective in protecting against the progression of the disease. Since cucurbitacins have shown an extreme range of pharmacological properties, in this study, we aimed to investigate the prophylactic effect of cucurbitacin B (CuB) in the experimental MS model. Experimental autoimmune encephalomyelitis (EAE) induced by subcutaneous immunization of MOG35-55 in C57BL/6 mice. CuB interventions (0.5 and 1 mg/kg, i.p.) were performed every other day from the first day of EAE induction. Assessment of clinical scores and motor function, inflammatory responses, and microglial activation were assessed by qRT-PCR, western blotting, and immunohistochemical (IHC) analyses. CuB (1 mg/kg) significantly decreased the population of CD45⁺ (P < 0.01), CD11b⁺ (P < 0.01) and CD45⁺/CD11b⁺ (P < 0.05) cells in cortical lesions of EAE mice. In addition, activation of STAT3 (P < 0.001), expression of IL-17 A and IL-23 A (both mRNA and protein), and transcription of Iba-1 significantly decreased. On the contrary, CuB (1 mg/kg) significantly increased the transcription of MBP and Olig-2. Furthermore, a significant decrease in the severity of EAE (P < 0.05), and an improvement in motor function (P < 0.05) and coordination (P < 0.05) were observed after treatment with a high dose of CuB. Our results suggest that CuB may have a wide-ranging effect on autoimmune responses in MS via a reduction in STAT3 activation, microgliosis, and adaptation of the IL-23/IL-17 axis. Further studies are needed to investigate the exact effect of CuB in glial cells and its efficiency and bioavailability in other neuroinflammatory diseases.

Analysis of herpesvirus infection and genome single nucleotide polymorphism risk factors in multiple sclerosis, Volga federal district, Russia

Multiple sclerosis (MS) is a heterogeneous disease where herpesvirus infection and genetic predisposition are identified as the most consistent risk factors. Serum and blood samples were collected from 151 MS and 70 controls and used to analyze circulating antibodies for, and DNA of, Epstein Barr virus (EBV), human cytomegalovirus (HCMV), human herpes virus 6 (HHV6), and varicella zoster virus (VZV). The frequency of selected single nucleotide polymorphisms (SNPs) in MS and controls were studied. Herpesvirus DNA in blood samples were analyzed using qPCR. Anti-herpesvirus antibodies were detected by ELISA. SNPs were analyzed by the allele-specific PCR. For statistical analysis, Fisher exact test, odds ratio and Kruskall-Wallis test were used; p<0.05 values were considered as significant. We have found an association between circulating anti-HHV6 antibodies and MS diagnosis. We also confirmed higher frequency of A and C alleles in rs2300747 and rs12044852 of CD58 gene and G allele in rs929230 of CD6 gene in MS as compared to controls. Fatigue symptom was linked to AC and AA genotype in rs12044852 of CD58 gene. An interesting observation was finding higher frequency of GG genotype in rs12722489 of IL2RA and T allele in rs1535045 of CD40 genes in patient having anti-HHV6 antibodies. A link was found between having anti-VZV antibodies in MS and CC genotype in rs1883832 of CD40 gene.

Multiplex Analysis of Serum Cytokine Profiles in Systemic Lupus Erythematosus and Multiple Sclerosis

Changes in cytokine profiles and cytokine networks are known to be a hallmark of autoimmune diseases, including systemic lupus erythematosus (SLE) and multiple sclerosis (MS). However, cytokine profiles research studies are usually based on the analysis of a small number of cytokines and give conflicting results. In this work, we analyzed cytokine profiles of 41 analytes in patients with SLE and MS compared with healthy donors using multiplex immunoassay. The SLE group included treated patients, while the MS patients were drug-free. Levels of 11 cytokines, IL-1b, IL-1RA, IL-6, IL-9, IL-10, IL-15, MCP-1/CCL2, Fractalkine/CX3CL1, MIP-1a/CCL3, MIP-1b/CCL4, and TNFa, were increased, but sCD40L, PDGF-AA, and MDC/CCL22 levels were decreased in SLE patients. Thus, changes in the cytokine profile in SLE have been associated with the dysregulation of interleukins, TNF superfamily members, and chemokines. In the case of MS, levels of 10 cytokines, sCD40L, CCL2, CCL3, CCL22, PDGF-AA, PDGF-AB/BB, EGF, IL-8, TGF-a, and VEGF, decreased significantly compared to the control group. Therefore, cytokine network dysregulation in MS is characterized by abnormal levels of growth factors and chemokines. Cross-disorder analysis of cytokine levels in MS and SLE showed significant differences between 22 cytokines. Protein interaction network analysis showed that all significantly altered cytokines in both SLE and MS are functionally interconnected. Thus, MS and SLE may be associated with impaired functional relationships in the cytokine network. A cytokine correlation networks analysis revealed changes in correlation clusters in SLE and MS. These data expand the understanding of abnormal regulatory interactions in cytokine profiles associated with autoimmune diseases.

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

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.

Autoreactive lymphocytes in multiple sclerosis: Pathogenesis and treatment target

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS) characterized by destruction of the myelin sheath structure. The loss of myelin leads to damage of a neuron’s axon and cell body, which is identified as brain lesions on magnetic resonance image (MRI). The pathogenesis of MS remains largely unknown. However, immune mechanisms, especially those linked to the aberrant lymphocyte activity, are mainly responsible for neuronal damage. Th1 and Th17 populations of lymphocytes were primarily associated with MS pathogenesis. These lymphocytes are essential for differentiation of encephalitogenic CD8⁺ T cell and Th17 lymphocyte crossing the blood brain barrier and targeting myelin sheath in the CNS. B-lymphocytes could also contribute to MS pathogenesis by producing anti-myelin basic protein antibodies. In later studies, aberrant function of Treg and Th9 cells was identified as contributing to MS. This review summarizes the aberrant function and count of lymphocyte, and the contributions of these cell to the mechanisms of MS. Additionally, we have outlined the novel MS therapeutics aimed to amend the aberrant function or counts of these lymphocytes.

The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review

BACKGROUND

Multiple sclerosis (MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential. Machine learning (ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging.

METHODS

Systematic searches through eight databases were conducted for literature published in 2014-2020 on MS and specified ML algorithms.

RESULTS

Of the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms.

CONCLUSIONS

ML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease.

Multiple sclerosis diagnosis and phenotype identification by multivariate classification of in vivo frontal cortex metabolite profiles

Multiple sclerosis (MS) is a heterogeneous autoimmune disease for which diagnosis continues to rely on subjective clinical judgment over a battery of tests. Proton magnetic resonance spectroscopy (¹H MRS) enables the noninvasive in vivo detection of multiple small-molecule metabolites and is therefore in principle a promising means of gathering information sufficient for multiple sclerosis diagnosis and subtype classification. Here we show that supervised classification using ¹H-MRS-visible normal-appearing frontal cortex small-molecule metabolites alone can indeed differentiate individuals with progressive MS from control (held-out validation sensitivity 79% and specificity 68%), as well as between relapsing and progressive MS phenotypes (held-out validation sensitivity 84% and specificity 74%). Post hoc assessment demonstrated the disproportionate contributions of glutamate and glutamine to identifying MS status and phenotype, respectively. Our finding establishes ¹H MRS as a viable means of characterizing progressive multiple sclerosis disease status and paves the way for continued refinement of this method as an auxiliary or mainstay of multiple sclerosis diagnostics.

Role of the gut microbiome in multiple sclerosis: From etiology to therapeutics

Multiple sclerosis (MS) is a chronic inflammatory disease of the CNS that affects around one million people in the United States. Predisposition or protection from this disease is linked with both genetic and environmental factors. In recent years, gut microbiome has emerged as an important environmental factor in the pathobiology of MS. The gut microbiome supports various physiologic functions, including the development and maintenance of the host immune system, the perturbation of which is known as dysbiosis and has been linked with multiple diseases including MS. We and others have shown that people with MS (PwMS) have gut dysbiosis that is characterized by specific gut bacteria being enriched or depleted. Consequently, there is an emphasis on determining the mechanism(s) through which gut bacteria and/or their metabolites alter the course of MS through their ability to provide protection, predispose individuals, or promote disease progression. Improving our understanding of these mechanisms will allow us to harness the enormous potential of the gut microbiome as a diagnostic and/or therapeutic agent. In this chapter, we will discuss current advances in microbiome research in the context of MS, including a review of specific bacteria that are currently linked with this disease, potential mechanisms of disease pathogenesis, and the utility of microbiome-based therapy for PwMS.

Role of artificial intelligence in MS clinical practice

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For medical applications, machine learning (including deep learning) are the most commonly used artificial intelligence (AI) approaches.

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It can improve multiple sclerosis (MS) diagnosis, prognostication and treatment monitoring.

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Thanks to AI, MRI and cognitive phenotypes of MS patients were identified.

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AI can shorten MRI protocols for MS, allowing the application of advanced techniques.

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It can reduce the human effort for MRI analysis, especially for lesion segmentation.

Machine learning (ML) and its subset, deep learning (DL), are branches of artificial intelligence (AI) showing promising findings in the medical field, especially when applied to imaging data. Given the substantial role of MRI in the diagnosis and management of patients with multiple sclerosis (MS), this disease is an ideal candidate for the application of AI techniques. In this narrative review, we are going to discuss the potential applications of AI for MS clinical practice, together with their limitations. Among their several advantages, ML algorithms are able to automate repetitive tasks, to analyze more data in less time and to achieve higher accuracy and reproducibility than the human counterpart. To date, these algorithms have been applied to MS diagnosis, prognosis, disease and treatment monitoring. Other fields of application have been improvement of MRI protocols as well as automated lesion and tissue segmentation. However, several challenges remain, including a better understanding of the information selected by AI algorithms, appropriate multicenter and longitudinal validations of results and practical aspects regarding hardware and software integration. Finally, one cannot overemphasize the paramount importance of human supervision, in order to optimize the use and take full advantage of the potential of AI approaches.

Digital Twins for Multiple Sclerosis

An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient's characteristics, is still far from being part of the everyday care of pwMS. The development of digital twins could decisively advance the necessary implementation of an individualized innovative management of MS. Through artificial intelligence-based analysis of several disease parameters - including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient's life circumstances and plans, and medical procedures - a digital twin paired to the patient's characteristic can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to a more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication and facilitating a shared decision-making. With a clear display of pre-analyzed patient data on a dashboard, patient participation and individualized clinical decisions as well as the prediction of disease progression and treatment simulation could become possible. In this review, we focus on the advantages, challenges and practical aspects of digital twins in the management of MS. We discuss the use of digital twins for MS as a revolutionary tool to improve diagnosis, monitoring and therapy refining patients' well-being, saving economic costs, and enabling prevention of disease progression. Digital twins will help make precision medicine and patient-centered care a reality in everyday life.

An evaluation of the recognised systemic inflammatory biomarkers of chronic sub-optimal inflammation provides evidence for inflammageing (IFA) during multiple sclerosis (MS)

The pathogenesis of the human demyelinating disorder multiple sclerosis (MS) involves the loss of immune tolerance to self-neuroantigens. A deterioration in immune tolerance is linked to inherent immune ageing, or immunosenescence (ISC). Previous work by the author has confirmed the presence of ISC during MS. Moreover, evidence verified a prematurely aged immune system that may change the frequency and profile of MS through an altered decline in immune tolerance. Immune ageing is closely linked to a chronic systemic sub-optimal inflammation, termed inflammageing (IFA), which disrupts the efficiency of immune tolerance by varying the dynamics of ISC that includes accelerated changes to the immune system over time. Therefore, a shifting deterioration in immunological tolerance may evolve during MS through adversely-scheduled effects of IFA on ISC. However, there is, to date, no collective proof of ongoing IFA during MS. The Review addresses the constraint and provides a systematic critique of compelling evidence, through appraisal of IFA-related biomarker studies, to support the occurrence of a sub-optimal inflammation during MS. The findings justify further work to unequivocally demonstrate IFA in MS and provide additional insight into the complex pathology and developing epidemiology of the disease.

Current review and next steps for artificial intelligence in multiple sclerosis risk research

In the last few decades, the prevalence of multiple sclerosis (MS), a chronic inflammatory disease of the nervous system, has increased, particularly in Northern European countries, the United States, and United Kingdom. The promise of artificial intelligence (AI) and machine learning (ML) as tools to address problems in MS research has attracted increasing interest in these methods. Bayesian networks offer a clear advantage since they can integrate data and causal knowledge allowing for visualizing interactions between dependent variables and potential confounding factors. A review of AI/ML research methods applied to MS found 216 papers using terms "Multiple Sclerosis", "machine learning", "artificial intelligence", "Bayes", and "Bayesian", of which 90 were relevant and recently published. More than half of these involve the detection and segmentation of MS lesions for quantitative analysis; however clinical and lifestyle risk factor assessment and prediction have largely been ignored. Of those that address risk factors, most provide only association studies for some factors and often fail to include the potential impact of confounding factors and bias (especially where these have causal explanations) that could affect data interpretation, such as reporting quality and medical care access in various countries. To address these gaps in the literature, we propose a causal Bayesian network approach to assessing risk factors for MS, which can address deficiencies in current epidemiological methods of producing risk measurements and makes better use of observational data.

A systematic review of the application of machine-learning algorithms in multiple sclerosis

INTRODUCTION

The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years.

OBJECTIVE

We present a systematic review of the application of ML algorithms in MS.

MATERIALS AND METHODS

We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords "machine learning" and "multiple sclerosis." We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected.

CONCLUSIONS

After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.

Serum and Cerebrospinal Fluid Cytokine Biomarkers for Diagnosis of Multiple Sclerosis

Background

Multiple sclerosis (MS) is a chronic debilitating disorder characterized by persisting damage to the brain caused by autoreactive leukocytes. Leukocyte activation is regulated by cytokines, which are readily detected in MS serum and cerebrospinal fluid (CSF).

Objective

Serum and CSF levels of forty-five cytokines were analyzed to identify MS diagnostic markers.

Methods

Cytokines were analyzed using multiplex immunoassay. ANOVA-based feature and Pearson correlation coefficient scores were calculated to select the features which were used as input by machine learning models, to predict and classify MS.

Results

Twenty-two and twenty cytokines were altered in CSF and serum, respectively. The MS diagnosis accuracy was ≥92% when any randomly selected five of these biomarkers were used. Interestingly, the highest accuracy (99%) of MS diagnosis was demonstrated when CCL27, IFN-γ, and IL-4 were part of the five selected cytokines, suggesting their important role in MS pathogenesis. Also, these binary classifier models had the accuracy in the range of 70-78% (serum) and 60-69% (CSF) to discriminate between the progressive (primary and secondary progressive) and relapsing-remitting forms of MS.

Conclusion

We identified the set of cytokines from the serum and CSF that could be used for the MS diagnosis and classification.

The emergence of new trends in clinical laboratory diagnosis

Diagnostic processes typically rely on traditional and laborious methods, that are prone to human error, resulting in frequent misdiagnosis of diseases. Computational approaches are being increasingly used for more precise diagnosis of the clinical pathology, diagnosis of genetic and microbial diseases, and analysis of clinical chemistry data. These approaches are progressively used for improving the reliability of testing, resulting in reduced diagnostic errors. Artificial intelligence (AI)-based computational approaches mostly rely on training sets obtained from patient data stored in clinical databases. However, the use of AI is associated with several ethical issues, including patient privacy and data ownership. The capacity of AI-based mathematical models to interpret complex clinical data frequently leads to data bias and reporting of erroneous results based on patient data. In order to improve the reliability of computational approaches in clinical diagnostics, strategies to reduce data bias and analyzing real-life patient data need to be further refined.

Analysis of Polymorphisms rs7093069-IL-2RA, rs7138803-FAIM2, and rs1748033-PADI4 in the Group of Adolescents With Autoimmune Thyroid Diseases

INTRODUCTION

The pathogenesis of autoimmune thyroid diseases is complicated and not completely known. Among the causes of thyroid autoimmunity, we distinguish genetic predisposition and environmental factors. Graves' disease and Hashimoto's thyroiditis are associated with a disturbance of immune tolerance of thyroid antigen molecules. The IL2RA gene is located on chromosome 10 and encodes the interleukin 2 receptor (IL2RA), which is expressed by the regulatory T-cells (Tregs) responsible for suppression. It has been shown that this gene and its polymorphism occur in people with various autoimmune diseases (e.g. type 1 diabetes mellitus, rheumatoid arthritis, Graves' disease, or multiple sclerosis). The FAIM2 gene is located on chromosome 12 and encodes the molecule involved in the apoptosis inhibition process. The PADI4 gene is located on chromosome 1, and its expression is associated with activation of T-cells, differentiation of macrophages, which leads to increased inflammation.

AIM

The aim of the study was to analyze the polymorphisms of the IL-2RA (rs7093069), FAIM2 (rs7138803) and PADI4 (rs1748033) genes and their correlation to thyroid hormones and anti-thyroid antibodies in pediatric patients with Graves' disease and Hashimoto's thyroiditis compared to the control group.

MATERIAL AND METHODS

The study was performed in 180 patients with GD (mean age 16.5 ± 2), 80 with HT (mean age, 15.2 ± 2.2), and 114 children without any autoimmune diseases (mean age 16.3 ± 3) recruited from the endocrinology outpatient clinic. Three single nucleotide polymorphisms (SNPs): rs7138803-FAIM2, rs7093069-IL-2RA, and rs1748033 PADI4 were determined by TaqMan SNP QuanStudio 12K Flex-OpenArray genotyping with PCR and correlated to thyroid hormones and anti-thyroid antibodies.

RESULTS

Rs7090369-IL-2RA allele T was more frequent in patients with AITDs (33.7% in GD vs 28.7% in HT, p = 0.077, OR = 1.52) compared with healthy children (25%). Allele T of that gene predisposes to the occurrence of autoimmune thyroid diseases, especially GD and TT genotype gives a statistically significant 5.2 times higher risk of GD (p = 0.03, OR = 5.26) and increased risk of HT (p = 0.109, OR = 4.46). Allele A rs7138803-FAIM2 is more frequent in patients with GD (p = 0.071, OR = 1.45) and HT (p = 0.028, OR = 1.8). In our data the presence of GG genotype of that gene significantly reduces the risk of autoimmune thyroid diseases (p = 0.05, OR = 0.42). Allele C rs1748033PADI4 and its CC genotype were more frequent in patients with autoimmune thyroid diseases, but it was not statistically significant. The occurrence of CT genotype significantly reduces the risk of HT (p = 0.03, OR = 0.4).

CONCLUSIONS

1). Polymorphisms rs7138803-FAIM2 and rs1748033-PADI4 are more frequent in patients with autoimmune thyroid diseases, more frequent in patients with Hashimoto' thyroiditis, but the occurrence of GG rs7138803-FAIM2 genotype could reduce the risk of thyrocyte apoptosis inhibition. 2). The TT rs7093069-IL2RA genotype may increase the risk of autoimmune thyroid diseases. 3). Analysis of polymorphisms of given genes in clinical practice will allow to determine predisposition to autoimmune thyroid disease development, to find symptoms of thyroid gland dysfunction earlier and to use appropriate treatment.