Differentiation of multiple sclerosis lesions and low-grade brain tumors on MRS data: machine learning approaches

The role of AI for MRI-analysis in multiple sclerosis-A brief overview

Magnetic resonance imaging (MRI) has played a crucial role in the diagnosis, monitoring and treatment optimization of multiple sclerosis (MS). It is an essential component of current diagnostic criteria for its ability to non-invasively visualize both lesional and non-lesional pathology. Nevertheless, modern day usage of MRI in the clinic is limited by lengthy protocols, error-prone procedures for identifying disease markers (e.g., lesions), and the limited predictive value of existing imaging biomarkers for key disability outcomes. Recent advances in artificial intelligence (AI) have underscored the potential for AI to not only improve, but also transform how MRI is being used in MS. In this short review, we explore the role of AI in MS applications that span the entire life-cycle of an MRI image, from data collection, to lesion segmentation, detection, and volumetry, and finally to downstream clinical and scientific tasks. We conclude with a discussion on promising future directions.

Brain lesion MRI and co-related MRS spectroscopy dataset

Analysing brain lesions of various aetiologies necessitates imaging data with subsequent diagnostic techniques that may enable concomitant visualization of spatial anatomical entities, and aberrant molecular behaviour. To fulfil this necessity, several image types and modalities should be collected. This dataset includes MRI (Magnetic Resonance Imaging) with three modalities T2-FLAIR, T1-weighted pre- and post-contrast along with Magnetic Resonance Spectroscopy (MRS) scans for 55 patients. All patients were diagnosed with neoplastic and non-neoplastic brain lesions. T2-FLAIR and contrast-enhanced T1 MRI modalities reveal structural differences in lesions, whereas MRS affords metabolite information. In addition to the imaging data, patient metadata such as age, gender, and expert diagnosis- classify brain lesions into two types: neoplastic (low or high grade) and non-neoplastic. The data were collected between 2023 and 2024 at Al-Andalus Oncology Centre in Baghdad, Iraq, and are publicly available. This dataset is essential as it combines conventional (MRI) and metabolic (MRS) imaging with expert diagnosis information. This dataset is significant as it includes MRI and MRS images along with expert diagnoses information, offering high reuse potential in medical imaging and diagnostic research.

A density-based MS disease diagnosis model using the capuchin search algorithm and an ensemble of deep neural networks

Multiple sclerosis (MS) is a severe brain disease that permanently destroys brain cells, impacting vision, balance, muscle control, and daily activity. This research employs a weighted combination of deep neural networks and optimization techniques for MS disease diagnosis. This method uses slices of magnetic resonance imaging (MRI) images as input. Then, after the pre-processing operation, the process of segmentation and identification of the region of interest (ROI) is performed using a combination of the fuzzy c-means (FCM) algorithm and the capuchin search algorithm (CapSA) algorithm. When the target view is detected, the features of each ROI are extracted through three techniques: local binary pattern (LBP), multi-linear principal component analysis (MPCA), and gray level co-occurrence matrix (GLCM). Each of these features is then processed by a deep neural network. In each deep neural network, the CapSA algorithm is used to determine the optimal topology structure and adjust the weight vector of the neural network. This means that in this process, the vector and topology of the deep neural network are adjusted using the CapSA algorithm in such a way that the training error is minimized. Finally, after creating the trained models, the weighted combination of the outputs of these three models is used for the final diagnosis. The implementation results showed that our method was successful in achieving 100% precision compared to other comparative methods. Also, in the average accuracy criterion, it showed a performance of 99.51%, which shows the high performance of our method in diagnosing patients.

Neuroimage analysis using artificial intelligence approaches: a systematic review

In the contemporary era, artificial intelligence (AI) has undergone a transformative evolution, exerting a profound influence on neuroimaging data analysis. This development has significantly elevated our comprehension of intricate brain functions. This study investigates the ramifications of employing AI techniques on neuroimaging data, with a specific objective to improve diagnostic capabilities and contribute to the overall progress of the field. A systematic search was conducted in prominent scientific databases, including PubMed, IEEE Xplore, and Scopus, meticulously curating 456 relevant articles on AI-driven neuroimaging analysis spanning from 2013 to 2023. To maintain rigor and credibility, stringent inclusion criteria, quality assessments, and precise data extraction protocols were consistently enforced throughout this review. Following a rigorous selection process, 104 studies were selected for review, focusing on diverse neuroimaging modalities with an emphasis on mental and neurological disorders. Among these, 19.2% addressed mental illness, and 80.7% focused on neurological disorders. It is found that the prevailing clinical tasks are disease classification (58.7%) and lesion segmentation (28.9%), whereas image reconstruction constituted 7.3%, and image regression and prediction tasks represented 9.6%. AI-driven neuroimaging analysis holds tremendous potential, transforming both research and clinical applications. Machine learning and deep learning algorithms outperform traditional methods, reshaping the field significantly.

The Role of Peripheral Inflammatory Markers and Coagulation Factors in Patients with Central Nervous System (CNS) Immune Disease and Glioma

OBJECTIVE

Gliomas are associated with high rates of disability and mortality, and currently, there is a lack of specific and sensitive biomarkers for diagnosis. The ideal biomarkers should be detected early through noninvasive methods. Our research aims to develop a rapid, convenient, noninvasive diagnostic method for gliomas, as well as for grading and differentiation.

METHOD

We retrospectively collected data from patients who underwent surgery for glioma, trigeminal neuralgia/hemifacial spasmschwannoma, and those diagnosed with multiple sclerosis at our institution from January 2018 to December 2020. Inflammatory markers and coagulation factor levels were collected on admission, and neutrophil count (NLR), (WBC count minus neutrophil count) / lymphocyte count, platelet count / lymphocyte count, lymphocyte count / monocyte count, and albumin count [g/L] + total lymphocyte count × 5 were calculated for patients. Analyze the significance of biomarkers in the diagnosis and grading of gliomas, the diagnosis of MS, and the differential diagnosis of them.

RESULTS

We evaluated 155 healthy individuals, 64 trigeminal neuralgia/hemifacial spasm patients, 47 MS patients, 316 schwannoma patients, and 814 with glioma patients. Compared with healthy controls and MS group, the preoperative levels of NLR, (WBC count minus neutrophil count) / lymphocyte count, D-dimer, Fibrinogen, Antithrobin, and Factor VIII of glioma patients were significantly higher in glioma patients and positively correlated with the grade of glioma. Conversely, 0020 lymphocyte count / Monocyte count and albumin count [g/L] + total lymphocyte count × 5 were significantly lower and negatively correlated with glioma grading. ROC curves confirmed that for the diagnosis of glioma, NLR showed a maximum area under the curve value of 0.8616 (0.8322-0.8910), followed by D-dimer and Antithrombin, with area under the curve values of 0.8205 (0.7601-0.8809) and 0.8455 (0.8153-0.8758), respectively. NLR and d-dimer also showed great sensitivity in the diagnosis of MS and differential diagnosis with gliomas.

CONCLUSIONS

Our study demonstrated that multiple inflammatory markers and coagulation factors could be utilized as biomarkers for the glioma diagnosis, grading, and differential diagnosis of MS. Furthermore, the combination of these markers exhibited high sensitivity and specificity.

Concurrent glioma and multiple sclerosis: A systematic review of case reports

BACKGROUND

It is uncommon for individuals with demyelinating disease, notably multiple sclerosis (MS), to be diagnosed with intracranial gliomas. It has been debated whether or not the concurrence of these two disorders is accidental. Clinically, it may be challenging to diagnose someone who has MS and an intracranial tumor simultaneously. We conducted this systematic review to evaluate the glioma patients following MS.

METHODS

We collected 63 studies from 1672 databases from January 1990 to February 2023, and our inclusion criteria involved peer-reviewed case reports/series studies reporting concurrent MS and glioma in patients, considering various types of gliomas.

RESULTS

We included 145 cases, 51% were women and 49 % were men, with an average age of 47.4 years. Common symptoms of glioma at admission included seizures (31.2 %), hemiparesis (15.6 %), and headache (14.3 %). 75 % of patients had primarily with relapsing-remitting MS (RRMS). MS treatments included interferon(IFN)-ß (44.6 %), glatiramer acetate (GA) (21.4 %), fingolimod (19.6 %), and natalizumab (19.6 %). The average time between MS and glioma diagnosis was 12.1 years, with various timeframes. Among the 59 reported cases, 45.8 % led to patient fatalities, while the remaining 54.2 % managed to survive.

CONCLUSION

This co-occurrence, though rare, suggests potential underlying shared mechanisms or vulnerabilities, possibly at a genetic or environmental level. An interdisciplinary approach, combining the expertise of neurologists, oncologists, radiologists, and pathologists, is vital to ensure accurate diagnosis and optimal management of affected individuals. Nonetheless, there is still a significant lack of information regarding this phenomenon, necessitating large-scale population-based studies and experimental research.

Artificial Intelligence and Multiple Sclerosis: Up-to-Date Review

Multiple sclerosis (MS) remains a challenging neurological disorder for the clinician in terms of diagnosis and management. The growing integration of AI-based algorithms in healthcare offers a golden opportunity for clinicians and patients with MS. AI models are based on statistical analyses of large quantities of data from patients including "demographics, genetics, clinical and radiological presentation." These approaches are promising in the quest for greater diagnostic accuracy, tailored management plans, and better prognostication of disease. The use of AI in multiple sclerosis represents a paradigm shift in disease management. With ongoing advancements in AI technologies and the increasing availability of large-scale datasets, the potential for further innovation is immense. As AI continues to evolve, its integration into clinical practice will play a vital role in improving diagnostics, optimizing treatment strategies, and enhancing patient outcomes for MS. This review is about conducting a literature review to identify relevant studies on AI applications in MS. Only peer-reviewed studies published in the last four years have been selected. Data related to AI techniques, advancements, and implications are extracted. Through data analysis, key themes and tendencies are identified. The review presents a cohesive synthesis of the current state of AI and MS, highlighting potential implications and new advancements.

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

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.

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.

Role of artificial intelligence in MS clinical practice

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.