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Zhou X, Cai F, Li S, Li G, Zhang C, Xie J, Yang Y. Machine learning techniques for prediction in pregnancy complicated by autoimmune rheumatic diseases: Applications and challenges. Int Immunopharmacol 2024; 134:112238. [PMID: 38735259 DOI: 10.1016/j.intimp.2024.112238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 05/01/2024] [Accepted: 05/08/2024] [Indexed: 05/14/2024]
Abstract
Autoimmune rheumatic diseases are chronic conditions affecting multiple systems and often occurring in young women of childbearing age. The diseases and the physiological characteristics of pregnancy significantly impact maternal-fetal health and pregnancy outcomes. Currently, the integration of big data with healthcare has led to the increasing popularity of using machine learning (ML) to mine clinical data for studying pregnancy complications. In this review, we introduce the basics of ML and the recent advances and trends of ML in different prediction applications for common pregnancy complications by autoimmune rheumatic diseases. Finally, the challenges and future for enhancing the accuracy, reliability, and clinical applicability of ML in prediction have been discussed. This review will provide insights into the utilization of ML in identifying and assisting clinical decision-making for pregnancy complications, while also establishing a foundation for exploring comprehensive management strategies for pregnancy and enhancing maternal and child health.
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Affiliation(s)
- Xiaoshi Zhou
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Feifei Cai
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Shiran Li
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Guolin Li
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Changji Zhang
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Jingxian Xie
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; College of Pharmacy, Southwest Medical University, Luzhou, China
| | - Yong Yang
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
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Mayfield JD, Murtagh R, Ciotti J, Robertson D, Naqa IE. Time-Dependent Deep Learning Prediction of Multiple Sclerosis Disability. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01031-y. [PMID: 38871944 DOI: 10.1007/s10278-024-01031-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/05/2024] [Accepted: 01/23/2024] [Indexed: 06/15/2024]
Abstract
The majority of deep learning models in medical image analysis concentrate on single snapshot timepoint circumstances, such as the identification of current pathology on a given image or volume. This is often in contrast to the diagnostic methodology in radiology where presumed pathologic findings are correlated to prior studies and subsequent changes over time. For multiple sclerosis (MS), the current body of literature describes various forms of lesion segmentation with few studies analyzing disability progression over time. For the purpose of longitudinal time-dependent analysis, we propose a combinatorial analysis of a video vision transformer (ViViT) benchmarked against traditional recurrent neural network of Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architectures and a hybrid Vision Transformer-LSTM (ViT-LSTM) to predict long-term disability based upon the Extended Disability Severity Score (EDSS). The patient cohort was procured from a two-site institution with 703 patients' multisequence, contrast-enhanced MRIs of the cervical spine between the years 2002 and 2023. Following a competitive performance analysis, a VGG-16-based CNN-LSTM was compared to ViViT with an ablation analysis to determine time-dependency of the models. The VGG16-LSTM predicted trinary classification of EDSS score in 6 years with 0.74 AUC versus the ViViT with 0.84 AUC (p-value < 0.001 per 5 × 2 cross-validation F-test) on an 80:20 hold-out testing split. However, the VGG16-LSTM outperformed ViViT when patients with only 2 years of MRIs (n = 94) (0.75 AUC versus 0.72 AUC, respectively). Exact EDSS classification was investigated for both models using both classification and regression strategies but showed collectively worse performance. Our experimental results demonstrate the ability of time-dependent deep learning models to predict disability in MS using trinary stratification of disability, mimicking clinical practice. Further work includes external validation and subsequent observational clinical trials.
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Affiliation(s)
- John D Mayfield
- USF Health Department of Radiology, 2 Tampa General Circle, STC 6103, Tampa, FL, 33612, USA.
| | - Ryan Murtagh
- USF Health Department of Radiology, 2 Tampa General Circle, STC 6103, Tampa, FL, 33612, USA
| | - John Ciotti
- Department of Neurology, University of South Florida, Morsani College of Medicine, USF Multiple Sclerosis Center, 13330 USF Laurel Drive, Tampa, FL, 33612, USA
| | - Derrick Robertson
- Department of Neurology, James A. Haley VA Medical Center, 13000 Bruce B Downs Blvd, Tampa, FL, 33612, USA
| | - Issam El Naqa
- University of South Florida, College of Engineering, 12902 USF Magnolia Drive, Tampa, FL, 33612, USA
- H. Lee Moffitt Cancer Center Department of Machine Learning, Tampa, FL, 33612, USA
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Suh A, Hampel G, Vinjamuri A, Ong J, Kamran SA, Waisberg E, Paladugu P, Zaman N, Sarker P, Tavakkoli A, Lee AG. Oculomics analysis in multiple sclerosis: Current ophthalmic clinical and imaging biomarkers. Eye (Lond) 2024:10.1038/s41433-024-03132-y. [PMID: 38858520 DOI: 10.1038/s41433-024-03132-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 03/18/2024] [Accepted: 05/07/2024] [Indexed: 06/12/2024] Open
Abstract
Multiple Sclerosis (MS) is a chronic autoimmune demyelinating disease of the central nervous system (CNS) characterized by inflammation, demyelination, and axonal damage. Early recognition and treatment are important for preventing or minimizing the long-term effects of the disease. Current gold standard modalities of diagnosis (e.g., CSF and MRI) are invasive and expensive in nature, warranting alternative methods of detection and screening. Oculomics, the interdisciplinary combination of ophthalmology, genetics, and bioinformatics to study the molecular basis of eye diseases, has seen rapid development through various technologies that detect structural, functional, and visual changes in the eye. Ophthalmic biomarkers (e.g., tear composition, retinal nerve fibre layer thickness, saccadic eye movements) are emerging as promising tools for evaluating MS progression. The eye's structural and embryological similarity to the brain makes it a potentially suitable assessment of neurological and microvascular changes in CNS. In the advent of more powerful machine learning algorithms, oculomics screening modalities such as optical coherence tomography (OCT), eye tracking, and protein analysis become more effective tools aiding in MS diagnosis. Artificial intelligence can analyse larger and more diverse data sets to potentially discover new parameters of pathology for efficiently diagnosing MS before symptom onset. While there is no known cure for MS, the integration of oculomics with current modalities of diagnosis creates a promising future for developing more sensitive, non-invasive, and cost-effective approaches to MS detection and diagnosis.
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Affiliation(s)
- Alex Suh
- Tulane University School of Medicine, New Orleans, LA, USA.
| | - Gilad Hampel
- Tulane University School of Medicine, New Orleans, LA, USA
| | | | - Joshua Ong
- Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Sharif Amit Kamran
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA
| | - Ethan Waisberg
- University College Dublin School of Medicine, Belfield, Dublin, Ireland
| | - Phani Paladugu
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Nasif Zaman
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA
| | - Prithul Sarker
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA
| | - Alireza Tavakkoli
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA
| | - Andrew G Lee
- Center for Space Medicine, Baylor College of Medicine, Houston, TX, USA
- Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, TX, USA
- The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, TX, USA
- Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, NY, USA
- Department of Ophthalmology, University of Texas Medical Branch, Galveston, TX, USA
- University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Texas A&M College of Medicine, Galveston, TX, USA
- Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, IA, USA
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Carvalho Macruz FBD, Dias ALMP, Andrade CS, Nucci MP, Rimkus CDM, Lucato LT, Rocha AJD, Kitamura FC. The new era of artificial intelligence in neuroradiology: current research and promising tools. ARQUIVOS DE NEURO-PSIQUIATRIA 2024; 82:1-12. [PMID: 38565188 PMCID: PMC10987255 DOI: 10.1055/s-0044-1779486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/13/2023] [Indexed: 04/04/2024]
Abstract
Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.
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Affiliation(s)
- Fabíola Bezerra de Carvalho Macruz
- Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.
- Rede D'Or São Luiz, Departamento de Radiologia e Diagnóstico por Imagem, São Paulo SP, Brazil.
- Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.
- Academia Nacional de Medicina, Rio de Janeiro RJ, Brazil.
| | | | | | - Mariana Penteado Nucci
- Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.
| | - Carolina de Medeiros Rimkus
- Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.
- Rede D'Or São Luiz, Departamento de Radiologia e Diagnóstico por Imagem, São Paulo SP, Brazil.
- Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.
| | - Leandro Tavares Lucato
- Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.
- Diagnósticos da América SA, São Paulo SP, Brazil.
| | | | - Felipe Campos Kitamura
- Diagnósticos da América SA, São Paulo SP, Brazil.
- Universidade Federal de São Paulo, São Paulo SP, Brazil.
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Liang X, Yan Z, Li Y. Exploring subtypes of multiple sclerosis through unsupervised machine learning of automated fiber quantification. Jpn J Radiol 2024; 42:581-589. [PMID: 38409299 DOI: 10.1007/s11604-024-01535-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 01/14/2024] [Indexed: 02/28/2024]
Abstract
PURPOSE This study aimed to subtype multiple sclerosis (MS) patients using unsupervised machine learning on white matter (WM) fiber tracts and investigate the implications for cognitive function and disability outcomes. MATERIALS AND METHODS We utilized the automated fiber quantification (AFQ) method to extract 18 WM fiber tracts from the imaging data of 103 MS patients in total. Unsupervised machine learning techniques were applied to conduct cluster analysis and identify distinct subtypes. Clinical and diffusion tensor imaging (DTI) metrics were compared among the subtypes, and survival analysis was conducted to examine disability progression and cognitive impairment. RESULTS The clustering analysis revealed three distinct subtypes with variations in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Significant differences were observed in clinical and DTI metrics among the subtypes. Subtype 3 showed the fastest disability progression and cognitive decline, while Subtype 2 exhibited a slower rate, and Subtype 1 fell in between. CONCLUSIONS Subtyping MS based on WM fiber tracts using unsupervised machine learning identified distinct subtypes with significant cognitive and disability differences. WM abnormalities may serve as biomarkers for predicting disease outcomes, enabling personalized treatment strategies and prognostic predictions for MS patients.
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Affiliation(s)
- Xueheng Liang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No 1 Youyi Road, Yuzhong District, Chongqing, 40016, China
- Department of Radiology, Banan Hospital of Chongqing Medical University, Chongqing, China
| | - Zichun Yan
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No 1 Youyi Road, Yuzhong District, Chongqing, 40016, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No 1 Youyi Road, Yuzhong District, Chongqing, 40016, China.
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Prathapan V, Eipert P, Wigger N, Kipp M, Appali R, Schmitt O. Modeling and simulation for prediction of multiple sclerosis progression. Comput Biol Med 2024; 175:108416. [PMID: 38657465 DOI: 10.1016/j.compbiomed.2024.108416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 03/28/2024] [Accepted: 04/03/2024] [Indexed: 04/26/2024]
Abstract
In light of extensive work that has created a wide range of techniques for predicting the course of multiple sclerosis (MS) disease, this paper attempts to provide an overview of these approaches and put forth an alternative way to predict the disease progression. For this purpose, the existing methods for estimating and predicting the course of the disease have been categorized into clinical, radiological, biological, and computational or artificial intelligence-based markers. Weighing the weaknesses and strengths of these prognostic groups is a profound method that is yet in need and works directly at the level of diseased connectivity. Therefore, we propose using the computational models in combination with established connectomes as a predictive tool for MS disease trajectories. The fundamental conduction-based Hodgkin-Huxley model emerged as promising from examining these studies. The advantage of the Hodgkin-Huxley model is that certain properties of connectomes, such as neuronal connection weights, spatial distances, and adjustments of signal transmission rates, can be taken into account. It is precisely these properties that are particularly altered in MS and that have strong implications for processing, transmission, and interactions of neuronal signaling patterns. The Hodgkin-Huxley (HH) equations as a point-neuron model are used for signal propagation inside a small network. The objective is to change the conduction parameter of the neuron model, replicate the changes in myelin properties in MS and observe the dynamics of the signal propagation across the network. The model is initially validated for different lengths, conduction values, and connection weights through three nodal connections. Later, these individual factors are incorporated into a small network and simulated to mimic the condition of MS. The signal propagation pattern is observed after inducing changes in conduction parameters at certain nodes in the network and compared against a control model pattern obtained before the changes are applied to the network. The signal propagation pattern varies as expected by adapting to the input conditions. Similarly, when the model is applied to a connectome, the pattern changes could give an insight into disease progression. This approach has opened up a new path to explore the progression of the disease in MS. The work is in its preliminary state, but with a future vision to apply this method in a connectome, providing a better clinical tool.
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Affiliation(s)
- Vishnu Prathapan
- Medical School Hamburg University of Applied Sciences and Medical University, Am Kaiserkai 1, 20457, Hamburg, Germany.
| | - Peter Eipert
- Medical School Hamburg University of Applied Sciences and Medical University, Am Kaiserkai 1, 20457, Hamburg, Germany.
| | - Nicole Wigger
- Department of Anatomy, University of Rostock Gertrudenstr 9, 18057, Rostock, Germany.
| | - Markus Kipp
- Department of Anatomy, University of Rostock Gertrudenstr 9, 18057, Rostock, Germany.
| | - Revathi Appali
- Institute of General Electrical Engineering, University of Rostock, Albert-Einstein-Straße 2, 18059, Rostock, Germany; Department of Aging of Individuals and Society, Interdisciplinary Faculty, University of Rostock, Universitätsplatz 1, 18055, Rostock, Germany.
| | - Oliver Schmitt
- Medical School Hamburg University of Applied Sciences and Medical University, Am Kaiserkai 1, 20457, Hamburg, Germany; Department of Anatomy, University of Rostock Gertrudenstr 9, 18057, Rostock, Germany.
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Xu M, Wang H, Ren S, Wang B, Yang W, Lv L, Sha X, Li W, Wang Y. Identification of crucial inflammaging related risk factors in multiple sclerosis. Front Mol Neurosci 2024; 17:1398665. [PMID: 38836117 PMCID: PMC11148336 DOI: 10.3389/fnmol.2024.1398665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Accepted: 04/30/2024] [Indexed: 06/06/2024] Open
Abstract
Background Multiple sclerosis (MS) is an immune-mediated disease characterized by inflammatory demyelinating lesions in the central nervous system. Studies have shown that the inflammation is vital to both the onset and progression of MS, where aging plays a key role in it. However, the potential mechanisms on how aging-related inflammation (inflammaging) promotes MS have not been fully understood. Therefore, there is an urgent need to integrate the underlying mechanisms between inflammaging and MS, where meaningful prediction models are needed. Methods First, both aging and disease models were developed using machine learning methods, respectively. Then, an integrated inflammaging model was used to identify relative risk factors, by identifying essential "aging-inflammation-disease" triples. Finally, a series of bioinformatics analyses (including network analysis, enrichment analysis, sensitivity analysis, and pan-cancer analysis) were further used to explore the potential mechanisms between inflammaging and MS. Results A series of risk factors were identified, such as the protein homeostasis, cellular homeostasis, neurodevelopment and energy metabolism. The inflammaging indices were further validated in different cancer types. Therefore, various risk factors were integrated, and even both the theories of inflammaging and immunosenescence were further confirmed. Conclusion In conclusion, our study systematically investigated the potential relationships between inflammaging and MS through a series of computational approaches, and could present a novel thought for other aging-related diseases.
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Affiliation(s)
- Mengchu Xu
- Department of Biomedical Engineering, School of Intelligent Sciences, China Medical University, Shenyang, Liaoning, China
| | - Huize Wang
- Department of Nursing, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Siwei Ren
- Department of Biomedical Engineering, School of Intelligent Sciences, China Medical University, Shenyang, Liaoning, China
| | - Bing Wang
- Department of Biomedical Engineering, School of Intelligent Sciences, China Medical University, Shenyang, Liaoning, China
| | - Wenyan Yang
- Department of Biomedical Engineering, School of Intelligent Sciences, China Medical University, Shenyang, Liaoning, China
| | - Ling Lv
- Department of Thorax, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xianzheng Sha
- Department of Biomedical Engineering, School of Intelligent Sciences, China Medical University, Shenyang, Liaoning, China
| | - Wenya Li
- Department of Thorax, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yin Wang
- Department of Biomedical Engineering, School of Intelligent Sciences, China Medical University, Shenyang, Liaoning, China
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
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Farabi Maleki S, Yousefi M, Afshar S, Pedrammehr S, Lim CP, Jafarizadeh A, Asadi H. Artificial Intelligence for Multiple Sclerosis Management Using Retinal Images: Pearl, Peaks, and Pitfalls. Semin Ophthalmol 2024; 39:271-288. [PMID: 38088176 DOI: 10.1080/08820538.2023.2293030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 11/23/2023] [Indexed: 03/28/2024]
Abstract
Multiple sclerosis (MS) is a complex autoimmune disease characterized by inflammatory processes, demyelination, neurodegeneration, and axonal damage within the central nervous system (CNS). Retinal imaging, particularly Optical coherence tomography (OCT), has emerged as a crucial tool for investigating MS-related retinal injury. The integration of artificial intelligence(AI) has shown promise in enhancing OCT analysis for MS. Researchers are actively utilizing AI algorithms to accurately detect and classify MS-related abnormalities, leading to improved efficiency in diagnosis, monitoring, and personalized treatment planning. The prognostic value of AI in predicting MS disease progression has garnered substantial attention. Machine learning (ML) and deep learning (DL) algorithms can analyze longitudinal OCT data to forecast the course of the disease, providing critical information for personalized treatment planning and improved patient outcomes. Early detection of high-risk patients allows for targeted interventions to mitigate disability progression effectively. As such, AI-driven approaches yielded remarkable abilities in classifying distinct MS subtypes based on retinal features, aiding in disease characterization and guiding tailored therapeutic strategies. Additionally, these algorithms have enhanced the accuracy and efficiency of OCT image segmentation, streamlined diagnostic processes, and reduced human error. This study reviews the current research studies on the integration of AI,including ML and DL algorithms, with OCT in the context of MS. It examines the advancements, challenges, potential prospects, and ethical concerns of AI-powered techniques in enhancing MS diagnosis, monitoring disease progression, revolutionizing patient care, the development of patient screening tools, and supported clinical decision-making based on OCT images.
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Affiliation(s)
| | - Milad Yousefi
- Faculty of Mathematics, Statistics and Computer Sciences, University of Tabriz, Tabriz, Iran
| | - Sayeh Afshar
- Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | - Chee Peng Lim
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Burwood, Australia
| | - Ali Jafarizadeh
- Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Houshyar Asadi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Burwood, Australia
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Yan Z, Yuan S, Zhu Q, Wang X, Shi Z, Zhang Y, Liu J, Feng J, Wei Y, Yin F, Chen S, Li Y. Radiomics models based on cortical damages for identification of multiple sclerosis with cognitive impairment. Mult Scler Relat Disord 2024; 81:105348. [PMID: 38061318 DOI: 10.1016/j.msard.2023.105348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 10/12/2023] [Accepted: 11/25/2023] [Indexed: 01/23/2024]
Abstract
BACKGROUND Cognitive impairment (CI) is a common symptom in multiple sclerosis (MS) patients. Cortical damages can be closely associated with cognitive network dysfunction and clinically significant CI in MS. So, in this study, We aimed to develop a radiomics model to efficiently identify the MS patients with CI based on clinical data and cortical damages. METHODS One hundred and eighteen patients with MS were divided into CI and normal cognitive (NC) cohorts (62/56) as defined by the Montreal Cognitive Assessment (MoCA). All participants were randomly divided into train and test sets with a ratio of 7:3. The radiomic features were selected by using the least absolute shrinkage and selection operator (LASSO) method. The discrimination models were built with the support vector machines (SVM) by the clinical data, radiomic features, and merge data, respectively. And the patients were further divided according to each cognitive domain including memory, visuospatial, language, attention and executive, and each domain model was applied by the most suitable classifier. RESULTS A total of 2298 features were extracted, of which 36 were finally selected. The merge model showed the greatest performance with the area under the curve (AUC) of 0.86 (95 % confidence interval: 0.81-0.91), accuracy (ACC) of 0.78, sensitivity of 0.79 and specificity of 0.77 in test cohort. However, although the visuospatial domain model showed the highest AUC of 0.71 (95 % confidence interval: 0.61-0.81) among five domain models, other domain models did not meet satisfactory results with a relatively low AUC, ACC, sensitivity and specificity. CONCLUSIONS The radiomics model based on clinical data and cortical damages had a great potential to identify the MS patients with CI for clinical cognitive assessment.
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Affiliation(s)
- Zichun Yan
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Shiqi Yuan
- Department of Computer Science, Southwest University, Chongqing, China
| | - Qiyuan Zhu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaohua Wang
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
| | - Zhuowei Shi
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yu Zhang
- Department of Computer Science, Southwest University, Chongqing, China
| | - Jie Liu
- Department of Computer Science, Southwest University, Chongqing, China
| | - Jinzhou Feng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yiqiu Wei
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Feiyue Yin
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Shanxiong Chen
- Department of Computer Science, Southwest University, Chongqing, China.
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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10
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Kulyabin M, Zhdanov A, Dolganov A, Ronkin M, Borisov V, Maier A. Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer. SENSORS (BASEL, SWITZERLAND) 2023; 23:8727. [PMID: 37960427 PMCID: PMC10648817 DOI: 10.3390/s23218727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 10/16/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023]
Abstract
The electroretinogram (ERG) is a clinical test that records the retina's electrical response to light. Analysis of the ERG signal offers a promising way to study different retinal diseases and disorders. Machine learning-based methods are expected to play a pivotal role in achieving the goals of retinal diagnostics and treatment control. This study aims to improve the classification accuracy of the previous work using the combination of three optimal mother wavelet functions. We apply Continuous Wavelet Transform (CWT) on a dataset of mixed pediatric and adult ERG signals and show the possibility of simultaneous analysis of the signals. The modern Visual Transformer-based architectures are tested on a time-frequency representation of the signals. The method provides 88% classification accuracy for Maximum 2.0 ERG, 85% for Scotopic 2.0, and 91% for Photopic 2.0 protocols, which on average improves the result by 7.6% compared to previous work.
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Affiliation(s)
- Mikhail Kulyabin
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany;
| | - Aleksei Zhdanov
- Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University Named after the First President of Russia B. N. Yeltsin, 620002 Yekaterinburg, Russia; (A.Z.); (A.D.); (M.R.); (V.B.)
| | - Anton Dolganov
- Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University Named after the First President of Russia B. N. Yeltsin, 620002 Yekaterinburg, Russia; (A.Z.); (A.D.); (M.R.); (V.B.)
| | - Mikhail Ronkin
- Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University Named after the First President of Russia B. N. Yeltsin, 620002 Yekaterinburg, Russia; (A.Z.); (A.D.); (M.R.); (V.B.)
| | - Vasilii Borisov
- Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University Named after the First President of Russia B. N. Yeltsin, 620002 Yekaterinburg, Russia; (A.Z.); (A.D.); (M.R.); (V.B.)
| | - Andreas Maier
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany;
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11
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Naji Y, Mahdaoui M, Klevor R, Kissani N. Artificial Intelligence and Multiple Sclerosis: Up-to-Date Review. Cureus 2023; 15:e45412. [PMID: 37854769 PMCID: PMC10581506 DOI: 10.7759/cureus.45412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/17/2023] [Indexed: 10/20/2023] Open
Abstract
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.
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Affiliation(s)
- Yahya Naji
- Neurology Department, REGNE Research Laboratory, Faculty of Medicine and Pharmacy, Ibn Zohr University, Agadir, MAR
- Neurology Department, Agadir University Hospital, Agadir, MAR
| | - Mohamed Mahdaoui
- Neurology Department, University Hospital Mohammed VI, Marrakech, MAR
- Neuroscience Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech, MAR
| | - Raymond Klevor
- Neurology Department, University Hospital Mohammed VI, Marrakech, MAR
- Neuroscience Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech, MAR
| | - Najib Kissani
- Neurology Department, University Hospital Mohammed VI, Marrakech, MAR
- Neuroscience Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech, MAR
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Hernandez M, Ramon-Julvez U, Vilades E, Cordon B, Mayordomo E, Garcia-Martin E. Explainable artificial intelligence toward usable and trustworthy computer-aided diagnosis of multiple sclerosis from Optical Coherence Tomography. PLoS One 2023; 18:e0289495. [PMID: 37549174 PMCID: PMC10406231 DOI: 10.1371/journal.pone.0289495] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 07/19/2023] [Indexed: 08/09/2023] Open
Abstract
BACKGROUND Several studies indicate that the anterior visual pathway provides information about the dynamics of axonal degeneration in Multiple Sclerosis (MS). Current research in the field is focused on the quest for the most discriminative features among patients and controls and the development of machine learning models that yield computer-aided solutions widely usable in clinical practice. However, most studies are conducted with small samples and the models are used as black boxes. Clinicians should not trust machine learning decisions unless they come with comprehensive and easily understandable explanations. MATERIALS AND METHODS A total of 216 eyes from 111 healthy controls and 100 eyes from 59 patients with relapsing-remitting MS were enrolled. The feature set was obtained from the thickness of the ganglion cell layer (GCL) and the retinal nerve fiber layer (RNFL). Measurements were acquired by the novel Posterior Pole protocol from Spectralis Optical Coherence Tomography (OCT) device. We compared two black-box methods (gradient boosting and random forests) with a glass-box method (explainable boosting machine). Explainability was studied using SHAP for the black-box methods and the scores of the glass-box method. RESULTS The best-performing models were obtained for the GCL layer. Explainability pointed out to the temporal location of the GCL layer that is usually broken or thinning in MS and the relationship between low thickness values and high probability of MS, which is coherent with clinical knowledge. CONCLUSIONS The insights on how to use explainability shown in this work represent a first important step toward a trustworthy computer-aided solution for the diagnosis of MS with OCT.
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Affiliation(s)
- Monica Hernandez
- Computer Science Department, University of Zaragoza, Zaragoza, Spain
- Aragon Institute on Engineering Research, Zaragoza, Spain
| | - Ubaldo Ramon-Julvez
- Computer Science Department, University of Zaragoza, Zaragoza, Spain
- Aragon Institute on Engineering Research, Zaragoza, Spain
| | - Elisa Vilades
- Ophtalmology Department, Miguel Servet Hospital, Zaragoza, Spain
| | - Beatriz Cordon
- Ophtalmology Department, Miguel Servet Hospital, Zaragoza, Spain
| | - Elvira Mayordomo
- Computer Science Department, University of Zaragoza, Zaragoza, Spain
- Aragon Institute on Engineering Research, Zaragoza, Spain
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Bakirtzis C, Lima M, De Lorenzo SS, Artemiadis A, Theotokis P, Kesidou E, Konstantinidou N, Sintila SA, Boziki MK, Parissis D, Ioannidis P, Karapanayiotides T, Hadjigeorgiou G, Grigoriadis N. Secondary Central Nervous System Demyelinating Disorders in the Elderly: A Narrative Review. Healthcare (Basel) 2023; 11:2126. [PMID: 37570367 PMCID: PMC10418902 DOI: 10.3390/healthcare11152126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/14/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
Secondary demyelinating diseases comprise a wide spectrum group of pathological conditions and may either be attributed to a disorder primarily affecting the neurons or axons, followed by demyelination, or to an underlying condition leading to secondary damage of the myelin sheath. In the elderly, primary demyelinating diseases of the central nervous system (CNS), such as multiple sclerosis, are relatively uncommon. However, secondary causes of CNS demyelination may often occur and in this case, extensive diagnostic workup is usually needed. Infectious, postinfectious, or postvaccinal demyelination may be observed, attributed to age-related alterations of the immune system in this population. Osmotic disturbances and nutritional deficiencies, more commonly observed in the elderly, may lead to conditions such as pontine/extrapontine myelinolysis, Wernicke encephalopathy, and demyelination of the posterior columns of the spinal cord. The prevalence of malignancies is higher in the elderly, sometimes leading to radiation-induced, immunotherapy-related, or paraneoplastic CNS demyelination. This review intends to aid clinical neurologists in broadening their diagnostic approach to secondary CNS demyelinating diseases in the elderly. Common clinical conditions leading to secondary demyelination and their clinical manifestations are summarized here, while the current knowledge of the underlying pathophysiological mechanisms is additionally presented.
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Affiliation(s)
- Christos Bakirtzis
- Second Department of Neurology, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece; (M.L.); (S.S.D.L.); (P.T.); (E.K.); (N.K.); (S.-A.S.); (M.-K.B.); (D.P.); (P.I.); (T.K.); (N.G.)
| | - Maria Lima
- Second Department of Neurology, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece; (M.L.); (S.S.D.L.); (P.T.); (E.K.); (N.K.); (S.-A.S.); (M.-K.B.); (D.P.); (P.I.); (T.K.); (N.G.)
| | - Sotiria Stavropoulou De Lorenzo
- Second Department of Neurology, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece; (M.L.); (S.S.D.L.); (P.T.); (E.K.); (N.K.); (S.-A.S.); (M.-K.B.); (D.P.); (P.I.); (T.K.); (N.G.)
| | - Artemios Artemiadis
- Faculty of Medicine, University of Cyprus, Nicosia CY-2029, Cyprus; (A.A.); (G.H.)
| | - Paschalis Theotokis
- Second Department of Neurology, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece; (M.L.); (S.S.D.L.); (P.T.); (E.K.); (N.K.); (S.-A.S.); (M.-K.B.); (D.P.); (P.I.); (T.K.); (N.G.)
| | - Evangelia Kesidou
- Second Department of Neurology, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece; (M.L.); (S.S.D.L.); (P.T.); (E.K.); (N.K.); (S.-A.S.); (M.-K.B.); (D.P.); (P.I.); (T.K.); (N.G.)
| | - Natalia Konstantinidou
- Second Department of Neurology, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece; (M.L.); (S.S.D.L.); (P.T.); (E.K.); (N.K.); (S.-A.S.); (M.-K.B.); (D.P.); (P.I.); (T.K.); (N.G.)
| | - Styliani-Aggeliki Sintila
- Second Department of Neurology, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece; (M.L.); (S.S.D.L.); (P.T.); (E.K.); (N.K.); (S.-A.S.); (M.-K.B.); (D.P.); (P.I.); (T.K.); (N.G.)
| | - Marina-Kleopatra Boziki
- Second Department of Neurology, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece; (M.L.); (S.S.D.L.); (P.T.); (E.K.); (N.K.); (S.-A.S.); (M.-K.B.); (D.P.); (P.I.); (T.K.); (N.G.)
| | - Dimitrios Parissis
- Second Department of Neurology, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece; (M.L.); (S.S.D.L.); (P.T.); (E.K.); (N.K.); (S.-A.S.); (M.-K.B.); (D.P.); (P.I.); (T.K.); (N.G.)
| | - Panagiotis Ioannidis
- Second Department of Neurology, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece; (M.L.); (S.S.D.L.); (P.T.); (E.K.); (N.K.); (S.-A.S.); (M.-K.B.); (D.P.); (P.I.); (T.K.); (N.G.)
| | - Theodoros Karapanayiotides
- Second Department of Neurology, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece; (M.L.); (S.S.D.L.); (P.T.); (E.K.); (N.K.); (S.-A.S.); (M.-K.B.); (D.P.); (P.I.); (T.K.); (N.G.)
| | | | - Nikolaos Grigoriadis
- Second Department of Neurology, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece; (M.L.); (S.S.D.L.); (P.T.); (E.K.); (N.K.); (S.-A.S.); (M.-K.B.); (D.P.); (P.I.); (T.K.); (N.G.)
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Akila SM, Imanov E, Almezhghwi K. Investigating Beta-Variational Convolutional Autoencoders for the Unsupervised Classification of Chest Pneumonia. Diagnostics (Basel) 2023; 13:2199. [PMID: 37443592 DOI: 10.3390/diagnostics13132199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 07/15/2023] Open
Abstract
The world's population is increasing and so is the challenge on existing healthcare infrastructure to cope with the growing demand in medical diagnosis and evaluation. Although human experts are primarily tasked with the diagnosis of different medical conditions, artificial intelligence (AI)-assisted diagnoses have become considerably useful in recent times. One of the critical lung infections, which requires early diagnosis and subsequent treatment to reduce the mortality rate, is pneumonia. There are different methods for obtaining a pneumonia diagnosis; however, the adoption of chest X-rays is popular since it is non-invasive. The AI systems for a pneumonia diagnosis using chest X-rays are often built on supervised machine-learning (ML) models, which require labeled datasets for development. However, collecting labeled datasets is sometimes infeasible due to constraints such as human resources, cost, and time. As such, the problem that we address in this paper is the unsupervised classification of pneumonia using unsupervised ML models including the beta-variational convolutional autoencoder (β-VCAE) and other variants, such as convolutional autoencoders (CAE), denoising convolutional autoencoders (DCAE), and sparse convolutional autoencoders (SCAE). Namely, the pneumonia classification problem is cast into an anomaly detection to develop the aforementioned ML models. The experimental results show that pneumonia can be diagnosed with high recall, precision, f1-score, and f2-score using the proposed unsupervised models. In addition, we observe that the proposed models are competitive with the state-of-the-art models, which are trained on a labeled dataset.
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Affiliation(s)
- Serag Mohamed Akila
- Department of Biomedical Engineering, Near East University, Mersin 10, 99138 Nicosia, Turkey
| | - Elbrus Imanov
- Department of Computer Engineering, Near East University, Mersin 10, 99138 Nicosia, Turkey
| | - Khaled Almezhghwi
- Electrical and Electronics Engineering, College of Electronics Technology Tripoli, Tripoli 00000, Libya
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Kihara Y, Chun J. Molecular and neuroimmune pharmacology of S1P receptor modulators and other disease-modifying therapies for multiple sclerosis. Pharmacol Ther 2023; 246:108432. [PMID: 37149155 DOI: 10.1016/j.pharmthera.2023.108432] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/25/2023] [Accepted: 05/02/2023] [Indexed: 05/08/2023]
Abstract
Multiple sclerosis (MS) is a neurological, immune-mediated demyelinating disease that affects people in the prime of life. Environmental, infectious, and genetic factors have been implicated in its etiology, although a definitive cause has yet to be determined. Nevertheless, multiple disease-modifying therapies (DMTs: including interferons, glatiramer acetate, fumarates, cladribine, teriflunomide, fingolimod, siponimod, ozanimod, ponesimod, and monoclonal antibodies targeting ITGA4, CD20, and CD52) have been developed and approved for the treatment of MS. All the DMTs approved to date target immunomodulation as their mechanism of action (MOA); however, the direct effects of some DMTs on the central nervous system (CNS), particularly sphingosine 1-phosphate (S1P) receptor (S1PR) modulators, implicate a parallel MOA that may also reduce neurodegenerative sequelae. This review summarizes the currently approved DMTs for the treatment of MS and provides details and recent advances in the molecular pharmacology, immunopharmacology, and neuropharmacology of S1PR modulators, with a special focus on the CNS-oriented, astrocyte-centric MOA of fingolimod.
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Affiliation(s)
- Yasuyuki Kihara
- Sanford Burnham Prebys Medical Discovery Institute, United States of America.
| | - Jerold Chun
- Sanford Burnham Prebys Medical Discovery Institute, United States of America
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Olatunji SO, Alsheikh N, Alnajrani L, Alanazy A, Almusairii M, Alshammasi S, Alansari A, Zaghdoud R, Alahmadi A, Basheer Ahmed MI, Ahmed MS, Alhiyafi J. Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern Province of Saudi Arabia. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4261. [PMID: 36901273 PMCID: PMC10002108 DOI: 10.3390/ijerph20054261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/22/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
Multiple Sclerosis (MS) is characterized by chronic deterioration of the nervous system, mainly the brain and the spinal cord. An individual with MS develops the condition when the immune system begins attacking nerve fibers and the myelin sheathing that covers them, affecting the communication between the brain and the rest of the body and eventually causing permanent damage to the nerve. Patients with MS (pwMS) might experience different symptoms depending on which nerve was damaged and how much damage it has sustained. Currently, there is no cure for MS; however, there are clinical guidelines that help control the disease and its accompanying symptoms. Additionally, no specific laboratory biomarker can precisely identify the presence of MS, leaving specialists with a differential diagnosis that relies on ruling out other possible diseases with similar symptoms. Since the emergence of Machine Learning (ML) in the healthcare industry, it has become an effective tool for uncovering hidden patterns that aid in diagnosing several ailments. Several studies have been conducted to diagnose MS using ML and Deep Learning (DL) models trained using MRI images, achieving promising results. However, complex and expensive diagnostic tools are needed to collect and examine imaging data. Thus, the intention of this study is to implement a cost-effective, clinical data-driven model that is capable of diagnosing pwMS. The dataset was obtained from King Fahad Specialty Hospital (KFSH) in Dammam, Saudi Arabia. Several ML algorithms were compared, namely Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extra Trees (ET). The results indicated that the ET model outpaced the rest with an accuracy of 94.74%, recall of 97.26%, and precision of 94.67%.
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Affiliation(s)
- Sunday O. Olatunji
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Nawal Alsheikh
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Lujain Alnajrani
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Alhatoon Alanazy
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Meshael Almusairii
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Salam Alshammasi
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Aisha Alansari
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Rim Zaghdoud
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Alaa Alahmadi
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Mohammed Imran Basheer Ahmed
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Mohammed Salih Ahmed
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Jamal Alhiyafi
- Department of Computer Science, Kettering University, Flint, MI 48504, USA
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Cerebrospinal Fluid Biomarkers in Differential Diagnosis of Multiple Sclerosis and Systemic Inflammatory Diseases with Central Nervous System Involvement. Biomedicines 2023; 11:biomedicines11020425. [PMID: 36830963 PMCID: PMC9953577 DOI: 10.3390/biomedicines11020425] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/19/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Diagnosis of multiple sclerosis (MS) is established on criteria according to clinical and radiological manifestation. Cerebrospinal fluid (CSF) analysis is an important part of differential diagnosis of MS and other inflammatory processes in the central nervous system (CNS). METHODS In total, 242 CSF samples were collected from patients undergoing differential MS diagnosis because of the presence of T2-hyperintensive lesions on brain MRI. The non-MS patients were subdivided into systemic inflammatory diseases with CNS involvement (SID) or cerebrovascular diseases (CVD) or other non-inflammatory diseases (NID). All samples were analyzed for the presence of oligoclonal bands and ELISA was performed for detection of: INF gamma, IL-6, neurofilaments light chain (NF-L), GFAP, CHI3L1, CXCL13, and osteopontin. RESULTS The level of IL-6 (p = 0.024), osteopontin (p = 0.0002), and NF-L (p = 0.002) was significantly different among groups. IL-6 (p = 0.0350) and NF-L (p = 0.0015) level was significantly higher in SID compared to NID patients. A significantly higher level of osteopontin (p = 0.00026) and NF-L (p = 0.002) in MS compared to NID population was noted. ROC analysis found weak diagnostic power for osteopontin and NFL-L. CONCLUSIONS The classical and non-standard markers of inflammatory process and neurodegeneration do not allow for sufficient differentiation between MS and non-MS inflammatory CNS disorders. Weak diagnostic power observed for the osteopontin and NF-L needs to be further investigated.
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