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Pilehvari S, Morgan Y, Peng W. An analytical review on the use of artificial intelligence and machine learning in diagnosis, prediction, and risk factor analysis of multiple sclerosis. Mult Scler Relat Disord 2024; 89:105761. [PMID: 39018642 DOI: 10.1016/j.msard.2024.105761] [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: 09/14/2023] [Revised: 06/19/2024] [Accepted: 07/04/2024] [Indexed: 07/19/2024]
Abstract
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.
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Affiliation(s)
- Shima Pilehvari
- University of Regina, 3737 Wascana Parkway, Regina, SK, S4S 0A2, Canada
| | - Yasser Morgan
- University of Regina, 3737 Wascana Parkway, Regina, SK, S4S 0A2, Canada
| | - Wei Peng
- University of Regina, 3737 Wascana Parkway, Regina, SK, S4S 0A2, Canada.
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Tuncer SA, Danacı C, Bilek F, Demir CF, Tuncer T. Utilizing Aerobic Capacity Data for EDSS Score Estimation in Multiple Sclerosis: A Machine Learning Approach. Diagnostics (Basel) 2024; 14:1249. [PMID: 38928664 PMCID: PMC11203342 DOI: 10.3390/diagnostics14121249] [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: 04/20/2024] [Revised: 06/10/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
The Expanded Disability Status Scale (EDSS) is the most popular method to assess disease progression and treatment effectiveness in patients with multiple sclerosis (PwMS). One of the main problems with the EDSS method is that different results can be determined by different physicians for the same patient. In this case, it is necessary to produce autonomous solutions that will increase the reliability of the EDSS, which has a decision-making role. This study proposes a machine learning approach to predict EDSS scores using aerobic capacity data from PwMS. The primary goal is to reduce potential complications resulting from incorrect scoring procedures. Cardiovascular and aerobic capacity parameters of individuals, including aerobic capacity, ventilation, respiratory frequency, heart rate, average oxygen density, load, and energy expenditure, were evaluated. These parameters were given as input to CatBoost, gradient boosting (GBM), extreme gradient boosting (XGBoost), and decision tree (DT) machine learning methods. The most significant EDSS results were determined with the XGBoost algorithm. Mean absolute error, root mean square error, mean square error, mean absolute percent error, and R square values were obtained as 0.26, 0.4, 0.26, 16, and 0.68, respectively. The XGBoost based machine learning technique was shown to be effective in predicting EDSS based on aerobic capacity and cardiovascular data in PwMS.
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Affiliation(s)
- Seda Arslan Tuncer
- Software Engineering, Faculty of Engineering, Firat University, 23119 Elazığ, Turkey; (S.A.T.); (C.D.)
| | - Cagla Danacı
- Software Engineering, Faculty of Engineering, Firat University, 23119 Elazığ, Turkey; (S.A.T.); (C.D.)
- Department of Software Engineering, Faculty of Technology, Sivas Republic University, 58070 Sivas, Turkey
| | - Furkan Bilek
- Department of Gerontology, Fethiye Faculty of Health Sciences, Muğla Sıtkı Koçman University, 48000 Muğla, Turkey;
| | - Caner Feyzi Demir
- Department of Neurology, School of Medicine, Fırat University, 23119 Elazig, Turkey;
| | - Taner Tuncer
- Computer Engineering, Faculty of Engineering, Firat University, 23119 Elazığ, Turkey
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Khattap MG, Abd Elaziz M, Hassan HGEMA, Elgarayhi A, Sallah M. AI-based model for automatic identification of multiple sclerosis based on enhanced sea-horse optimizer and MRI scans. Sci Rep 2024; 14:12104. [PMID: 38802440 DOI: 10.1038/s41598-024-61876-9] [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: 09/07/2023] [Accepted: 05/10/2024] [Indexed: 05/29/2024] Open
Abstract
This study aims to develop an AI-enhanced methodology for the expedited and accurate diagnosis of Multiple Sclerosis (MS), a chronic disease affecting the central nervous system leading to progressive impairment. Traditional diagnostic methods are slow and require substantial expertise, underscoring the need for innovative solutions. Our approach involves two phases: initially, extracting features from brain MRI images using first-order histograms, the gray level co-occurrence matrix, and local binary patterns. A unique feature selection technique combining the Sine Cosine Algorithm with the Sea-horse Optimizer is then employed to identify the most significant features. Utilizing the eHealth lab dataset, which includes images from 38 MS patients (mean age 34.1 ± 10.5 years; 17 males, 21 females) and matched healthy controls, our model achieved a remarkable 97.97% detection accuracy using the k-nearest neighbors classifier. Further validation on a larger dataset containing 262 MS cases (199 females, 63 males; mean age 31.26 ± 10.34 years) and 163 healthy individuals (109 females, 54 males; mean age 32.35 ± 10.30 years) demonstrated a 92.94% accuracy for FLAIR images and 91.25% for T2-weighted images with the Random Forest classifier, outperforming existing MS detection methods. These results highlight the potential of the proposed technique as a clinical decision-making tool for the early identification and management of MS.
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Affiliation(s)
- Mohamed G Khattap
- Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt.
- Technology of Radiology and Medical Imaging Program, Faculty of Applied Health Sciences Technology, Galala University, Suez, 435611, Egypt.
| | - Mohamed Abd Elaziz
- Faculty of Computer Science and Engineering, Galala University, Suez, 435611, Egypt
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon
| | - Hend Galal Eldeen Mohamed Ali Hassan
- Technology of Radiology and Medical Imaging Program, Faculty of Applied Health Sciences Technology, Galala University, Suez, 435611, Egypt
- Diagnostic and Interventional Radiology Department, Faculty of Medicine, Ain Shams University, Cairo, 11591, Egypt
| | - Ahmed Elgarayhi
- Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
| | - Mohammed Sallah
- Department of Physics, College of Sciences, University of Bisha, P.O. Box 344, Bisha, 61922, Saudi Arabia
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Ding S, Zheng H, Wang L, Fan X, Yang X, Huang Z, Zhang X, Yan Z, Li X, Cai J. Classification of Myelin Oligodendrocyte Glycoprotein Antibody-Related Disease and Its Mimicking Acute Demyelinating Syndromes in Children Using MRI-Based Radiomics: From Lesion to Subject. Acad Radiol 2024; 31:2085-2096. [PMID: 38007367 DOI: 10.1016/j.acra.2023.11.011] [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: 10/02/2023] [Revised: 11/02/2023] [Accepted: 11/06/2023] [Indexed: 11/27/2023]
Abstract
RATIONALE AND OBJECTIVES To develop MRI-based radiomics models from the lesion level to the subject level and assess their value for differentiating myelin oligodendrocyte glycoprotein antibody-related disease (MOGAD) from non-MOGAD acute demyelinating syndromes in pediatrics. MATERIALS AND METHODS 66 MOGAD and 66 non-MOGAD children were assigned to the training set (36/35), internal test set (14/16), and external test set (16/15), respectively. At the lesion level, five single-sequence models were developed alongside a fusion model (combining these five sequences). The radiomics features of each lesion were quantified as the lesion-level radscore (LRS) using the best-performing model. Subsequently, a lesion-typing function was employed to classify lesions into two types (MOGAD-like or non-MOGAD-like), and the average LRS of the predominant type lesions in each subject was considered as the subject-level radscore (SRS). Based on SRS, a subject-level model was established and compared to both clinical models and radiologists' assessments. RESULTS At the lesion level, the fusion model outperformed the five single-sequence models in distinguishing MOGAD and non-MOGAD lesions (0.867 and 0.810 of area under the curve [AUC] in internal and external testing, respectively). At the subject level, the SRS model showed superior performance (0.844 and 0.846 of AUC in internal and external testing, respectively) compared to clinical models and radiologists' assessments for distinguishing MOGAD and non-MOGAD. CONCLUSION MRI-based radiomics models have potential clinical value for identifying MOGAD from non-MOGAD. The fusion model and SRS model can distinguish between MOGAD and non-MOGAD at the lesion level and subject level, respectively, providing a differential diagnosis method for these two diseases.
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Affiliation(s)
- Shuang Ding
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014, China (S.D., H.Z., L.W., X.F., X.Y., Z.H., X.Z., J.C.)
| | - Helin Zheng
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014, China (S.D., H.Z., L.W., X.F., X.Y., Z.H., X.Z., J.C.)
| | - Longlun Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014, China (S.D., H.Z., L.W., X.F., X.Y., Z.H., X.Z., J.C.)
| | - Xiao Fan
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014, China (S.D., H.Z., L.W., X.F., X.Y., Z.H., X.Z., J.C.)
| | - Xinyi Yang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014, China (S.D., H.Z., L.W., X.F., X.Y., Z.H., X.Z., J.C.)
| | - Zhongxin Huang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014, China (S.D., H.Z., L.W., X.F., X.Y., Z.H., X.Z., J.C.)
| | - Xiangmin Zhang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014, China (S.D., H.Z., L.W., X.F., X.Y., Z.H., X.Z., J.C.)
| | - Zichun Yan
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400000, China (Z.Y.)
| | - Xiujuan Li
- Department of Neurology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014, China (X.L.)
| | - Jinhua Cai
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014, China (S.D., H.Z., L.W., X.F., X.Y., Z.H., X.Z., J.C.).
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Montolío A, Cegoñino J, Garcia-Martin E, Pérez Del Palomar A. The macular retinal ganglion cell layer as a biomarker for diagnosis and prognosis in multiple sclerosis: A deep learning approach. Acta Ophthalmol 2024; 102:e272-e284. [PMID: 37300357 DOI: 10.1111/aos.15722] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 05/12/2023] [Accepted: 05/28/2023] [Indexed: 06/12/2023]
Abstract
PURPOSE The macular ganglion cell layer (mGCL) is a strong potential biomarker of axonal degeneration in multiple sclerosis (MS). For this reason, this study aims to develop a computer-aided method to facilitate diagnosis and prognosis in MS. METHODS This paper combines a cross-sectional study of 72 MS patients and 30 healthy control subjects for diagnosis and a 10-year longitudinal study of the same MS patients for the prediction of disability progression, during which the mGCL was measured using optical coherence tomography (OCT). Deep neural networks were used as an automatic classifier. RESULTS For MS diagnosis, greatest accuracy (90.3%) was achieved using 17 features as inputs. The neural network architecture comprised the input layer, two hidden layers and the output layer with softmax activation. For the prediction of disability progression 8 years later, accuracy of 81.9% was achieved with a neural network comprising two hidden layers and 400 epochs. CONCLUSION We present evidence that by applying deep learning techniques to clinical and mGCL thickness data it is possible to identify MS and predict the course of the disease. This approach potentially constitutes a non-invasive, low-cost, easy-to-implement and effective method.
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Affiliation(s)
- Alberto Montolío
- Biomaterials Group, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain
- Mechanical Engineering Department, University of Zaragoza, Zaragoza, Spain
| | - José Cegoñino
- Biomaterials Group, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain
- Mechanical Engineering Department, University of Zaragoza, Zaragoza, Spain
| | - Elena Garcia-Martin
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain
- GIMSO Research and Innovation Group, Aragon Institute for Health Research (IIS Aragon), Zaragoza, Spain
| | - Amaya Pérez Del Palomar
- Biomaterials Group, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain
- Mechanical Engineering Department, University of Zaragoza, Zaragoza, Spain
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Rasouli S, Dakkali MS, Azarbad R, Ghazvini A, Asani M, Mirzaasgari Z, Arish M. Predicting the conversion from clinically isolated syndrome to multiple sclerosis: An explainable machine learning approach. Mult Scler Relat Disord 2024; 86:105614. [PMID: 38642495 DOI: 10.1016/j.msard.2024.105614] [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: 12/03/2023] [Revised: 04/04/2024] [Accepted: 04/07/2024] [Indexed: 04/22/2024]
Abstract
INTRODUCTION Predicting the conversion of clinically isolated syndrome (CIS) to clinically definite multiple sclerosis (CDMS) is critical to personalizing treatment planning and benefits for patients. The aim of this study is to develop an explainable machine learning (ML) model for predicting this conversion based on demographic, clinical, and imaging data. METHOD The ML model, Extreme Gradient Boosting (XGBoost), was employed on the public dataset of 273 Mexican mestizo CIS patients with 10-year follow-up. The data was divided into a training set for cross-validation and feature selection, and a holdout test set for final testing. Feature importance was determined using the SHapley Additive Explanations library (SHAP). Then, two experiments were conducted to optimize the model's performance by selectively adding variables and selecting the most contributive variables for the final model. RESULTS Nine variables including age, gender, schooling, motor symptoms, infratentorial and periventricular lesion at imaging, oligoclonal band in cerebrospinal fluid, lesion and symptoms types were significant. The model achieved an accuracy of 83.6 %, AUC of 91.8 %, sensitivity of 83.9 %, and specificity of 83.4 % in cross-validation. In the final testing, the model achieved an accuracy of 78.3 %, AUC of 85.8 %, sensitivity of 75 %, and specificity of 81.1 %. Finally, a web-based demo of the model was created for testing purposes. CONCLUSION The model, focusing on feature selection and interpretability, effectively stratifies risk for treatment decisions and disability prevention in MS patients. It provides a numerical risk estimate for CDMS conversion, enhancing transparency in clinical decision-making and aiding in patient care.
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Affiliation(s)
- Saeid Rasouli
- School of Medicine, Five Senses Health Research Institute, Hazrat-e Rasool General Hospital, Iran University of Medical Sciences, Tehran, Iran.
| | - Mohammad Sedigh Dakkali
- Department of Ophthalmology, School of Medicine, Al Zahra Eye Hospital, Zahedan University of Medical Sciences, Zahedan, Iran
| | - Reza Azarbad
- Cellular and Molecular Biology Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran
| | - Azim Ghazvini
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Mahdi Asani
- Department of Ophthalmology, School of Medicine, Al Zahra Eye Hospital, Zahedan University of Medical Sciences, Zahedan, Iran
| | - Zahra Mirzaasgari
- Department of Neurology, Firoozgar hospital, School of medicine, University of Medical Science, Iran
| | - Mohammed Arish
- Department of Ophthalmology, School of Medicine, Al Zahra Eye Hospital, Zahedan University of Medical Sciences, Zahedan, Iran
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Vázquez-Marrufo M, Sarrias-Arrabal E, García-Torres M, Martín-Clemente R, Izquierdo G. A systematic review of the application of machine-learning algorithms in multiple sclerosis. Neurologia 2023; 38:577-590. [PMID: 35843587 DOI: 10.1016/j.nrleng.2020.10.013] [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: 06/05/2020] [Accepted: 10/11/2020] [Indexed: 10/17/2022] Open
Abstract
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.
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Affiliation(s)
- M Vázquez-Marrufo
- Departamento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Sevilla, Spain.
| | - E Sarrias-Arrabal
- Departamento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Sevilla, Spain
| | - M García-Torres
- Escuela Politécnica Superior, Universidad Pablo de Olavide, Sevilla, Spain
| | - R Martín-Clemente
- Departamento de Teoría de la Señal y Comunicaciones, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Sevilla, Spain
| | - G Izquierdo
- Unidad de Esclerosis Múltiple, Hospital VITHAS, Sevilla, Spain
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Reeve K, On BI, Havla J, Burns J, Gosteli-Peter MA, Alabsawi A, Alayash Z, Götschi A, Seibold H, Mansmann U, Held U. Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis. Cochrane Database Syst Rev 2023; 9:CD013606. [PMID: 37681561 PMCID: PMC10486189 DOI: 10.1002/14651858.cd013606.pub2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
BACKGROUND Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system that affects millions of people worldwide. The disease course varies greatly across individuals and many disease-modifying treatments with different safety and efficacy profiles have been developed recently. Prognostic models evaluated and shown to be valid in different settings have the potential to support people with MS and their physicians during the decision-making process for treatment or disease/life management, allow stratified and more precise interpretation of interventional trials, and provide insights into disease mechanisms. Many researchers have turned to prognostic models to help predict clinical outcomes in people with MS; however, to our knowledge, no widely accepted prognostic model for MS is being used in clinical practice yet. OBJECTIVES To identify and summarise multivariable prognostic models, and their validation studies for quantifying the risk of clinical disease progression, worsening, and activity in adults with MS. SEARCH METHODS We searched MEDLINE, Embase, and the Cochrane Database of Systematic Reviews from January 1996 until July 2021. We also screened the reference lists of included studies and relevant reviews, and references citing the included studies. SELECTION CRITERIA We included all statistically developed multivariable prognostic models aiming to predict clinical disease progression, worsening, and activity, as measured by disability, relapse, conversion to definite MS, conversion to progressive MS, or a composite of these in adult individuals with MS. We also included any studies evaluating the performance of (i.e. validating) these models. There were no restrictions based on language, data source, timing of prognostication, or timing of outcome. DATA COLLECTION AND ANALYSIS Pairs of review authors independently screened titles/abstracts and full texts, extracted data using a piloted form based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), assessed risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST), and assessed reporting deficiencies based on the checklist items in Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). The characteristics of the included models and their validations are described narratively. We planned to meta-analyse the discrimination and calibration of models with at least three external validations outside the model development study but no model met this criterion. We summarised between-study heterogeneity narratively but again could not perform the planned meta-regression. MAIN RESULTS We included 57 studies, from which we identified 75 model developments, 15 external validations corresponding to only 12 (16%) of the models, and six author-reported validations. Only two models were externally validated multiple times. None of the identified external validations were performed by researchers independent of those that developed the model. The outcome was related to disease progression in 39 (41%), relapses in 8 (8%), conversion to definite MS in 17 (18%), and conversion to progressive MS in 27 (28%) of the 96 models or validations. The disease and treatment-related characteristics of included participants, and definitions of considered predictors and outcome, were highly heterogeneous amongst the studies. Based on the publication year, we observed an increase in the percent of participants on treatment, diversification of the diagnostic criteria used, an increase in consideration of biomarkers or treatment as predictors, and increased use of machine learning methods over time. Usability and reproducibility All identified models contained at least one predictor requiring the skills of a medical specialist for measurement or assessment. Most of the models (44; 59%) contained predictors that require specialist equipment likely to be absent from primary care or standard hospital settings. Over half (52%) of the developed models were not accompanied by model coefficients, tools, or instructions, which hinders their application, independent validation or reproduction. The data used in model developments were made publicly available or reported to be available on request only in a few studies (two and six, respectively). Risk of bias We rated all but one of the model developments or validations as having high overall risk of bias. The main reason for this was the statistical methods used for the development or evaluation of prognostic models; we rated all but two of the included model developments or validations as having high risk of bias in the analysis domain. None of the model developments that were externally validated or these models' external validations had low risk of bias. There were concerns related to applicability of the models to our research question in over one-third (38%) of the models or their validations. Reporting deficiencies Reporting was poor overall and there was no observable increase in the quality of reporting over time. The items that were unclearly reported or not reported at all for most of the included models or validations were related to sample size justification, blinding of outcome assessors, details of the full model or how to obtain predictions from it, amount of missing data, and treatments received by the participants. Reporting of preferred model performance measures of discrimination and calibration was suboptimal. AUTHORS' CONCLUSIONS The current evidence is not sufficient for recommending the use of any of the published prognostic prediction models for people with MS in clinical routine today due to lack of independent external validations. The MS prognostic research community should adhere to the current reporting and methodological guidelines and conduct many more state-of-the-art external validation studies for the existing or newly developed models.
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Affiliation(s)
- Kelly Reeve
- Epidemiology, Biostatistics and Prevention Institute, University of Zürich, Zurich, Switzerland
| | - Begum Irmak On
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Joachim Havla
- lnstitute of Clinical Neuroimmunology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Jacob Burns
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | | | - Albraa Alabsawi
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Zoheir Alayash
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
- Institute of Health Services Research in Dentistry, University of Münster, Muenster, Germany
| | - Andrea Götschi
- Epidemiology, Biostatistics and Prevention Institute, University of Zürich, Zurich, Switzerland
| | | | - Ulrich Mansmann
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Ulrike Held
- Epidemiology, Biostatistics and Prevention Institute, University of Zürich, Zurich, Switzerland
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Tayyab M, Metz LM, Li DKB, Kolind S, Carruthers R, Traboulsee A, Tam RC. Accounting for uncertainty in training data to improve machine learning performance in predicting new disease activity in early multiple sclerosis. Front Neurol 2023; 14:1165267. [PMID: 37305756 PMCID: PMC10251494 DOI: 10.3389/fneur.2023.1165267] [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] [Received: 02/13/2023] [Accepted: 05/09/2023] [Indexed: 06/13/2023] Open
Abstract
Introduction Machine learning (ML) has great potential for using health data to predict clinical outcomes in individual patients. Missing data are a common challenge in training ML algorithms, such as when subjects withdraw from a clinical study, leaving some samples with missing outcome labels. In this study, we have compared three ML models to determine whether accounting for label uncertainty can improve a model's predictions. Methods We used a dataset from a completed phase-III clinical trial that evaluated the efficacy of minocycline for delaying the conversion from clinically isolated syndrome to multiple sclerosis (MS), using the McDonald 2005 diagnostic criteria. There were a total of 142 participants, and at the 2-year follow-up 81 had converted to MS, 29 remained stable, and 32 had uncertain outcomes. In a stratified 7-fold cross-validation, we trained three random forest (RF) ML models using MRI volumetric features and clinical variables to predict the conversion outcome, which represented new disease activity within 2 years of a first clinical demyelinating event. One RF was trained using subjects with the uncertain labels excluded (RFexclude), another RF was trained using the entire dataset but with assumed labels for the uncertain group (RFnaive), and a third, a probabilistic RF (PRF, a type of RF that can model label uncertainty) was trained on the entire dataset, with probabilistic labels assigned to the uncertain group. Results Probabilistic random forest outperformed both the RF models with the highest AUC (0.76, compared to 0.69 for RFexclude and 0.71 for RFnaive) and F1-score (86.6% compared to 82.6% for RFexclude and 76.8% for RFnaive). Conclusion Machine learning algorithms capable of modeling label uncertainty can improve predictive performance in datasets in which a substantial number of subjects have unknown outcomes.
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Affiliation(s)
- Maryam Tayyab
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Luanne M Metz
- Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - David K B Li
- Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Shannon Kolind
- Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Robert Carruthers
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Anthony Traboulsee
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Roger C Tam
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
- Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
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10
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Present and future of the diagnostic work-up of multiple sclerosis: the imaging perspective. J Neurol 2023; 270:1286-1299. [PMID: 36427168 PMCID: PMC9971159 DOI: 10.1007/s00415-022-11488-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 11/26/2022]
Abstract
In recent years, the use of magnetic resonance imaging (MRI) for the diagnostic work-up of multiple sclerosis (MS) has evolved considerably. The 2017 McDonald criteria show high sensitivity and accuracy in predicting a second clinical attack in patients with a typical clinically isolated syndrome and allow an earlier diagnosis of MS. They have been validated, are evidence-based, simplify the clinical use of MRI criteria and improve MS patients' management. However, to limit the risk of misdiagnosis, they should be applied by expert clinicians only after the careful exclusion of alternative diagnoses. Recently, new MRI markers have been proposed to improve diagnostic specificity for MS and reduce the risk of misdiagnosis. The central vein sign and chronic active lesions (i.e., paramagnetic rim lesions) may increase the specificity of MS diagnostic criteria, but further effort is necessary to validate and standardize their assessment before implementing them in the clinical setting. The feasibility of subpial demyelination assessment and the clinical relevance of leptomeningeal enhancement evaluation in the diagnostic work-up of MS appear more limited. Artificial intelligence tools may capture MRI attributes that are beyond the human perception, and, in the future, artificial intelligence may complement human assessment to further ameliorate the diagnostic work-up and patients' classification. However, guidelines that ensure reliability, interpretability, and validity of findings obtained from artificial intelligence approaches are still needed to implement them in the clinical scenario. This review provides a summary of the most recent updates regarding the application of MRI for the diagnosis of MS.
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11
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Marzi C, d'Ambrosio A, Diciotti S, Bisecco A, Altieri M, Filippi M, Rocca MA, Storelli L, Pantano P, Tommasin S, Cortese R, De Stefano N, Tedeschi G, Gallo A. Prediction of the information processing speed performance in multiple sclerosis using a machine learning approach in a large multicenter magnetic resonance imaging data set. Hum Brain Mapp 2022; 44:186-202. [PMID: 36255155 PMCID: PMC9783441 DOI: 10.1002/hbm.26106] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 06/02/2022] [Accepted: 09/24/2022] [Indexed: 02/05/2023] Open
Abstract
Many patients with multiple sclerosis (MS) experience information processing speed (IPS) deficits, and the Symbol Digit Modalities Test (SDMT) has been recommended as a valid screening test. Magnetic resonance imaging (MRI) has markedly improved the understanding of the mechanisms associated with cognitive deficits in MS. However, which structural MRI markers are the most closely related to cognitive performance is still unclear. We used the multicenter 3T-MRI data set of the Italian Neuroimaging Network Initiative to extract multimodal data (i.e., demographic, clinical, neuropsychological, and structural MRIs) of 540 MS patients. We aimed to assess, through machine learning techniques, the contribution of brain MRI structural volumes in the prediction of IPS deficits when combined with demographic and clinical features. We trained and tested the eXtreme Gradient Boosting (XGBoost) model following a rigorous validation scheme to obtain reliable generalization performance. We carried out a classification and a regression task based on SDMT scores feeding each model with different combinations of features. For the classification task, the model trained with thalamus, cortical gray matter, hippocampus, and lesions volumes achieved an area under the receiver operating characteristic curve of 0.74. For the regression task, the model trained with cortical gray matter and thalamus volumes, EDSS, nucleus accumbens, lesions, and putamen volumes, and age reached a mean absolute error of 0.95. In conclusion, our results confirmed that damage to cortical gray matter and relevant deep and archaic gray matter structures, such as the thalamus and hippocampus, is among the most relevant predictors of cognitive performance in MS.
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Affiliation(s)
- Chiara Marzi
- MS Center and 3T‐MRI Research Unit, Department of Advanced Medical and Surgical Sciences (DAMSS)University of Campania “Luigi Vanvitelli”NapoliItaly,Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi” – DEIAlma Mater Studiorum – University of BolognaBolognaItaly
| | - Alessandro d'Ambrosio
- MS Center and 3T‐MRI Research Unit, Department of Advanced Medical and Surgical Sciences (DAMSS)University of Campania “Luigi Vanvitelli”NapoliItaly
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi” – DEIAlma Mater Studiorum – University of BolognaBolognaItaly,Alma Mater Research Institute for Human‐Centered Artificial IntelligenceUniversity of BolognaBolognaItaly
| | - Alvino Bisecco
- MS Center and 3T‐MRI Research Unit, Department of Advanced Medical and Surgical Sciences (DAMSS)University of Campania “Luigi Vanvitelli”NapoliItaly
| | - Manuela Altieri
- MS Center and 3T‐MRI Research Unit, Department of Advanced Medical and Surgical Sciences (DAMSS)University of Campania “Luigi Vanvitelli”NapoliItaly,Department of PsychologyUniversity of Campania “Luigi Vanvitelli”NapoliItaly
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of NeuroscienceVita‐Salute San Raffaele University, IRCCS San Raffaele Scientific InstituteMilanItaly,Neurology and Neurophysiology UnitVita‐Salute San Raffaele University, IRCCS San Raffaele Scientific InstituteMilanItaly
| | - Maria Assunta Rocca
- Neuroimaging Research Unit, Division of NeuroscienceVita‐Salute San Raffaele University, IRCCS San Raffaele Scientific InstituteMilanItaly,Neurology and Neurophysiology UnitVita‐Salute San Raffaele University, IRCCS San Raffaele Scientific InstituteMilanItaly
| | - Loredana Storelli
- Neuroimaging Research Unit, Division of NeuroscienceVita‐Salute San Raffaele University, IRCCS San Raffaele Scientific InstituteMilanItaly
| | - Patrizia Pantano
- Department of Human NeurosciencesSapienza University of RomeRomeItaly,IRCCS NeuromedPozzilliItaly
| | - Silvia Tommasin
- Department of Human NeurosciencesSapienza University of RomeRomeItaly
| | - Rosa Cortese
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaSienaItaly
| | - Nicola De Stefano
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaSienaItaly
| | - Gioacchino Tedeschi
- MS Center and 3T‐MRI Research Unit, Department of Advanced Medical and Surgical Sciences (DAMSS)University of Campania “Luigi Vanvitelli”NapoliItaly
| | - Antonio Gallo
- MS Center and 3T‐MRI Research Unit, Department of Advanced Medical and Surgical Sciences (DAMSS)University of Campania “Luigi Vanvitelli”NapoliItaly
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Aslam N, Khan IU, Bashamakh A, Alghool FA, Aboulnour M, Alsuwayan NM, Alturaif RK, Brahimi S, Aljameel SS, Al Ghamdi K. Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22207856. [PMID: 36298206 PMCID: PMC9609137 DOI: 10.3390/s22207856] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/29/2022] [Accepted: 10/11/2022] [Indexed: 05/17/2023]
Abstract
Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients' data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization.
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Affiliation(s)
- Nida Aslam
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
- Correspondence:
| | - Irfan Ullah Khan
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Asma Bashamakh
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Fatima A. Alghool
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Menna Aboulnour
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Noorah M. Alsuwayan
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Rawa’a K. Alturaif
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Samiha Brahimi
- Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Sumayh S. Aljameel
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Kholoud Al Ghamdi
- Department of Physiology, College of Medicine, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
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13
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Afzal HMR, Luo S, Ramadan S, Khari M, Chaudhary G, Lechner-Scott J. Prediction of Conversion from CIS to Clinically Definite Multiple Sclerosis Using Convolutional Neural Networks. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:5154896. [PMID: 35872945 PMCID: PMC9307372 DOI: 10.1155/2022/5154896] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 06/13/2022] [Indexed: 11/18/2022]
Abstract
Multiple sclerosis (MS) is a chronic neurological disease of the central nervous system (CNS). Early diagnosis of MS is highly desirable as treatments are more effective in preventing MS-related disability when given in the early stages of the disease. The main aim of this research is to predict the occurrence of a second MS-related clinical event, which indicates the conversion of clinically isolated syndrome (CIS) to clinically definite MS (CDMS). In this study, we apply a branch of artificial intelligence known as deep learning and develop a fully automated algorithm primed with convolutional neural network (CNN) that has the ability to learn from MRI scan features. The basic architecture of our algorithm is that of the VGG16 CNN model, but amended such that it can handle MRI DICOM images. A dataset comprised of scans acquired using two different scanners was used for the purposes of verification of the algorithm. A group of 49 patients had volumetric MRI scans taken at onset of the disease and then again one year later using one of the two scanners. In total, this yielded 7360 images which were then used for training, validation, and testing of the algorithm. Initially, these raw images were taken through 4 steps of preprocessing. In order to boost the efficiency of the process, we pretrained our algorithm using the publicly available ADNI dataset used to classify Alzheimer's disease. Finally, we used our preprocessed dataset to train and test the algorithm. Clinical evaluation conducted a year after the first time point revealed that 26 of the 49 patients had converted to CDMS, while the remaining 23 had not. Results of testing showed that our algorithm was able to predict the clinical results with an accuracy of 88.8% and with an area under the curve (AUC) of 91%. A highly accurate algorithm was developed using CNN approach to reliably predict conversion of patients with CIS to CDMS using MRI data from two different scanners.
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Affiliation(s)
- H. M. Rehan Afzal
- School of Electrical Engineering and Computing, University of Newcastle, Callaghan, NSW 2308, Australia
| | - Suhuai Luo
- School of Electrical Engineering and Computing, University of Newcastle, Callaghan, NSW 2308, Australia
| | - Saadallah Ramadan
- Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia
| | - Manju Khari
- Jawaharlal Nehru University, New Delhi, India
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14
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Role of artificial intelligence in MS clinical practice. Neuroimage Clin 2022; 35:103065. [PMID: 35661470 PMCID: PMC9163993 DOI: 10.1016/j.nicl.2022.103065] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 05/04/2022] [Accepted: 05/26/2022] [Indexed: 11/24/2022]
Abstract
For medical applications, machine learning (including deep learning) are the most commonly used artificial intelligence (AI) approaches. It can improve multiple sclerosis (MS) diagnosis, prognostication and treatment monitoring. Thanks to AI, MRI and cognitive phenotypes of MS patients were identified. AI can shorten MRI protocols for MS, allowing the application of advanced techniques. 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.
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15
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Azizian M, Ghasemi Darestani N, Aliabadi A, Afzali M, Tavoosi N, Fosouli M, Khataei J, Aali H, Nourian SMA. Predictive value of number and volume of demyelinating plaques in treatment response in patients with multiple sclerosis treated with INF-B. AMERICAN JOURNAL OF NEURODEGENERATIVE DISEASE 2022; 11:10-16. [PMID: 35600511 PMCID: PMC9123433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/27/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Multiple Sclerosis (MS) is an autoimmune, inflammatory disease of the central nervous system. Magnetic resonance imaging (MRI) findings are associated with disease clinical activity and response to treatment. This study aimed to evaluate the future value of plaque number and volume in MRI as radiological criteria in determining the treatment response to INF-B in patients with MS. METHODS This is a cross-sectional study performed in 2016-2021 in Iran on patients with the newly diagnosed (less than one year) relapsing-remitting MS. Brain MRI was taken for all patients. The number and volumes of the MS plaques were evaluated from FLAIR images by the two radiologists. Patients were treated with INF-B1a with a dosage of 12 million units equal to 44 micrograms subcutaneously, three times per week. Patients were visited monthly by neurologists to examine their clinical status. After one year, the brain MRI was conducted with the similar characteristics to the beginning of the study, and the number and volume of MS plaques were measured again. RESULTS The study population consisted of 33 males and 90 females with a mean age of 28.37 ± 6.29 years. The mean Expanded Disability Status Scale (EDSS) of the patients was 3.16 ± 0.23 at the beginning of the study. The specificity for a 50% reduction in the number and volume of plaques as two separate criteria was the same and equal to 100%. The sensitivity of the number and volume of plaques were 65.5% and 90.6%, respectively. In addition, considering 10% as the cut-off point of the number of plaques, the sensitivity of the number of plaques as a criterion was equal to the sensitivity of the plaque volume. CONCLUSION The results of this study showed that imaging criteria provide a more objective tool for evaluating the effectiveness of treatment. These findings indicate that the number and volume of plaques could be two reliable MRI imaging criteria for assessing therapy response. The number of plaques was less accurate than the volume of plaques.
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Affiliation(s)
- Maryam Azizian
- School of Medicine, Kerman University of Medical SciencesKerman, Iran
| | | | - Athena Aliabadi
- Advanced Diagnostic and Interventional Radiology Research Center, Tehran University of Medical ScienceTehran, Iran
| | - Mahdieh Afzali
- Department of Neurology, School of Medicine, Yas Hospital, Tehran University of Medical SciencesTehran, Iran
| | - Nooshin Tavoosi
- Department of Midwifery, School of Nursing and Midwifery, Islamic Azad University Shahrekord BranchShahrekord, Iran
| | - Mahnaz Fosouli
- Department of Radiology, Isfahan University of Medical SciencesIsfahan, Iran
| | - Jalil Khataei
- Department of Radiology, Isfahan University of Medical SciencesIsfahan, Iran
| | - Halimeh Aali
- Department of Internal Medicine, University of Medical SciencesZabol, Iran
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16
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A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders. BIOLOGY 2022; 11:biology11030469. [PMID: 35336842 PMCID: PMC8945195 DOI: 10.3390/biology11030469] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/12/2022] [Accepted: 03/14/2022] [Indexed: 01/19/2023]
Abstract
Simple Summary This study represents a resourceful review article that can deliver resources on neurological diseases and their implemented classification algorithms to reveal the future direction of researchers. Researchers interested in studying neurological diseases and previously implemented techniques in this field can follow this article. Various challenges occur in detecting different stages of the disorders. A limited amount of labeled and unlabeled datasets and other limitations is represented in this article to assist them in finding out the directions. The authors’ purpose for composing this article is to make a straightforward and concrete path for researchers to quickly find the way and the scope in this field for implementing future research on neurological disease detection. Abstract Neurological disorders (NDs) are becoming more common, posing a concern to pregnant women, parents, healthy infants, and children. Neurological disorders arise in a wide variety of forms, each with its own set of origins, complications, and results. In recent years, the intricacy of brain functionalities has received a better understanding due to neuroimaging modalities, such as magnetic resonance imaging (MRI), magnetoencephalography (MEG), and positron emission tomography (PET), etc. With high-performance computational tools and various machine learning (ML) and deep learning (DL) methods, these modalities have discovered exciting possibilities for identifying and diagnosing neurological disorders. This study follows a computer-aided diagnosis methodology, leading to an overview of pre-processing and feature extraction techniques. The performance of existing ML and DL approaches for detecting NDs is critically reviewed and compared in this article. A comprehensive portion of this study also shows various modalities and disease-specified datasets that detect and records images, signals, and speeches, etc. Limited related works are also summarized on NDs, as this domain has significantly fewer works focused on disease and detection criteria. Some of the standard evaluation metrics are also presented in this study for better result analysis and comparison. This research has also been outlined in a consistent workflow. At the conclusion, a mandatory discussion section has been included to elaborate on open research challenges and directions for future work in this emerging field.
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17
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Moazami F, Lefevre-Utile A, Papaloukas C, Soumelis V. Machine Learning Approaches in Study of Multiple Sclerosis Disease Through Magnetic Resonance Images. Front Immunol 2021; 12:700582. [PMID: 34456913 PMCID: PMC8385534 DOI: 10.3389/fimmu.2021.700582] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 07/26/2021] [Indexed: 11/13/2022] Open
Abstract
Multiple sclerosis (MS) is one of the most common autoimmune diseases which is commonly diagnosed and monitored using magnetic resonance imaging (MRI) with a combination of clinical manifestations. The purpose of this review is to highlight the main applications of Machine Learning (ML) models and their performance in the MS field using MRI. We reviewed the articles of the last decade and grouped them based on the applications of ML in MS using MRI data into four categories: 1) Automated diagnosis of MS, 2) Prediction of MS disease progression, 3) Differentiation of MS stages, 4) Differentiation of MS from similar disorders.
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Affiliation(s)
- Faezeh Moazami
- Université de Paris, Institut de Recherche Saint-Louis, Institut National de la Santé et de la Recherche Médicale (INSERM) U976, Hôpital Saint-Louis, Paris, France
| | - Alain Lefevre-Utile
- Université de Paris, Institut de Recherche Saint-Louis, Institut National de la Santé et de la Recherche Médicale (INSERM) U976, Hôpital Saint-Louis, Paris, France.,Université Paris-Saclay, Saint Aubin, France.,Assistance Publique Hopitaux de Paris (APHP), General Pediatric and Pediatric Emergency Department, Jean Verdier Hospital, Bondy, France
| | - Costas Papaloukas
- Department of Biological Applications and Technology, University of Ioannina, Ioannina, Greece
| | - Vassili Soumelis
- Université de Paris, Institut de Recherche Saint-Louis, Institut National de la Santé et de la Recherche Médicale (INSERM) U976, Hôpital Saint-Louis, Paris, France.,Assistance Publique Hopitaux de Paris (APHP), Hôpital Saint-Louis, Immunology-Histocompatibility Department, Paris, France
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Shoeibi A, Khodatars M, Jafari M, Moridian P, Rezaei M, Alizadehsani R, Khozeimeh F, Gorriz JM, Heras J, Panahiazar M, Nahavandi S, Acharya UR. Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review. Comput Biol Med 2021; 136:104697. [PMID: 34358994 DOI: 10.1016/j.compbiomed.2021.104697] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 07/22/2021] [Accepted: 07/25/2021] [Indexed: 11/18/2022]
Abstract
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far; among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. Diagnosing MS using MRI is time-consuming, tedious, and prone to manual errors. Research on the implementation of computer aided diagnosis system (CADS) based on artificial intelligence (AI) to diagnose MS involves conventional machine learning and deep learning (DL) methods. In conventional machine learning, feature extraction, feature selection, and classification steps are carried out by using trial and error; on the contrary, these steps in DL are based on deep layers whose values are automatically learn. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities is provided. Initially, the steps involved in various CADS proposed using MRI modalities and DL techniques for MS diagnosis are investigated. The important preprocessing techniques employed in various works are analyzed. Most of the published papers on MS diagnosis using MRI modalities and DL are presented. The most significant challenges facing and future direction of automated diagnosis of MS using MRI modalities and DL techniques are also provided.
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Affiliation(s)
- Afshin Shoeibi
- Faculty of Electrical Engineering, Biomedical Data Acquisition Lab (BDAL), K. N. Toosi University of Technology, Tehran, Iran.
| | - Marjane Khodatars
- Faculty of Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Mahboobeh Jafari
- Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran
| | - Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Mitra Rezaei
- Electrical and Computer Engineering Dept., Tarbiat Modares University, Tehran, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - Fahime Khozeimeh
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - Juan Manuel Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, Spain; Department of Psychiatry. University of Cambridge, UK
| | - Jónathan Heras
- Department of Mathematics and Computer Science, University of La Rioja, La Rioja, Spain
| | | | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - U Rajendra Acharya
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Dept. of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan
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Baur C, Wiestler B, Muehlau M, Zimmer C, Navab N, Albarqouni S. Modeling Healthy Anatomy with Artificial Intelligence for Unsupervised Anomaly Detection in Brain MRI. Radiol Artif Intell 2021; 3:e190169. [PMID: 34136814 PMCID: PMC8204131 DOI: 10.1148/ryai.2021190169] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 01/26/2021] [Accepted: 02/01/2021] [Indexed: 04/12/2023]
Abstract
PURPOSE To develop an unsupervised deep learning model on MR images of normal brain anatomy to automatically detect deviations indicative of pathologic states on abnormal MR images. MATERIALS AND METHODS In this retrospective study, spatial autoencoders with skip-connections (which can learn to compress and reconstruct data) were leveraged to learn the normal variability of the brain from MR scans of healthy individuals. A total of 100 normal, in-house MR scans were used for training. Subsequently, as the model was unable to reconstruct anomalies well, this characteristic was exploited for detecting and delineating various diseases by computing the difference between the input data and their reconstruction. The unsupervised model was compared with a supervised U-Net- and threshold-based classifier trained on data from 50 patients with multiple sclerosis (in-house dataset) and 50 patients from The Cancer Imaging Archive. Both the unsupervised and supervised U-Net models were tested on five different datasets containing MR images of microangiopathy, glioblastoma, and multiple sclerosis. Precision-recall statistics and derivations thereof (mean area under the precision-recall curve, Dice score) were used to quantify lesion detection and segmentation performance. RESULTS The unsupervised approach outperformed the naive thresholding approach in lesion detection (mean F1 scores ranging from 17% to 62% vs 6.4% to 15% across the five different datasets) and performed similarly to the supervised U-Net (20%-64%) across a variety of pathologic conditions. This outperformance was mostly driven by improved precision compared with the thresholding approach (mean precisions, 15%-59% vs 3.4%-10%). The model was also developed to create an anomaly heatmap display. CONCLUSION The unsupervised deep learning model was able to automatically detect anomalies on brain MR images with high performance. Supplemental material is available for this article. Keywords: Brain/Brain Stem Computer Aided Diagnosis (CAD), Convolutional Neural Network (CNN), Experimental Investigations, Head/Neck, MR-Imaging, Quantification, Segmentation, Stacked Auto-Encoders, Technology Assessment, Tissue Characterization © RSNA, 2021.
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Zhou R, Zeng Q, Yang H, Xu Y, Tan G, Liu H, Wang L, Zhou H, Zhang M, Feng J, Jin T, Zhang X, Wang J, Zhang X, Gao F, Yang C, Bu B, Li C, Zhang M, Dong H, Lin A, Liu W, Wu L, Wang M, Tang Y, Wang H, Long Y, Wang Z, Zheng W. Status of Immunotherapy Acceptance in Chinese Patients With Multiple Sclerosis: Analysis of Multiple Sclerosis Patient Survival Report 2018. Front Neurol 2021; 12:651511. [PMID: 33897605 PMCID: PMC8060470 DOI: 10.3389/fneur.2021.651511] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 03/03/2021] [Indexed: 02/05/2023] Open
Abstract
Objective: The prevalence of multiple sclerosis (MS) in China is low, although it has been increasing recently. Owing to the paucity of data on immunotherapy acceptance in the Chinese population, we conducted this study to analyze factors affecting the acceptance of immunotherapy and selection of disease-modifying therapies (DMTs) based on personal and clinical data of patients with MS. Methods: In this study, data were obtained from the Multiple Sclerosis Patient Survival Report 2018, which was the first national survey of patients with MS in China. There were 1,212 patients with MS from 31 provinces who were treated at 49 Chinese hospitals over a 4-month period from May 2018 to August 2018, and the patients were asked to complete online questionnaires to assess their understanding of the disease. Results: In general, highly educated patients with frequent relapses were more willing to receive treatment regardless of DMTs or other immunotherapy, and patients with more understanding of the disease opted to be treated. Younger patient population, patients with severe disease course, and those with more symptoms were likely to choose the treatment. Moreover, a higher proportion of women chose to be treated with DMTs than with other immunotherapies. Conclusions: Education status and patient awareness of the disease impact the treatment acceptance in Chinese patients with MS. Therefore, we call for improving the awareness of MS disease and social security to help patients to improve their quality of life.
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Affiliation(s)
- Ran Zhou
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Qiuming Zeng
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Huan Yang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Yan Xu
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Guojun Tan
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Hongbo Liu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lihua Wang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Hongyu Zhou
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Meini Zhang
- Department of Neurology, The First Affiliated Hospital of Shanxi Medical University, Taiyuan, China
| | - Jinzhou Feng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Tao Jin
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, China
| | - Xinghu Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiawei Wang
- Department of Neurology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Xu Zhang
- Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Feng Gao
- Department of Neurology, Peking University First Hospital, Beijing, China
| | - Chunsheng Yang
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China
| | - Bitao Bu
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chunyang Li
- Department of Neurology, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China
| | - Min Zhang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Huiqing Dong
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Aiyu Lin
- Department of Neurology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Weibin Liu
- Department of Neurology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Lei Wu
- Department of Neurology, General Hospital of the People's Liberation Army, Beijing, China
| | - Manxia Wang
- Department of Neurology, The Second Hospital of Lanzhou University, Lanzhou, China
| | - Yulan Tang
- Department of Neurology, The First Affiliated Hospital, Guangxi Medical University, Nanning, China
| | - Honghao Wang
- Department of Neurology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Youming Long
- Department of Neurology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zhe Wang
- Department of Neurology, The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Weihong Zheng
- Department of Neurology, Affiliated Zhongshan Hospital, Xiamen University, Xiamen, China
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21
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Hartmann M, Fenton N, Dobson R. Current review and next steps for artificial intelligence in multiple sclerosis risk research. Comput Biol Med 2021; 132:104337. [PMID: 33773193 DOI: 10.1016/j.compbiomed.2021.104337] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 03/09/2021] [Accepted: 03/10/2021] [Indexed: 12/30/2022]
Abstract
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.
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Affiliation(s)
- Morghan Hartmann
- Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK.
| | - Norman Fenton
- Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK
| | - Ruth Dobson
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, E1 4NS, UK
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22
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Guerrero JM, Aguirre FS, Mota ML, Carrillo A. Advances for the Development of In Vitro Immunosensors for Multiple Sclerosis Diagnosis. BIOCHIP JOURNAL 2021. [DOI: 10.1007/s13206-021-00018-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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23
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Vázquez-Marrufo M, Sarrias-Arrabal E, García-Torres M, Martín-Clemente R, Izquierdo G. A systematic review of the application of machine-learning algorithms in multiple sclerosis. Neurologia 2021; 38:S0213-4853(20)30431-X. [PMID: 33549371 DOI: 10.1016/j.nrl.2020.10.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 08/20/2020] [Accepted: 10/11/2020] [Indexed: 11/24/2022] Open
Abstract
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.
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Affiliation(s)
- M Vázquez-Marrufo
- Departamento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Sevilla, España.
| | - E Sarrias-Arrabal
- Departamento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Sevilla, España
| | - M García-Torres
- Escuela Politécnica Superior, Universidad Pablo de Olavide, Sevilla, España
| | - R Martín-Clemente
- Departamento de Teoría de la Señal y Comunicaciones, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Sevilla, España
| | - G Izquierdo
- Unidad de Esclerosis Múltiple, Hospital VITHAS, Sevilla, España
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24
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McKinley R, Wepfer R, Aschwanden F, Grunder L, Muri R, Rummel C, Verma R, Weisstanner C, Reyes M, Salmen A, Chan A, Wagner F, Wiest R. Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks. Sci Rep 2021; 11:1087. [PMID: 33441684 PMCID: PMC7806997 DOI: 10.1038/s41598-020-79925-4] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 12/04/2020] [Indexed: 12/12/2022] Open
Abstract
Segmentation of white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. In this paper we explore segmentation solutions based on convolutional neural networks (CNNs) for providing fast, reliable segmentations of lesions and grey-matter structures in multi-modal MR imaging, and the performance of these methods when applied to out-of-centre data. We trained two state-of-the-art fully convolutional CNN architectures on the 2016 MSSEG training dataset, which was annotated by seven independent human raters: a reference implementation of a 3D Unet, and a more recently proposed 3D-to-2D architecture (DeepSCAN). We then retrained those methods on a larger dataset from a single centre, with and without labels for other brain structures. We quantified changes in performance owing to dataset shift, and changes in performance by adding the additional brain-structure labels. We also compared performance with freely available reference methods. Both fully-convolutional CNN methods substantially outperform other approaches in the literature when trained and evaluated in cross-validation on the MSSEG dataset, showing agreement with human raters in the range of human inter-rater variability. Both architectures showed drops in performance when trained on single-centre data and tested on the MSSEG dataset. When trained with the addition of weak anatomical labels derived from Freesurfer, the performance of the 3D Unet degraded, while the performance of the DeepSCAN net improved. Overall, the DeepSCAN network predicting both lesion and anatomical labels was the best-performing network examined.
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Affiliation(s)
- Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland.
| | - Rik Wepfer
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Fabian Aschwanden
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Lorenz Grunder
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Raphaela Muri
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | | | | | - Mauricio Reyes
- ARTORG Centre for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Anke Salmen
- University Clinic for Neurology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Andrew Chan
- University Clinic for Neurology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Franca Wagner
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland
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25
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Eichinger P, Zimmer C, Wiestler B. AI in Radiology: Where are we today in Multiple Sclerosis Imaging? ROFO-FORTSCHR RONTG 2020; 192:847-853. [DOI: 10.1055/a-1167-8402] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Background MR imaging is an essential component in managing patients with Multiple sclerosis (MS). This holds true for the initial diagnosis as well as for assessing the clinical course of MS. In recent years, a growing number of computer tools were developed to analyze imaging data in MS. This review gives an overview of the most important applications with special emphasis on artificial intelligence (AI).
Methods Relevant studies were identified through a literature search in recognized databases, and through parsing the references in studies found this way. Literature published as of November 2019 was included with a special focus on recent studies from 2018 and 2019.
Results There are a number of studies which focus on optimizing lesion visualization and lesion segmentation. Some of these studies accomplished these tasks with high accuracy, enabling a reproducible quantitative analysis of lesion loads. Some studies took a radiomics approach and aimed at predicting clinical endpoints such as the conversion from a clinically isolated syndrome to definite MS. Moreover, recent studies investigated synthetic imaging, i. e. imaging data that is not measured during an MR scan but generated by a computer algorithm to optimize the contrast between MS lesions and brain parenchyma.
Conclusion Computer-based image analysis and AI are hot topics in imaging MS. Some applications are ready for use in clinical routine. A major challenge for the future is to improve prediction of expected disease courses and thereby helping to find optimal treatment decisions on an individual level. With technical improvements, more questions arise about the integration of new tools into the radiological workflow.
Key Points:
Citation Format
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Affiliation(s)
- Paul Eichinger
- Department of Radiology, Klinikum rechts der Isar der Technischen Universität München, München, Germany
| | - Claus Zimmer
- Department of Neuroradiology, Klinikum rechts der Isar der Technischen Universität München, München, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, Klinikum rechts der Isar der Technischen Universität München, München, Germany
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26
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Machine learning for classification and prediction of brain diseases: recent advances and upcoming challenges. Curr Opin Neurol 2020; 33:439-450. [DOI: 10.1097/wco.0000000000000838] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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27
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Maggi P, Fartaria MJ, Jorge J, La Rosa F, Absinta M, Sati P, Meuli R, Du Pasquier R, Reich DS, Cuadra MB, Granziera C, Richiardi J, Kober T. CVSnet: A machine learning approach for automated central vein sign assessment in multiple sclerosis. NMR IN BIOMEDICINE 2020; 33:e4283. [PMID: 32125737 PMCID: PMC7754184 DOI: 10.1002/nbm.4283] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 01/22/2020] [Accepted: 02/05/2020] [Indexed: 05/28/2023]
Abstract
The central vein sign (CVS) is an efficient imaging biomarker for multiple sclerosis (MS) diagnosis, but its application in clinical routine is limited by inter-rater variability and the expenditure of time associated with manual assessment. We describe a deep learning-based prototype for automated assessment of the CVS in white matter MS lesions using data from three different imaging centers. We retrospectively analyzed data from 3 T magnetic resonance images acquired on four scanners from two different vendors, including adults with MS (n = 42), MS mimics (n = 33, encompassing 12 distinct neurological diseases mimicking MS) and uncertain diagnosis (n = 5). Brain white matter lesions were manually segmented on FLAIR* images. Perivenular assessment was performed according to consensus guidelines and used as ground truth, yielding 539 CVS-positive (CVS+ ) and 448 CVS-negative (CVS- ) lesions. A 3D convolutional neural network ("CVSnet") was designed and trained on 47 datasets, keeping 33 for testing. FLAIR* lesion patches of CVS+ /CVS- lesions were used for training and validation (n = 375/298) and for testing (n = 164/150). Performance was evaluated lesion-wise and subject-wise and compared with a state-of-the-art vesselness filtering approach through McNemar's test. The proposed CVSnet approached human performance, with lesion-wise median balanced accuracy of 81%, and subject-wise balanced accuracy of 89% on the validation set, and 91% on the test set. The process of CVS assessment, in previously manually segmented lesions, was ~ 600-fold faster using the proposed CVSnet compared with human visual assessment (test set: 4 seconds vs. 40 minutes). On the validation and test sets, the lesion-wise performance outperformed the vesselness filter method (P < 0.001). The proposed deep learning prototype shows promising performance in differentiating MS from its mimics. Our approach was evaluated using data from different hospitals, enabling larger multicenter trials to evaluate the benefit of introducing the CVS marker into MS diagnostic criteria.
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Affiliation(s)
- Pietro Maggi
- Department of Neurology, Lausanne University Hospital, Lausanne, Switzerland
- Department of Neurology, Saint-Luc University Hospital, Brussels, Belgium
| | - Mário João Fartaria
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Signal Processing Laboratory (LTS5), École Polytechnique Fédéral de Lausanne, Switzerland
| | - João Jorge
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Francesco La Rosa
- Signal Processing Laboratory (LTS5), École Polytechnique Fédéral de Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Martina Absinta
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Pascal Sati
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Reto Meuli
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Renaud Du Pasquier
- Department of Neurology, Lausanne University Hospital, Lausanne, Switzerland
| | - Daniel S. Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Meritxell Bach Cuadra
- Signal Processing Laboratory (LTS5), École Polytechnique Fédéral de Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Medical Image Analysis Laboratory (MIAL), Centre d’Imagerie BioMédicale (CIBM), Lausanne, Switzerland
| | - Cristina Granziera
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jonas Richiardi
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Signal Processing Laboratory (LTS5), École Polytechnique Fédéral de Lausanne, Switzerland
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Grzegorski T, Losy J. What do we currently know about the clinically isolated syndrome suggestive of multiple sclerosis? An update. Rev Neurosci 2020; 31:335-349. [DOI: 10.1515/revneuro-2019-0084] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 09/22/2019] [Indexed: 12/31/2022]
Abstract
AbstractMultiple sclerosis (MS) is a chronic, demyelinating, not fully understood disease of the central nervous system. The first demyelinating clinical episode is called clinically isolated syndrome (CIS) suggestive of MS. Although the most common manifestations of CIS are long tracts dysfunction and unilateral optic neuritis, it can also include isolated brainstem syndromes, cerebellar involvement, and polysymptomatic clinical image. Recently, the frequency of CIS diagnosis has decreased due to the more sensitive and less specific 2017 McDonald criteria compared with the revisions from 2010. Not all patients with CIS develop MS. The risk of conversion can be estimated based on many predictive factors including epidemiological, ethnical, clinical, biochemical, radiological, immunogenetic, and other markers. The management of CIS is nowadays widely discussed among clinicians and neuroscientists. To date, interferons, glatiramer acetate, teriflunomide, cladribine, and some other agents have been evaluated in randomized, placebo-controlled, double-blind studies relying on large groups of patients with the first demyelinating event. All of these drugs were shown to have beneficial effects in patients with CIS and might be used routinely in the future. The goal of this article is to explore the most relevant topics regarding CIS as well as to provide the most recent information in the field. The review presents CIS definition, classification, clinical image, predictive factors, and management. What is more, this is one of very few reviews summarizing the topic in the light of the 2017 McDonald criteria.
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Affiliation(s)
- Tomasz Grzegorski
- Department of Clinical Neuroimmunology, Chair of Neurology, Poznan University of Medical Sciences, 49 Przybyszewskiego Street, 60-355Poznan, Poland
| | - Jacek Losy
- Department of Clinical Neuroimmunology, Chair of Neurology, Poznan University of Medical Sciences, 49 Przybyszewskiego Street, 60-355Poznan, Poland
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29
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Seccia R, Gammelli D, Dominici F, Romano S, Landi AC, Salvetti M, Tacchella A, Zaccaria A, Crisanti A, Grassi F, Palagi L. Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis. PLoS One 2020; 15:e0230219. [PMID: 32196512 PMCID: PMC7083323 DOI: 10.1371/journal.pone.0230219] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 02/24/2020] [Indexed: 12/27/2022] Open
Abstract
Multiple Sclerosis (MS) progresses at an unpredictable rate, but predictions on the disease course in each patient would be extremely useful to tailor therapy to the individual needs. We explore different machine learning (ML) approaches to predict whether a patient will shift from the initial Relapsing-Remitting (RR) to the Secondary Progressive (SP) form of the disease, using only "real world" data available in clinical routine. The clinical records of 1624 outpatients (207 in the SP phase) attending the MS service of Sant'Andrea hospital, Rome, Italy, were used. Predictions at 180, 360 or 720 days from the last visit were obtained considering either the data of the last available visit (Visit-Oriented setting), comparing four classical ML methods (Random Forest, Support Vector Machine, K-Nearest Neighbours and AdaBoost) or the whole clinical history of each patient (History-Oriented setting), using a Recurrent Neural Network model, specifically designed for historical data. Missing values were handled by removing either all clinical records presenting at least one missing parameter (Feature-saving approach) or the 3 clinical parameters which contained missing values (Record-saving approach). The performances of the classifiers were rated using common indicators, such as Recall (or Sensitivity) and Precision (or Positive predictive value). In the visit-oriented setting, the Record-saving approach yielded Recall values from 70% to 100%, but low Precision (5% to 10%), which however increased to 50% when considering only predictions for which the model returned a probability above a given "confidence threshold". For the History-oriented setting, both indicators increased as prediction time lengthened, reaching values of 67% (Recall) and 42% (Precision) at 720 days. We show how "real world" data can be effectively used to forecast the evolution of MS, leading to high Recall values and propose innovative approaches to improve Precision towards clinically useful values.
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Affiliation(s)
- Ruggiero Seccia
- Dept. of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Daniele Gammelli
- Dept. of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Fabio Dominici
- Dept. of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Silvia Romano
- Dept. of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Anna Chiara Landi
- Dept. of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Marco Salvetti
- Dept. of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
- IRCCS Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Italy
| | - Andrea Tacchella
- Dept. of Physics, Istituto dei Sistemi Complessi (ISC)-CNR, UOS Sapienza, Rome, Italy
| | - Andrea Zaccaria
- Dept. of Physics, Istituto dei Sistemi Complessi (ISC)-CNR, UOS Sapienza, Rome, Italy
| | | | - Francesca Grassi
- Dept. of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Laura Palagi
- Dept. of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
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30
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Stafford IS, Kellermann M, Mossotto E, Beattie RM, MacArthur BD, Ennis S. A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases. NPJ Digit Med 2020; 3:30. [PMID: 32195365 PMCID: PMC7062883 DOI: 10.1038/s41746-020-0229-3] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 01/17/2020] [Indexed: 02/07/2023] Open
Abstract
Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.
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Affiliation(s)
- I. S. Stafford
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
- Institute for Life Sciences, University of Southampton, Southampton, UK
| | - M. Kellermann
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
| | - E. Mossotto
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
- Institute for Life Sciences, University of Southampton, Southampton, UK
| | - R. M. Beattie
- Department of Paediatric Gastroenterology, Southampton Children’s Hospital, Southampton, UK
| | - B. D. MacArthur
- Institute for Life Sciences, University of Southampton, Southampton, UK
| | - S. Ennis
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
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Law MT, Traboulsee AL, Li DK, Carruthers RL, Freedman MS, Kolind SH, Tam R. Machine learning in secondary progressive multiple sclerosis: an improved predictive model for short-term disability progression. Mult Scler J Exp Transl Clin 2019; 5:2055217319885983. [PMID: 31723436 PMCID: PMC6836306 DOI: 10.1177/2055217319885983] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 08/23/2019] [Accepted: 10/09/2019] [Indexed: 11/15/2022] Open
Abstract
Background Enhanced prediction of progression in secondary progressive multiple sclerosis (SPMS) could improve clinical trial design. Machine learning (ML) algorithms are methods for training predictive models with minimal human intervention. Objective To evaluate individual and ensemble model performance built using decision tree (DT)-based algorithms compared to logistic regression (LR) and support vector machines (SVMs) for predicting SPMS disability progression. Methods SPMS participants (n = 485) enrolled in a 2-year placebo-controlled (negative) trial assessing the efficacy of MBP8298 were classified as progressors if a 6-month sustained increase in Expanded Disability Status Scale (EDSS) (≥1.0 or ≥0.5 for a baseline of ≤5.5 or ≥6.0 respectively) was observed. Variables included EDSS, Multiple Sclerosis Functional Composite component scores, T2 lesion volume, brain parenchymal fraction, disease duration, age, and sex. Area under the receiver operating characteristic curve (AUC) was the primary outcome for model evaluation. Results Three DT-based models had greater AUCs (61.8%, 60.7%, and 60.2%) than independent and ensemble SVM (52.4% and 51.0%) and LR (49.5% and 51.1%). Conclusion SPMS disability progression was best predicted by non-parametric ML. If confirmed, ML could select those with highest progression risk for inclusion in SPMS trial cohorts and reduce the number of low-risk individuals exposed to experimental therapies.
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Affiliation(s)
- Marco Tk Law
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Anthony L Traboulsee
- Department of Neurology, The University of British Columbia, Vancouver, BC, Canada
| | - David Kb Li
- Department of Radiology, The University of British Columbia, Vancouver, BC, Canada
| | - Robert L Carruthers
- Department of Neurology, The University of British Columbia, Vancouver, BC, Canada
| | - Mark S Freedman
- Department of Medicine, University of Ottawa and The Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Shanon H Kolind
- Department of Radiology, The University of British Columbia, Vancouver, BC, Canada
| | - Roger Tam
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada
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