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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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Li S, Xing X, Hua X, Zhang Y, Wu J, Shan C, Wang H, Zheng M, Xu J. Electroacupuncture modulates abnormal brain connectivity after ischemia reperfusion injury in rats: A graph theory-based approach. Brain Behav 2024; 14:e3504. [PMID: 38698583 PMCID: PMC11066419 DOI: 10.1002/brb3.3504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 03/29/2024] [Accepted: 04/06/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND Electroacupuncture (EA) has been shown to facilitate brain plasticity-related functional recovery following ischemic stroke. The functional magnetic resonance imaging technique can be used to determine the range and mode of brain activation. After stroke, EA has been shown to alter brain connectivity, whereas EA's effect on brain network topology properties remains unclear. An evaluation of EA's effects on global and nodal topological properties in rats with ischemia reperfusion was conducted in this study. METHODS AND RESULTS There were three groups of adult male Sprague-Dawley rats: sham-operated group (sham group), middle cerebral artery occlusion/reperfusion (MCAO/R) group, and MCAO/R plus EA (MCAO/R + EA) group. The differences in global and nodal topological properties, including shortest path length, global efficiency, local efficiency, small-worldness index, betweenness centrality (BC), and degree centrality (DC) were estimated. Graphical network analyses revealed that, as compared with the sham group, the MCAO/R group demonstrated a decrease in BC value in the right ventral hippocampus and increased BC in the right substantia nigra, accompanied by increased DC in the left nucleus accumbens shell (AcbSh). The BC was increased in the right hippocampus ventral and decreased in the right substantia nigra after EA intervention, and MCAO/R + EA resulted in a decreased DC in left AcbSh compared to MCAO/R. CONCLUSION The results of this study provide a potential basis for EA to promote cognitive and motor function recovery after ischemic stroke.
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Affiliation(s)
- Si‐Si Li
- School of Rehabilitation ScienceShanghai University of Traditional Chinese MedicineShanghaiChina
- Department of Physical Medicine and RehabilitationThe Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Xiang‐Xin Xing
- Center of Rehabilitation MedicineYueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina
| | - Xu‐Yun Hua
- Department of Traumatology and OrthopedicsYueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina
| | - Yu‐Wen Zhang
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghaiChina
| | - Jia‐Jia Wu
- Center of Rehabilitation MedicineYueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina
| | - Chun‐Lei Shan
- School of Rehabilitation ScienceShanghai University of Traditional Chinese MedicineShanghaiChina
- Center of Rehabilitation MedicineYueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of EducationShanghaiChina
| | - He Wang
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghaiChina
| | - Mou‐Xiong Zheng
- Department of Traumatology and OrthopedicsYueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina
| | - Jian‐Guang Xu
- School of Rehabilitation ScienceShanghai University of Traditional Chinese MedicineShanghaiChina
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of EducationShanghaiChina
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3
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Li J, Jiang P, An Q, Wang GG, Kong HF. Medical image identification methods: A review. Comput Biol Med 2024; 169:107777. [PMID: 38104516 DOI: 10.1016/j.compbiomed.2023.107777] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/30/2023] [Accepted: 11/28/2023] [Indexed: 12/19/2023]
Abstract
The identification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Medical image data mainly include electronic health record data and gene information data, etc. Although intelligent imaging provided a good scheme for medical image analysis over traditional methods that rely on the handcrafted features, it remains challenging due to the diversity of imaging modalities and clinical pathologies. Many medical image identification methods provide a good scheme for medical image analysis. The concepts pertinent of methods, such as the machine learning, deep learning, convolutional neural networks, transfer learning, and other image processing technologies for medical image are analyzed and summarized in this paper. We reviewed these recent studies to provide a comprehensive overview of applying these methods in various medical image analysis tasks, such as object detection, image classification, image registration, segmentation, and other tasks. Especially, we emphasized the latest progress and contributions of different methods in medical image analysis, which are summarized base on different application scenarios, including classification, segmentation, detection, and image registration. In addition, the applications of different methods are summarized in different application area, such as pulmonary, brain, digital pathology, brain, skin, lung, renal, breast, neuromyelitis, vertebrae, and musculoskeletal, etc. Critical discussion of open challenges and directions for future research are finally summarized. Especially, excellent algorithms in computer vision, natural language processing, and unmanned driving will be applied to medical image recognition in the future.
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Affiliation(s)
- Juan Li
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China; School of Artificial Intelligence, Wuchang University of Technology, Wuhan, 430223, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Pan Jiang
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China
| | - Qing An
- School of Artificial Intelligence, Wuchang University of Technology, Wuhan, 430223, China
| | - Gai-Ge Wang
- School of Computer Science and Technology, Ocean University of China, Qingdao, 266100, China.
| | - Hua-Feng Kong
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China.
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4
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Chen E, Barile B, Durand-Dubief F, Grenier T, Sappey-Marinier D. Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity. Front Neurosci 2024; 17:1268860. [PMID: 38304076 PMCID: PMC10830765 DOI: 10.3389/fnins.2023.1268860] [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: 07/28/2023] [Accepted: 12/18/2023] [Indexed: 02/03/2024] Open
Abstract
Multiple Sclerosis (MS) is an autoimmune disease that combines chronic inflammatory and neurodegenerative processes underlying different clinical forms of evolution, such as relapsing-remitting, secondary progressive, or primary progressive MS. This identification is usually performed by clinical evaluation at the diagnosis or during the course of the disease for the secondary progressive phase. In parallel, magnetic resonance imaging (MRI) analysis is a mandatory diagnostic complement. Identifying the clinical form from MR images is therefore a helpful and challenging task. Here, we propose a new approach for the automatic classification of MS forms based on conventional MRI (i.e., T1-weighted images) that are commonly used in clinical context. For this purpose, we investigated the morphological connectome features using graph based convolutional neural network. Our results obtained from the longitudinal study of 91 MS patients highlight the performance (F1-score) of this approach that is better than state-of-the-art as 3D convolutional neural networks. These results open the way for clinical applications such as disability correlation only using T1-weighted images.
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Affiliation(s)
- Enyi Chen
- CREATIS, CNRS UMR 5220, INSERM U1294, Université de Lyon, Université Claude Bernard-Lyon 1, INSA Lyon, Lyon, France
| | - Berardino Barile
- CREATIS, CNRS UMR 5220, INSERM U1294, Université de Lyon, Université Claude Bernard-Lyon 1, INSA Lyon, Lyon, France
| | - Françoise Durand-Dubief
- CREATIS, CNRS UMR 5220, INSERM U1294, Université de Lyon, Université Claude Bernard-Lyon 1, INSA Lyon, Lyon, France
- Service de Sclérose en Plaques, des Pathologies de la Myéline et Neuro-Inflammation, Groupement Hospitalier Est, Hôpital Neurologique, Bron, France
| | - Thomas Grenier
- CREATIS, CNRS UMR 5220, INSERM U1294, Université de Lyon, Université Claude Bernard-Lyon 1, INSA Lyon, Lyon, France
| | - Dominique Sappey-Marinier
- CREATIS, CNRS UMR 5220, INSERM U1294, Université de Lyon, Université Claude Bernard-Lyon 1, INSA Lyon, Lyon, France
- CERMEP - Imagerie du Vivant, Université de Lyon, Bron, France
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Faraji H, Soleymani F, Yaseri M, Sahraian MA, Abdollahiasl A, Meftah A, Nikfar S. Choosing the Best Instrument for Measuring Health Spillover Effect in Caregivers of Patients With Multiple Sclerosis. Value Health Reg Issues 2024; 39:49-56. [PMID: 37979543 DOI: 10.1016/j.vhri.2023.08.008] [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: 09/09/2022] [Revised: 05/22/2023] [Accepted: 08/01/2023] [Indexed: 11/20/2023]
Abstract
OBJECTIVES To measure the health spillover effect in caregivers of patients with multiple sclerosis (MS), we aimed to select the best instrument from 2 common health-related quality of life (QoL) instruments, the 3-level EQ-5D (EQ-5D-3L) and the Health Utilities Index Mark 3 (HUI-3), by assessing them. METHODS Using consecutive sampling, 452 primary caregivers of patients with MS were asked to fill out a Care-related QoL instrument (CarerQol-7D), EQ-5D-3L, HUI-3, and the Center for Epidemiologic Studies Depression Scale between October 2019 and May 2020. Convergent and clinical validity were assessed to measure spillover effect in caregivers of patients with MS. RESULTS A strong correlation of health-utility scores between EQ-5D-3L and HUI-3 (r = 0.914, P < .01) was observed. The 95% limit of agreement (LoA) for CarerQol-7D and HUI-3 (-10.6 to 8.2) was narrower than the LoA for CarerQol-7D and EQ-5D-3L (-15.1 to 17.1). Both EQ-5D-3L and HUI-3 proved clinical validity for the QoL of caregivers. The CarerQoL-7D score was significantly lower in female (P < .001), single (P < .014), lower-educated (P < .001), parent's relatives (P < .001), and unemployed (P < .001) caregivers. CONCLUSIONS We found that both, EQ-5D-3L and HUI-3, were appropriate for measuring caregivers' QoL, although HUI-3 was a better choice because of its narrower LoA. Our findings suggest researchers should use HUI-3 to measure the quality-adjusted life-year of caregivers to aggregate with the QoL of patients in the denominator of an economic evaluation equation, such as the cost-effective ratio.
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Affiliation(s)
- Hoda Faraji
- Department of Pharmacoeconomics and Pharmaceutical Administration, Faculty of Pharmacy, Tehran University of Medical Sciences, Iran, Tehran
| | - Fatemeh Soleymani
- Department of Pharmacoeconomics and Pharmaceutical Administration, Faculty of Pharmacy, Tehran University of Medical Sciences, Iran, Tehran
| | - Mehdi Yaseri
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Iran, Tehran
| | - Mohammad Ali Sahraian
- Neurology Department, MS Research Centre, Neuroscience institute, Tehran University of Medical Sciences, Iran, Tehran
| | - Akbar Abdollahiasl
- Department of Pharmacoeconomics and Pharmaceutical Administration, Faculty of Pharmacy, Tehran University of Medical Sciences, Iran, Tehran
| | - Azin Meftah
- Faculty of Health Sciences Pharmacy, University of Eastern Finland, Finland, Kuopio
| | - Shekoufeh Nikfar
- Department of Pharmacoeconomics and Pharmaceutical Administration, Faculty of Pharmacy, Tehran University of Medical Sciences, Iran, Tehran.
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Martinez-Heras E, Solana E, Vivó F, Lopez-Soley E, Calvi A, Alba-Arbalat S, Schoonheim MM, Strijbis EM, Vrenken H, Barkhof F, Rocca MA, Filippi M, Pagani E, Groppa S, Fleischer V, Dineen RA, Bellenberg B, Lukas C, Pareto D, Rovira A, Sastre-Garriga J, Collorone S, Prados F, Toosy A, Ciccarelli O, Saiz A, Blanco Y, Llufriu S. Diffusion-based structural connectivity patterns of multiple sclerosis phenotypes. J Neurol Neurosurg Psychiatry 2023; 94:916-923. [PMID: 37321841 DOI: 10.1136/jnnp-2023-331531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND We aimed to describe the severity of the changes in brain diffusion-based connectivity as multiple sclerosis (MS) progresses and the microstructural characteristics of these networks that are associated with distinct MS phenotypes. METHODS Clinical information and brain MRIs were collected from 221 healthy individuals and 823 people with MS at 8 MAGNIMS centres. The patients were divided into four clinical phenotypes: clinically isolated syndrome, relapsing-remitting, secondary progressive and primary progressive. Advanced tractography methods were used to obtain connectivity matrices. Then, differences in whole-brain and nodal graph-derived measures, and in the fractional anisotropy of connections between groups were analysed. Support vector machine algorithms were used to classify groups. RESULTS Clinically isolated syndrome and relapsing-remitting patients shared similar network changes relative to controls. However, most global and local network properties differed in secondary progressive patients compared with the other groups, with lower fractional anisotropy in most connections. Primary progressive participants had fewer differences in global and local graph measures compared with clinically isolated syndrome and relapsing-remitting patients, and reductions in fractional anisotropy were only evident for a few connections. The accuracy of support vector machine to discriminate patients from healthy controls based on connection was 81%, and ranged between 64% and 74% in distinguishing among the clinical phenotypes. CONCLUSIONS In conclusion, brain connectivity is disrupted in MS and has differential patterns according to the phenotype. Secondary progressive is associated with more widespread changes in connectivity. Additionally, classification tasks can distinguish between MS types, with subcortical connections being the most important factor.
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Affiliation(s)
- Eloy Martinez-Heras
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Elisabeth Solana
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Francesc Vivó
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Elisabet Lopez-Soley
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Alberto Calvi
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Salut Alba-Arbalat
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Eva M Strijbis
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Hugo Vrenken
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Milano, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Milano, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Sergiu Groppa
- Department of Neurology, Neurostimulation and Neuroimaging, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Vinzenz Fleischer
- Department of Neurology, Neurostimulation and Neuroimaging, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Robert A Dineen
- Mental Health and Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK; and NIHR Nottingham Biomedical Research Centre, Nottingham, UK
| | - Barbara Bellenberg
- Institute of Neuroradiology, St. Josef Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Carsten Lukas
- Institute of Neuroradiology, St. Josef Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology, Vall d'Hebron University Hospital and Research Institute (VHIR), Barcelona, Spain
| | - Alex Rovira
- Section of Neuroradiology, Department of Radiology, Vall d'Hebron University Hospital and Research Institute (VHIR), Barcelona, Spain
| | - Jaume Sastre-Garriga
- Neurology-Neuroimmunology Department, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Sara Collorone
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, London, UK
| | - Ferran Prados
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, London, UK
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- E-health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Ahmed Toosy
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, London, UK
| | - Olga Ciccarelli
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, London, UK
| | - Albert Saiz
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Yolanda Blanco
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Sara Llufriu
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
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Abdelgaied MY, Abd El-Aziz MK, Amin NS, El Tayebi HM. What's your cup of tea? The role of herbal compounds in the management of multiple sclerosis. Mult Scler Relat Disord 2023; 76:104799. [PMID: 37300922 DOI: 10.1016/j.msard.2023.104799] [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: 12/11/2022] [Accepted: 06/03/2023] [Indexed: 06/12/2023]
Abstract
Multiple Sclerosis (MS) is a chronic, inflammatory, neurodegenerative disease that is characterized by a complex etiology. Efforts towards the management of MS have long been directed towards symptomatic relief, as well as the use of immune-modulatory, disease modifying therapies; however, inconsistent treatment responses still prevail, increasing the risk for disease progression. While a great deal of research attempted to unravel the complexity of treatment responses in light of epigenetic variability, parallel efforts in the direction of alternative medicine may be as paramount. Herbal compounds have long been regarded as safe and versatile options for aiding in various disorders, including neurodegenerative conditions like MS. Numerous studies have taken interest in a myriad of herbal plants for their potential benefit in alleviating some of the most common MS symptoms such as spasticity and fatigue, delaying the progression of the disease, as well as influencing the overall quality of life for MS patients. This review aims to provide a comprehensive overview of recent clinical studies examining the effects of various herbal plants on different aspects of MS, in an attempt to shed light on an important tool for aiding in the management of this complex and multifactorial disease.
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Affiliation(s)
- Mohamed Y Abdelgaied
- Department of Pharmacology and Toxicology, Faculty of Pharmacy and Biotechnology, Clinical Pharmacology and Pharmacogenomics Research Group, Head of Clinical Pharmacology and Pharmacogenomics Research Group, German University in Cairo, Cairo 11835, Egypt
| | - Mostafa K Abd El-Aziz
- Department of Pharmacology and Toxicology, Faculty of Pharmacy and Biotechnology, Clinical Pharmacology and Pharmacogenomics Research Group, Head of Clinical Pharmacology and Pharmacogenomics Research Group, German University in Cairo, Cairo 11835, Egypt
| | - Nada Sherif Amin
- Department of Pharmacology and Toxicology, Faculty of Pharmacy and Biotechnology, Clinical Pharmacology and Pharmacogenomics Research Group, Head of Clinical Pharmacology and Pharmacogenomics Research Group, German University in Cairo, Cairo 11835, Egypt
| | - Hend M El Tayebi
- Department of Pharmacology and Toxicology, Faculty of Pharmacy and Biotechnology, Clinical Pharmacology and Pharmacogenomics Research Group, Head of Clinical Pharmacology and Pharmacogenomics Research Group, German University in Cairo, Cairo 11835, Egypt.
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8
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Diagnostic performance of artificial intelligence in multiple sclerosis: a systematic review and meta-analysis. Neurol Sci 2023; 44:499-517. [PMID: 36303065 DOI: 10.1007/s10072-022-06460-7] [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: 08/10/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND The expansion of the availability of advanced imaging methods needs more time, expertise, and resources which is in contrast to the primary goal of the imaging techniques. To overcome most of these difficulties, artificial intelligence (AI) can be used. A number of studies used AI models for multiple sclerosis (MS) diagnosis and reported diverse results. Therefore, we aim to perform a comprehensive systematic review and meta-analysis study on the role of AI in the diagnosis of MS. METHODS We performed a systematic search using four databases including PubMed, Scopus, Web of Science, and IEEE. Studies that applied deep learning or AI to the diagnosis of MS based on any modalities were considered eligible in our study. The accuracy, sensitivity, specificity, precision, and area under curve (AUC) were pooled with a random-effects model and 95% confidence interval (CI). RESULTS After the screening, 41 articles with 5989 individuals met the inclusion criteria and were included in our qualitative and quantitative synthesis. Our analysis showed that the overall accuracy among studies was 94% (95%CI: 93%, 96%). The pooled sensitivity and specificity were 92% (95%CI: 90%, 95%) and 93% (95%CI: 90%, 96%), respectively. Furthermore, our analysis showed 92% precision in MS diagnosis for AI studies (95%CI: 88%, 97%). Also, the overall pooled AUC was 93% (95%CI: 89%, 96%). CONCLUSION Overall, AI models can further improve our diagnostic practice in MS patients. Our results indicate that the use of AI can aid the clinicians in accurate diagnosis of MS and improve current diagnostic approaches as most of the parameters including accuracy, sensitivity, specificity, precision, and AUC were considerably high, especially when using MRI data.
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Rehák Bučková B, Mareš J, Škoch A, Kopal J, Tintěra J, Dineen R, Řasová K, Hlinka J. Multimodal-neuroimaging machine-learning analysis of motor disability in multiple sclerosis. Brain Imaging Behav 2023; 17:18-34. [PMID: 36396890 DOI: 10.1007/s11682-022-00737-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 11/19/2022]
Abstract
Motor disability is a dominant and restricting symptom in multiple sclerosis, yet its neuroimaging correlates are not fully understood. We apply statistical and machine learning techniques on multimodal neuroimaging data to discriminate between multiple sclerosis patients and healthy controls and to predict motor disability scores in the patients. We examine the data of sixty-four multiple sclerosis patients and sixty-five controls, who underwent the MRI examination and the evaluation of motor disability scales. The modalities used comprised regional fractional anisotropy, regional grey matter volumes, and functional connectivity. For analysis, we employ two approaches: high-dimensional support vector machines run on features selected by Fisher Score (aiming for maximal classification accuracy), and low-dimensional logistic regression on the principal components of data (aiming for increased interpretability). We apply analogous regression methods to predict symptom severity. While fractional anisotropy provides the classification accuracy of 96.1% and 89.9% with both approaches respectively, including other modalities did not bring further improvement. Concerning the prediction of motor impairment, the low-dimensional approach performed more reliably. The first grey matter volume component was significantly correlated (R = 0.28-0.46, p < 0.05) with most clinical scales. In summary, we identified the relationship between both white and grey matter changes and motor impairment in multiple sclerosis. Furthermore, we were able to achieve the highest classification accuracy based on quantitative MRI measures of tissue integrity between patients and controls yet reported, while also providing a low-dimensional classification approach with comparable results, paving the way to interpretable machine learning models of brain changes in multiple sclerosis.
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Affiliation(s)
- Barbora Rehák Bučková
- The Czech Technical University in Prague, Karlovo namesti 13, 121 35, Prague, Czech Republic.,Institute of Computer Science of the Czech Academy of Sciences, Pod Vodarenskou vezi 2/271, 182 00, Prague, Czech Republic.,National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic
| | - Jan Mareš
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic.,Institute for Clinical and Experimental Medicine, Videnska 1958, 140 21, Prague, Czech Republic
| | - Antonín Škoch
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic.,Institute for Clinical and Experimental Medicine, Videnska 1958, 140 21, Prague, Czech Republic
| | - Jakub Kopal
- Institute of Computer Science of the Czech Academy of Sciences, Pod Vodarenskou vezi 2/271, 182 00, Prague, Czech Republic
| | - Jaroslav Tintěra
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic.,Institute for Clinical and Experimental Medicine, Videnska 1958, 140 21, Prague, Czech Republic
| | - Robert Dineen
- University of Nottingham, Queen's Medical Centre, NG7 2UH, Nottingham, UK.,National Institute for Health Research, Nottingham Biomedical Research Centre, NG1 5DU, Nottingham, UK
| | - Kamila Řasová
- Charles University, Ruska 87, 100 00, Prague, Czech Republic
| | - Jaroslav Hlinka
- Institute of Computer Science of the Czech Academy of Sciences, Pod Vodarenskou vezi 2/271, 182 00, Prague, Czech Republic. .,National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic.
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10
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Hejazi S, Karwowski W, Farahani FV, Marek T, Hancock PA. Graph-Based Analysis of Brain Connectivity in Multiple Sclerosis Using Functional MRI: A Systematic Review. Brain Sci 2023; 13:brainsci13020246. [PMID: 36831789 PMCID: PMC9953947 DOI: 10.3390/brainsci13020246] [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: 12/06/2022] [Revised: 01/16/2023] [Accepted: 01/17/2023] [Indexed: 02/04/2023] Open
Abstract
(1) Background: Multiple sclerosis (MS) is an immune system disease in which myelin in the nervous system is affected. This abnormal immune system mechanism causes physical disabilities and cognitive impairment. Functional magnetic resonance imaging (fMRI) is a common neuroimaging technique used in studying MS. Computational methods have recently been applied for disease detection, notably graph theory, which helps researchers understand the entire brain network and functional connectivity. (2) Methods: Relevant databases were searched to identify articles published since 2000 that applied graph theory to study functional brain connectivity in patients with MS based on fMRI. (3) Results: A total of 24 articles were included in the review. In recent years, the application of graph theory in the MS field received increased attention from computational scientists. The graph-theoretical approach was frequently combined with fMRI in studies of functional brain connectivity in MS. Lower EDSSs of MS stage were the criteria for most of the studies (4) Conclusions: This review provides insights into the role of graph theory as a computational method for studying functional brain connectivity in MS. Graph theory is useful in the detection and prediction of MS and can play a significant role in identifying cognitive impairment associated with MS.
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Affiliation(s)
- Sara Hejazi
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
- Correspondence:
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
| | - Farzad V. Farahani
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Tadeusz Marek
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, 30-348 Kraków, Poland
| | - P. A. Hancock
- Department of Psychology, University of Central Florida, Orlando, FL 32816, USA
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11
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Tang C, Hu C, Sun J, Wang SH, Zhang YD. NSCGCN: A novel deep GCN model to diagnosis COVID-19. Comput Biol Med 2022; 150:106151. [PMID: 36244303 PMCID: PMC9559311 DOI: 10.1016/j.compbiomed.2022.106151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 09/05/2022] [Accepted: 09/24/2022] [Indexed: 11/03/2022]
Abstract
AIM Corona Virus Disease 2019 (COVID-19) was a lung disease with high mortality and was highly contagious. Early diagnosis of COVID-19 and distinguishing it from pneumonia was beneficial for subsequent treatment. OBJECTIVES Recently, Graph Convolutional Network (GCN) has driven a significant contribution to disease diagnosis. However, limited by the nature of the graph convolution algorithm, deep GCN has an over-smoothing problem. Most of the current GCN models are shallow neural networks, which do not exceed five layers. Furthermore, the objective of this study is to develop a novel deep GCN model based on the DenseGCN and the pre-trained model of deep Convolutional Neural Network (CNN) to complete the diagnosis of chest X-ray (CXR) images. METHODS We apply the pre-trained model of deep CNN to perform feature extraction on the data to complete the extraction of pixel-level features in the image. And then, to extract the potential relationship between the obtained features, we propose Neighbourhood Feature Reconstruction Algorithm to reconstruct them into graph-structured data. Finally, we design a deep GCN model that exploits the graph-structured data to diagnose COVID-19 effectively. In the deep GCN model, we propose a Node-Self Convolution Algorithm (NSC) based on feature fusion to construct a deep GCN model called NSCGCN (Node-Self Convolution Graph Convolutional Network). RESULTS Experiments were carried out on the Computed Tomography (CT) and CXR datasets. The results on the CT dataset confirmed that: compared with the six state-of-the-art (SOTA) shallow GCN models, the accuracy and sensitivity of the proposed NSCGCN had improve 8% as sensitivity (Sen.) = 87.50%, F1 score = 97.37%, precision (Pre.) = 89.10%, accuracy (Acc.) = 97.50%, area under the ROC curve (AUC) = 97.09%. Moreover, the results on the CXR dataset confirmed that: compared with the fourteen SOTA GCN models, sixteen SOTA CNN transfer learning models and eight SOTA COVID-19 diagnosis methods on the COVID-19 dataset. Our proposed method had best performances as Sen. = 96.45%, F1 score = 96.45%, Pre. = 96.61%, Acc. = 96.45%, AUC = 99.22%. CONCLUSION Our proposed NSCGCN model is effective and performed better than the thirty-eight SOTA methods. Thus, the proposed NSC could help build deep GCN models. Our proposed COVID-19 diagnosis method based on the NSCGCN model could help radiologists detect pneumonia from CXR images and distinguish COVID-19 from Ordinary Pneumonia (OPN). The source code of this work will be publicly available at https://github.com/TangChaosheng/NSCGCN.
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Affiliation(s)
- Chaosheng Tang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, PR China.
| | - Chaochao Hu
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, PR China.
| | - Junding Sun
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, PR China.
| | - Shui-Hua Wang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, PR China; School of Architecture Building and Civil Engineering, Loughborough University, Loughborough, LE11 3TU, UK; School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK.
| | - Yu-Dong Zhang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, PR China; School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK; Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
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12
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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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Barile B, Ashtari P, Stamile C, Marzullo A, Maes F, Durand-Dubief F, Van Huffel S, Sappey-Marinier D. Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome. Front Robot AI 2022; 9:926255. [PMID: 36313252 PMCID: PMC9608344 DOI: 10.3389/frobt.2022.926255] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/18/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose: The main goal of this study is to investigate the discrimination power of Grey Matter (GM) thickness connectome data between Multiple Sclerosis (MS) clinical profiles using statistical and Machine Learning (ML) methods. Materials and Methods: A dataset composed of 90 MS patients acquired at the MS clinic of Lyon Neurological Hospital was used for the analysis. Four MS profiles were considered, corresponding to Clinical Isolated Syndrome (CIS), Relapsing-Remitting MS (RRMS), Secondary Progressive MS (SPMS), and Primary Progressive MS (PPMS). Each patient was classified in one of these profiles by our neurologist and underwent longitudinal MRI examinations including T1-weighted image acquisition at each examination, from which the GM tissue was segmented and the cortical GM thickness measured. Following the GM parcellation using two different atlases (FSAverage and Glasser 2016), the morphological connectome was built and six global metrics (Betweenness Centrality (BC), Assortativity (r), Transitivity (T), Efficiency (Eg), Modularity (Q) and Density (D)) were extracted. Based on their connectivity metrics, MS profiles were first statistically compared and second, classified using four different learning machines (Logistic Regression, Random Forest, Support Vector Machine and AdaBoost), combined in a higher level ensemble model by majority voting. Finally, the impact of the GM spatial resolution on the MS clinical profiles classification was analyzed. Results: Using binary comparisons between the four MS clinical profiles, statistical differences and classification performances higher than 0.7 were observed. Good performances were obtained when comparing the two early clinical forms, RRMS and PPMS (F1 score of 0.86), and the two neurodegenerative profiles, PPMS and SPMS (F1 score of 0.72). When comparing the two atlases, slightly better performances were obtained with the Glasser 2016 atlas, especially between RRMS with PPMS (F1 score of 0.83), compared to the FSAverage atlas (F1 score of 0.69). Also, the thresholding value for graph binarization was investigated suggesting more informative graph properties in the percentile range between 0.6 and 0.8. Conclusion: An automated pipeline was proposed for the classification of MS clinical profiles using six global graph metrics extracted from the GM morphological connectome of MS patients. This work demonstrated that GM morphological connectivity data could provide good classification performances by combining four simple ML models, without the cost of long and complex MR techniques, such as MR diffusion, and/or deep learning architectures.
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Affiliation(s)
- Berardino Barile
- CREATIS (UMR 5220 CNRS & U1294 INSERM), Université Claude Bernard Lyon1, INSA-Lyon, Université de Lyon, Lyon, France
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | - Pooya Ashtari
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | | | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
| | - Frederik Maes
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | - Françoise Durand-Dubief
- CREATIS (UMR 5220 CNRS & U1294 INSERM), Université Claude Bernard Lyon1, INSA-Lyon, Université de Lyon, Lyon, France
- Hôpital Neurologique, Service de Neurologie, Hospices Civils de Lyon, Bron, France
| | | | - Dominique Sappey-Marinier
- CREATIS (UMR 5220 CNRS & U1294 INSERM), Université Claude Bernard Lyon1, INSA-Lyon, Université de Lyon, Lyon, France
- CERMEP–Imagerie du Vivant, Université de Lyon, Lyon, France
- *Correspondence: Dominique Sappey-Marinier,
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14
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Yılmaz Acar Z, Başçiftçi F, Ekmekci AH. Future activity prediction of multiple sclerosis with 3D MRI using 3D discrete wavelet transform. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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15
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Huang C, Wang J, Wang SH, Zhang YD. Applicable artificial intelligence for brain disease: A survey. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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16
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Efficacy of Transcranial Direct Current Stimulation (tDCS) on Balance and Gait in Multiple Sclerosis Patients: A Machine Learning Approach. J Clin Med 2022; 11:jcm11123505. [PMID: 35743575 PMCID: PMC9224780 DOI: 10.3390/jcm11123505] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 06/05/2022] [Accepted: 06/15/2022] [Indexed: 02/06/2023] Open
Abstract
Transcranial direct current stimulation (tDCS) has emerged as an appealing rehabilitative approach to improve brain function, with promising data on gait and balance in people with multiple sclerosis (MS). However, single variable weights have not yet been adequately assessed. Hence, the aim of this pilot randomized controlled trial was to evaluate the tDCS effects on balance and gait in patients with MS through a machine learning approach. In this pilot randomized controlled trial (RCT), we included people with relapsing−remitting MS and an Expanded Disability Status Scale >1 and <5 that were randomly allocated to two groups—a study group, undergoing a 10-session anodal motor cortex tDCS, and a control group, undergoing a sham treatment. Both groups underwent a specific balance and gait rehabilitative program. We assessed as outcome measures the Berg Balance Scale (BBS), Fall Risk Index and timed up-and-go and 6-min-walking tests at baseline (T0), the end of intervention (T1) and 4 (T2) and 6 weeks after the intervention (T3) with an inertial motion unit. At each time point, we performed a multiple factor analysis through a machine learning approach to allow the analysis of the influence of the balance and gait variables, grouping the participants based on the results. Seventeen MS patients (aged 40.6 ± 14.4 years), 9 in the study group and 8 in the sham group, were included. We reported a significant repeated measures difference between groups for distances covered (6MWT (meters), p < 0.03). At T1, we showed a significant increase in distance (m) with a mean difference (MD) of 37.0 [−59.0, 17.0] (p = 0.003), and in BBS with a MD of 2.0 [−4.0, 3.0] (p = 0.03). At T2, these improvements did not seem to be significantly maintained; however, considering the machine learning analysis, the Silhouette Index of 0.34, with a low cluster overlap trend, confirmed the possible short-term effects (T2), even at 6 weeks. Therefore, this pilot RCT showed that tDCS may provide non-sustained improvements in gait and balance in MS patients. In this scenario, machine learning could suggest evidence of prolonged beneficial effects.
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17
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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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18
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De Stefano N, Battaglini M, Pareto D, Cortese R, Zhang J, Oesingmann N, Prados F, Rocca MA, Valsasina P, Vrenken H, Gandini Wheeler-Kingshott CAM, Filippi M, Barkhof F, Rovira À. MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies. Neuroimage Clin 2022; 34:102972. [PMID: 35245791 PMCID: PMC8892169 DOI: 10.1016/j.nicl.2022.102972] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 02/22/2022] [Accepted: 02/23/2022] [Indexed: 11/24/2022]
Abstract
Sharing data from cooperative studies is essential to develop new biomarkers in MS. Differences in MRI acquisition, analysis, storage represent a substantial constraint. We review the state of the art and developments in the harmonization of MRI. We provide recommendations to harmonize large MRI datasets in the MS field.
There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources.
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Affiliation(s)
- Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Rosa Cortese
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Jian Zhang
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | | | - Ferran Prados
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, United Kingdom; e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Hugo Vrenken
- Amsterdam Neuroscience, MS Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Brain MRI 3T Research Center, C. Mondino National Neurological Institute, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy; Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Frederik Barkhof
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, United Kingdom; Amsterdam Neuroscience, MS Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
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An Accurate Multiple Sclerosis Detection Model Based on Exemplar Multiple Parameters Local Phase Quantization: ExMPLPQ. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12104920] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Multiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the white matter of the central nervous system that can be detected using magnetic resonance imaging (MRI). Many deep learning models for automated MS detection based on MRI have been presented in the literature. We developed a computationally lightweight machine learning model for MS diagnosis using a novel handcrafted feature engineering approach. The study dataset comprised axial and sagittal brain MRI images that were prospectively acquired from 72 MS and 59 healthy subjects who attended the Ozal University Medical Faculty in 2021. The dataset was divided into three study subsets: axial images only (n = 1652), sagittal images only (n = 1775), and combined axial and sagittal images (n = 3427) of both MS and healthy classes. All images were resized to 224 × 224. Subsequently, the features were generated with a fixed-size patch-based (exemplar) feature extraction model based on local phase quantization (LPQ) with three-parameter settings. The resulting exemplar multiple parameters LPQ (ExMPLPQ) features were concatenated to form a large final feature vector. The top discriminative features were selected using iterative neighborhood component analysis (INCA). Finally, a k-nearest neighbor (kNN) algorithm, Fine kNN, was deployed to perform binary classification of the brain images into MS vs. healthy classes. The ExMPLPQ-based model attained 98.37%, 97.75%, and 98.22% binary classification accuracy rates for axial, sagittal, and hybrid datasets, respectively, using Fine kNN with 10-fold cross-validation. Furthermore, our model outperformed 19 established pre-trained deep learning models that were trained and tested with the same data. Unlike deep models, the ExMPLPQ-based model is computationally lightweight yet highly accurate. It has the potential to be implemented as an automated diagnostic tool to screen brain MRIs for white matter lesions in suspected MS patients.
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20
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A survey of deep learning methods for multiple sclerosis identification using brain MRI images. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07099-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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21
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López-Dorado A, Ortiz M, Satue M, Rodrigo MJ, Barea R, Sánchez-Morla EM, Cavaliere C, Rodríguez-Ascariz JM, Orduna-Hospital E, Boquete L, Garcia-Martin E. Early Diagnosis of Multiple Sclerosis Using Swept-Source Optical Coherence Tomography and Convolutional Neural Networks Trained with Data Augmentation. SENSORS (BASEL, SWITZERLAND) 2021; 22:167. [PMID: 35009710 PMCID: PMC8747672 DOI: 10.3390/s22010167] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 05/07/2023]
Abstract
BACKGROUND The aim of this paper is to implement a system to facilitate the diagnosis of multiple sclerosis (MS) in its initial stages. It does so using a convolutional neural network (CNN) to classify images captured with swept-source optical coherence tomography (SS-OCT). METHODS SS-OCT images from 48 control subjects and 48 recently diagnosed MS patients have been used. These images show the thicknesses (45 × 60 points) of the following structures: complete retina, retinal nerve fiber layer, two ganglion cell layers (GCL+, GCL++) and choroid. The Cohen distance is used to identify the structures and the regions within them with greatest discriminant capacity. The original database of OCT images is augmented by a deep convolutional generative adversarial network to expand the CNN's training set. RESULTS The retinal structures with greatest discriminant capacity are the GCL++ (44.99% of image points), complete retina (26.71%) and GCL+ (22.93%). Thresholding these images and using them as inputs to a CNN comprising two convolution modules and one classification module obtains sensitivity = specificity = 1.0. CONCLUSIONS Feature pre-selection and the use of a convolutional neural network may be a promising, nonharmful, low-cost, easy-to-perform and effective means of assisting the early diagnosis of MS based on SS-OCT thickness data.
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Affiliation(s)
- Almudena López-Dorado
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28801 Alcalá de Henares, Spain; (A.L.-D.); (R.B.); (C.C.); (J.M.R.-A.)
| | - Miguel Ortiz
- Computer Vision, Imaging and Machine Intelligence Research Group, Interdisciplinary Center for Security, Reliability and Trust (SnT), University of Luxembourg, 4365 Luxembourg, Luxembourg;
| | - María Satue
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Department of Ophthalmology, Aragon Institute for Health Research (IIS Aragon), Miguel Servet University Hospital, University of Zaragoza, 50018 Zaragoza, Spain; (M.S.); (M.J.R.); (E.O.-H.)
| | - María J. Rodrigo
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Department of Ophthalmology, Aragon Institute for Health Research (IIS Aragon), Miguel Servet University Hospital, University of Zaragoza, 50018 Zaragoza, Spain; (M.S.); (M.J.R.); (E.O.-H.)
| | - Rafael Barea
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28801 Alcalá de Henares, Spain; (A.L.-D.); (R.B.); (C.C.); (J.M.R.-A.)
| | - Eva M. Sánchez-Morla
- Department of Psychiatry, Hospital 12 de Octubre Research Institute (i+12), 28041 Madrid, Spain;
- Faculty of Medicine, Complutense University of Madrid, 28040 Madrid, Spain
- Biomedical Research Networking Centre in Mental Health (CIBERSAM), 28029 Madrid, Spain
| | - Carlo Cavaliere
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28801 Alcalá de Henares, Spain; (A.L.-D.); (R.B.); (C.C.); (J.M.R.-A.)
| | - José M. Rodríguez-Ascariz
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28801 Alcalá de Henares, Spain; (A.L.-D.); (R.B.); (C.C.); (J.M.R.-A.)
| | - Elvira Orduna-Hospital
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Department of Ophthalmology, Aragon Institute for Health Research (IIS Aragon), Miguel Servet University Hospital, University of Zaragoza, 50018 Zaragoza, Spain; (M.S.); (M.J.R.); (E.O.-H.)
| | - Luciano Boquete
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28801 Alcalá de Henares, Spain; (A.L.-D.); (R.B.); (C.C.); (J.M.R.-A.)
| | - Elena Garcia-Martin
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Department of Ophthalmology, Aragon Institute for Health Research (IIS Aragon), Miguel Servet University Hospital, University of Zaragoza, 50018 Zaragoza, Spain; (M.S.); (M.J.R.); (E.O.-H.)
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22
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Zhao RZ, Zhang GX, Zhang WT, Yu WJ, Du L, Toledo MC, Olivera Leal IR, O'Farrill ZL, Izquierdo G. Ocular manifestations of multiple sclerosis in patients from three countries: A Web-based survey. Eur J Ophthalmol 2021; 32:2975-2981. [PMID: 34939452 DOI: 10.1177/11206721211069457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
OBJECTIVE This study evaluates the epidemiological characteristics, ophthalmological manifestations, and different therapeutic options available for patients with multiple sclerosis (MS) in China, Spain, and Cuba. METHODS A self-designed questionnaire was used to conduct a comparable descriptive cross-sectional study on patients with MS. The survey included patients' demographic data, ocular manifestations related to MS, and treatment methodology followed in the three countries. The online survey was designed using the Wenjuanxing survey platform, and a survey link was circulated through WhatsApp, WeChat, and emails. Quantitative data were expressed as mean and standard deviation, the Kruskal-Wallis test was used for non-parametric variables. Qualitative data were expressed as numerical and percentage. The chi-square test (χ2) was used to compare the group's response categories. The statistical difference was considered significant when p < 0.05. RESULTS The female-to-male ratio in all the three countries was 2-3:1, and relapsing-remitting MS (RRMS) was the most frequent in all three countries. Vision loss was slow and progressive in half of the patients from the three countries, with no significant differences (p = 0.524). A higher percentage of steroid treatment was observed in Chinese patients in comparison with the patients from other two countries (p < 0.001), and a similar trend was seen in the use of traditional medicines. Almost one-third of patients who did not receive any treatment recovered spontaneously in all the three countries (p = 0.097). CONCLUSIONS MS occurs more frequently in the relapsing-remitting clinical form and there is a clear female predominance. The first ocular crisis or clinical debut of MS is characterized by slow and progressive visual impairment, increasing and adding to other ocular manifestations during its evolutionary course. Spontaneous recovery of vision after an attack of optic neuritis in the course of MS is possible.
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Affiliation(s)
- Run-Ze Zhao
- Department of Ophthalmology, Eye Institute of PLA, 66352Xijing Hospital, Fourth Military Medical University, Shaanxi, The People of Republic of China.,Department of Ophthalmology, 113046Hermanos Ameijeiras Hospital, Havana, Cuba
| | - Guo-Xun Zhang
- Department of Neurology, 105849Yan'an University, Shaanxi, The People of Republic of China.,Multiple Sclerosis Unit, Neurology Service, 222071Vithas Nisa Hospital, Seville, Spain
| | - Wen-Ting Zhang
- Institute of Biomedicine of Seville, IBIS (University of Seville, HUVR, Government of Andalusia, CSIC), Spain; Department of Medical Biochemistry and Molecular Biology and Immunology, 16778University of Seville, 41009, Spain
| | - Wen-Jin Yu
- Department of Neurology, Xijing Hospital,Fourth Military Medicine University, Shaanxi, The People of Republic of China
| | - Lu Du
- Department of Ophthalmology, Eye Institute of PLA, 66352Xijing Hospital, Fourth Military Medical University, Shaanxi, The People of Republic of China
| | | | - I R Olivera Leal
- Department of Neurology, Hermanos Ameijeiras Hospital, Havana, Cuba
| | | | - G Izquierdo
- Multiple Sclerosis Unit, Neurology Service, 222071Vithas Nisa Hospital, Seville, Spain
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23
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Kontopodis EE, Papadaki E, Trivizakis E, Maris TG, Simos P, Papadakis GZ, Tsatsakis A, Spandidos DA, Karantanas A, Marias K. Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review). Exp Ther Med 2021; 22:1149. [PMID: 34504594 PMCID: PMC8393268 DOI: 10.3892/etm.2021.10583] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 07/29/2021] [Indexed: 12/18/2022] Open
Abstract
Computer-aided diagnosis systems aim to assist clinicians in the early identification of abnormal signs in order to optimize the interpretation of medical images and increase diagnostic precision. Multiple sclerosis (MS) and clinically isolated syndrome (CIS) are chronic inflammatory, demyelinating diseases affecting the central nervous system. Recent advances in deep learning (DL) techniques have led to novel computational paradigms in MS and CIS imaging designed for automatic segmentation and detection of areas of interest and automatic classification of anatomic structures, as well as optimization of neuroimaging protocols. To this end, there are several publications presenting artificial intelligence-based predictive models aiming to increase diagnostic accuracy and to facilitate optimal clinical management in patients diagnosed with MS and/or CIS. The current study presents a thorough review covering DL techniques that have been applied in MS and CIS during recent years, shedding light on their current advances and limitations.
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Affiliation(s)
- Eleftherios E Kontopodis
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Radiology, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Efrosini Papadaki
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Radiology, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Eleftherios Trivizakis
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Radiology, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Thomas G Maris
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Radiology, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Panagiotis Simos
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Psychiatry and Behavioral Sciences, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Georgios Z Papadakis
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Radiology, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Aristidis Tsatsakis
- Centre of Toxicology Science and Research, Faculty of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Demetrios A Spandidos
- Laboratory of Clinical Virology, Medical School, University of Crete, 71003 Heraklion, Greece
| | - Apostolos Karantanas
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Radiology, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Kostas Marias
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
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24
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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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25
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Cruciani F, Brusini L, Zucchelli M, Retuci Pinheiro G, Setti F, Boscolo Galazzo I, Deriche R, Rittner L, Calabrese M, Menegaz G. Interpretable deep learning as a means for decrypting disease signature in multiple sclerosis. J Neural Eng 2021; 18. [PMID: 34181581 DOI: 10.1088/1741-2552/ac0f4b] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/28/2021] [Indexed: 11/12/2022]
Abstract
Objective.The mechanisms driving multiple sclerosis (MS) are still largely unknown, calling for new methods allowing to detect and characterize tissue degeneration since the early stages of the disease. Our aim is to decrypt the microstructural signatures of the Primary Progressive versus the Relapsing-Remitting state of disease based on diffusion and structural magnetic resonance imaging data.Approach.A selection of microstructural descriptors, based on the 3D-Simple Harmonics Oscillator Based Reconstruction and Estimation and the set of new algebraically independent Rotation Invariant spherical harmonics Features, was considered and used to feed convolutional neural networks (CNNs) models. Classical measures derived from diffusion tensor imaging, that are fractional anisotropy and mean diffusivity, were used as benchmark for diffusion MRI (dMRI). Finally, T1-weighted images were also considered for the sake of comparison with the state-of-the-art. A CNN model was fit to each feature map and layerwise relevance propagation (LRP) heatmaps were generated for each model, target class and subject in the test set. Average heatmaps were calculated across correctly classified patients and size-corrected metrics were derived on a set of regions of interest to assess the LRP contrast between the two classes.Main results.Our results demonstrated that dMRI features extracted in grey matter tissues can help in disambiguating primary progressive multiple sclerosis from relapsing-remitting multiple sclerosis patients and, moreover, that LRP heatmaps highlight areas of high relevance which relate well with what is known from literature for MS disease.Significance.Within a patient stratification task, LRP allows detecting the input voxels that mostly contribute to the classification of the patients in either of the two classes for each feature, potentially bringing to light hidden data properties which might reveal peculiar disease-state factors.
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Affiliation(s)
- F Cruciani
- Department of Computer Science, University of Verona, Verona, Italy
| | - L Brusini
- Department of Computer Science, University of Verona, Verona, Italy
| | - M Zucchelli
- Athena Project-Team, Inria Sophia Antipolis-Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | - G Retuci Pinheiro
- MICLab, School of Electrical and Computer Engineering (FEEC), UNICAMP, Campinas, Brazil
| | - F Setti
- Department of Computer Science, University of Verona, Verona, Italy
| | | | - R Deriche
- Athena Project-Team, Inria Sophia Antipolis-Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | - L Rittner
- MICLab, School of Electrical and Computer Engineering (FEEC), UNICAMP, Campinas, Brazil
| | - M Calabrese
- Department of Neurosciences, Biomedicine and Movement, University of Verona, Verona, Italy
| | - G Menegaz
- Department of Computer Science, University of Verona, Verona, Italy
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26
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Barile B, Marzullo A, Stamile C, Durand-Dubief F, Sappey-Marinier D. Ensemble Learning for Multiple Sclerosis Disability Estimation Using Brain Structural Connectivity. Brain Connect 2021; 12:476-488. [PMID: 34269618 DOI: 10.1089/brain.2020.1003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Multiple Sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system characterized by demyelination and neurodegeneration processes. It leads to different clinical courses and degrees of disability that need to be anticipated by the neurologist for personalized therapy. Recently, machine learning (ML) techniques have reached a high level of performance in brain disease diagnosis and/or prognosis, but the decision process of a trained ML system is typically non-transparent. Using brain structural connectivity data, a fully automatic ensemble learning model, augmented with an interpretable model, is proposed for the estimation of MS patients' disability, measured by the Expanded Disability Status Scale (EDSS). METHOD An ensemble of four boosting-based models (GBM, XGBoost, CatBoost, LightBoost) organized following a stacking generalization scheme, was developed using DTI-based structural connectivity data. In addition, an interpretable model based on conditional logistic regression was developed to explain the best performances in terms of white matter (WM) links for three classes of EDSS (Low, Medium, High). RESULTS The ensemble model reached excellent level of performance (RMSE of 0.92 ± 0.28) compared to single-based models and provided a better EDSS estimation using DTI-based structural connectivity data compared to conventional MRI measures associated with patient data (age, gender and disease duration). Used for interpretation of the estimation process, the counterfactual method showed the importance of certain brain networks, corresponding mainly to left hemisphere WM links, connecting the left superior temporal with the left posterior cingulate and the right precuneus gray matter regions, and the inter-hemispheric WM links constituting the corpus callosum. Also, a better accuracy estimation was found for the high disability class. CONCLUSION The combination of advanced ML models and sensitive techniques such as DTI-based structural connectivity demonstrated to be useful for the estimation of MS patients' disability and to point out the most important brain WM networks involved in disability.
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Affiliation(s)
- Berardino Barile
- Université Claude Bernard Lyon 1, 27098, 1CREATIS (UMR5220 & INSERM U1206), 43 Boulevard du 11 Novembre 1918, Villeurbanne, Villeurbanne, France, 69100;
| | - Aldo Marzullo
- University of Calabria, 18950, Mathematics and Computer Science, Arcavacata di Rende, Calabria, Italy;
| | | | - Francoise Durand-Dubief
- Hospices Civils de Lyon, 26900, Lyon, Auvergne-Rhône-Alpes , France.,Université Claude Bernard Lyon 1, 27098, CREATIS (UMR5220 & INSERM U1206), Villeurbanne, Auvergne-Rhône-Alpes , France;
| | - Dominique Sappey-Marinier
- Université de Lyon, 133614, Lyon, Auvergne-Rhône-Alpes , France.,Université Claude Bernard Lyon 1, 27098, CREATIS (UMR5220 & INSERM U1206), Villeurbanne, Auvergne-Rhône-Alpes , France;
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27
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Muzio G, O’Bray L, Borgwardt K. Biological network analysis with deep learning. Brief Bioinform 2021; 22:1515-1530. [PMID: 33169146 PMCID: PMC7986589 DOI: 10.1093/bib/bbaa257] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 08/26/2020] [Accepted: 09/11/2020] [Indexed: 12/17/2022] Open
Abstract
Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in the form of a biological network. The rise of this data has created a need for new computational tools to analyze networks. One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). In this article, we describe biological networks and review the principles and underlying algorithms of GNNs. We then discuss domains in bioinformatics in which graph neural networks are frequently being applied at the moment, such as protein function prediction, protein-protein interaction prediction and in silico drug discovery and development. Finally, we highlight application areas such as gene regulatory networks and disease diagnosis where deep learning is emerging as a new tool to answer classic questions like gene interaction prediction and automatic disease prediction from data.
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Affiliation(s)
- Giulia Muzio
- Machine Learning and Computational Biology Lab at ETH Zürich
| | - Leslie O’Bray
- Machine Learning and Computational Biology Lab at ETH Zürich
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28
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Muzio G, O'Bray L, Borgwardt K. Biological network analysis with deep learning. Brief Bioinform 2021; 22:1515-1530. [PMID: 33169146 DOI: 10.1145/3447548.3467442] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 08/26/2020] [Accepted: 09/11/2020] [Indexed: 05/28/2023] Open
Abstract
Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in the form of a biological network. The rise of this data has created a need for new computational tools to analyze networks. One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). In this article, we describe biological networks and review the principles and underlying algorithms of GNNs. We then discuss domains in bioinformatics in which graph neural networks are frequently being applied at the moment, such as protein function prediction, protein-protein interaction prediction and in silico drug discovery and development. Finally, we highlight application areas such as gene regulatory networks and disease diagnosis where deep learning is emerging as a new tool to answer classic questions like gene interaction prediction and automatic disease prediction from data.
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Affiliation(s)
- Giulia Muzio
- Machine Learning and Computational Biology Lab at ETH Zürich
| | - Leslie O'Bray
- Machine Learning and Computational Biology Lab at ETH Zürich
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29
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Zhang Y, Hong D, McClement D, Oladosu O, Pridham G, Slaney G. Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging. J Neurosci Methods 2021; 353:109098. [PMID: 33582174 DOI: 10.1016/j.jneumeth.2021.109098] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 02/04/2021] [Accepted: 02/07/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND Deep learning using convolutional neural networks (CNNs) has shown great promise in advancing neuroscience research. However, the ability to interpret the CNNs lags far behind, confounding their clinical translation. NEW METHOD We interrogated 3 heatmap-generating techniques that have increasing generalizability for CNN interpretation: class activation mapping (CAM), gradient (Grad)-CAM, and Grad-CAM++. To investigate the impact of CNNs on heatmap generation, we also examined 6 different models trained to classify brain magnetic resonance imaging into 3 types: relapsing-remitting multiple sclerosis (RRMS), secondary progressive MS (SPMS), and control. Further, we designed novel methods to visualize and quantify the heatmaps to improve interpretability. RESULTS Grad-CAM showed the best heatmap localizing ability, and CNNs with a global average pooling layer and pretrained weights had the best classification performance. Based on the best-performing CNN model, called VGG19, the 95th percentile values of Grad-CAM in SPMS were significantly higher than RRMS, indicating greater heterogeneity. Further, voxel-wise analysis of the thresholded Grad-CAM confirmed the difference identified visually between RRMS and SPMS in discriminative brain regions: occipital versus frontal and occipital, or temporal/parietal. COMPARISON WITH EXISTING METHODS No study has examined the CAM methods together using clinical images. There is also lack of study on the impact of CNN architecture on heatmap outcomes, and of technologies to quantify heatmap patterns in clinical settings. CONCLUSIONS Grad-CAM outperforms CAM and Grad-CAM++. Integrating Grad-CAM, novel heatmap quantification approaches, and robust CNN models may be an effective strategy in identifying the most crucial brain areas underlying disease development in MS.
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Affiliation(s)
- Yunyan Zhang
- Radiology, University of Calgary, Alberta, T2N 4N1, Canada; Department of Clinical Neurosciences, University of Calgary, Alberta, T2N 4N1, Canada; Hotchkiss Brain Institute, University of Calgary, Alberta, T2N 4N1, Canada.
| | - Daphne Hong
- Departments of Schulich School of Engineering, University of Calgary, Alberta, T2N 4N1, Canada
| | - Daniel McClement
- Hotchkiss Brain Institute, University of Calgary, Alberta, T2N 4N1, Canada
| | - Olayinka Oladosu
- Hotchkiss Brain Institute, University of Calgary, Alberta, T2N 4N1, Canada; Department of Neuroscience, University of Calgary, Alberta, T2N 4N1, Canada
| | - Glen Pridham
- Hotchkiss Brain Institute, University of Calgary, Alberta, T2N 4N1, Canada
| | - Garth Slaney
- Departments of Schulich School of Engineering, University of Calgary, Alberta, T2N 4N1, Canada
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30
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Aoki S, Fujimori J, Mikami R, Hoshi K, Kawakami J, Sato K, Nakashima I. Assisting the diagnosis of multiple sclerosis using a set of regional brain volumes: A classification model for patients and healthy controls. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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31
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Lopatina A, Ropele S, Sibgatulin R, Reichenbach JR, Güllmar D. Investigation of Deep-Learning-Driven Identification of Multiple Sclerosis Patients Based on Susceptibility-Weighted Images Using Relevance Analysis. Front Neurosci 2020; 14:609468. [PMID: 33390890 PMCID: PMC7775402 DOI: 10.3389/fnins.2020.609468] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 11/30/2020] [Indexed: 01/22/2023] Open
Abstract
The diagnosis of multiple sclerosis (MS) is usually based on clinical symptoms and signs of damage to the central nervous system, which is assessed using magnetic resonance imaging. The correct interpretation of these data requires excellent clinical expertise and experience. Deep neural networks aim to assist clinicians in identifying MS using imaging data. However, before such networks can be integrated into clinical workflow, it is crucial to understand their classification strategy. In this study, we propose to use a convolutional neural network to identify MS patients in combination with attribution algorithms to investigate the classification decisions. The network was trained using images acquired with susceptibility-weighted imaging (SWI), which is known to be sensitive to the presence of paramagnetic iron components and is routinely applied in imaging protocols for MS patients. Different attribution algorithms were used to the trained network resulting in heatmaps visualizing the contribution of each input voxel to the classification decision. Based on the quantitative image perturbation method, we selected DeepLIFT heatmaps for further investigation. Single-subject analysis revealed veins and adjacent voxels as signs for MS, while the population-based study revealed relevant brain areas common to most subjects in a class. This pattern was found to be stable across different echo times and also for a multi-echo trained network. Intensity analysis of the relevant voxels revealed a group difference, which was found to be primarily based on the T1w magnitude images, which are part of the SWI calculation. This difference was not observed in the phase mask data.
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Affiliation(s)
- Alina Lopatina
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Jena, Germany.,Michael-Stifel-Center for Data-Driven and Simulation Science Jena, Jena, Germany
| | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Renat Sibgatulin
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Jena, Germany
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Jena, Germany.,Michael-Stifel-Center for Data-Driven and Simulation Science Jena, Jena, Germany.,Center of Medical Optics and Photonics Jena, Jena, Germany
| | - Daniel Güllmar
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Jena, Germany
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Burgos N, Bottani S, Faouzi J, Thibeau-Sutre E, Colliot O. Deep learning for brain disorders: from data processing to disease treatment. Brief Bioinform 2020; 22:1560-1576. [PMID: 33316030 DOI: 10.1093/bib/bbaa310] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 10/09/2020] [Accepted: 10/13/2020] [Indexed: 12/19/2022] Open
Abstract
In order to reach precision medicine and improve patients' quality of life, machine learning is increasingly used in medicine. Brain disorders are often complex and heterogeneous, and several modalities such as demographic, clinical, imaging, genetics and environmental data have been studied to improve their understanding. Deep learning, a subpart of machine learning, provides complex algorithms that can learn from such various data. It has become state of the art in numerous fields, including computer vision and natural language processing, and is also growingly applied in medicine. In this article, we review the use of deep learning for brain disorders. More specifically, we identify the main applications, the concerned disorders and the types of architectures and data used. Finally, we provide guidelines to bridge the gap between research studies and clinical routine.
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Segato A, Marzullo A, Calimeri F, De Momi E. Artificial intelligence for brain diseases: A systematic review. APL Bioeng 2020; 4:041503. [PMID: 33094213 PMCID: PMC7556883 DOI: 10.1063/5.0011697] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 09/09/2020] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.
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Affiliation(s)
- Alice Segato
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
| | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Francesco Calimeri
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
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Duong MT, Rauschecker AM, Mohan S. Diverse Applications of Artificial Intelligence in Neuroradiology. Neuroimaging Clin N Am 2020; 30:505-516. [PMID: 33039000 DOI: 10.1016/j.nic.2020.07.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Recent advances in artificial intelligence (AI) and deep learning (DL) hold promise to augment neuroimaging diagnosis for patients with brain tumors and stroke. Here, the authors review the diverse landscape of emerging neuroimaging applications of AI, including workflow optimization, lesion segmentation, and precision education. Given the many modalities used in diagnosing neurologic diseases, AI may be deployed to integrate across modalities (MR imaging, computed tomography, PET, electroencephalography, clinical and laboratory findings), facilitate crosstalk among specialists, and potentially improve diagnosis in patients with trauma, multiple sclerosis, epilepsy, and neurodegeneration. Together, there are myriad applications of AI for neuroradiology."
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Affiliation(s)
- Michael Tran Duong
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce Street, 219 Dulles Building, Philadelphia, PA 19104, USA. https://twitter.com/MichaelDuongMD
| | - Andreas M Rauschecker
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, 513 Parnassus Avenue, Room S-261, San Francisco, CA 94143, USA. https://twitter.com/DrDreMDPhD
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce Street, 219 Dulles Building, Philadelphia, PA 19104, USA.
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Inferring Multiple Sclerosis Stages from the Blood Transcriptome via Machine Learning. CELL REPORTS MEDICINE 2020; 1:100053. [PMID: 33205062 PMCID: PMC7659538 DOI: 10.1016/j.xcrm.2020.100053] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 05/05/2020] [Accepted: 06/22/2020] [Indexed: 12/01/2022]
Abstract
Peripheral blood mononuclear cells (PBMCs) bear specific dysregulations in genes and pathways at distinct stages of multiple sclerosis (MS) that may help with classifying MS and non-MS subjects, specifying the early stage of disease, or discriminating among MS courses. Here we describe an unbiased machine learning workflow to build MS stage-specific classifiers based on PBMC transcriptomics profiles from more than 300 individuals, including healthy subjects and patients with clinically isolated syndromes, relapsing-remitting MS, primary or secondary progressive MS, or other neurological disorders. The pipeline, designed to optimize and compare the performance of distinct machine learning algorithms in the training cohort, generates predictive models not influenced by demographic features, such as age and gender, and displays high accuracy in the independent validation cohort. Proper application of machine learning to transcriptional profiles of circulating blood cells may allow identification of disease state and stage in MS. Generated PBMC transcriptomes from multiple sclerosis and control subjects Unbiased machine learning workflow allows algorithm comparison and optimization Classifiers built on training cohort have high accuracy in the independent test set PBMC transcriptomes identify disease state and stage in multiple sclerosis
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Mousavi SR, Tahmasebivand M, Khorrami M, Ayromlou H, Khalili SK, Khorvash F, Rikhtegar R, Khademi B, Bahmanpour Z, Emamalizadeh B. Connection of miR-185 and miR-320a expression levels with response to interferon-beta in multiple sclerosis patients. Mult Scler Relat Disord 2020; 44:102264. [PMID: 32554287 DOI: 10.1016/j.msard.2020.102264] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 05/27/2020] [Accepted: 06/03/2020] [Indexed: 12/15/2022]
Abstract
BACKGROUND Multiple sclerosis (MS) is an inflammatory autoimmune disease characterized by neurodegeneration in the CNS. Interferon-beta (IFN-β) is an FDA-approved drug used as the first-line treatment for relapse-remitting multiple sclerosis (RRMS). The exact mechanism of IFN-β during the treatment of RRMS still remains unknown. Recently, many studies have shifted towards the role of miRNAs in the treatment of MS patients. METHODS Herein, the expression level of miR-185-5p and miR-320a has been evaluated in order to candidate them as novel biomarkers for monitoring the response to IFN-β therapy. For this purpose, one-hundred whole blood samples from patients with RRMS were collected, consisting of 50 responders and 50 non-responders to IFN-β therapy. To predict the possible molecular mechanisms of IFN-β and highlight the role of these miRNAs, in silico analysis was applied to enrich the signaling pathways which may be involved based on the target genes of miR-185-5p and miR-320a. RESULTS It is identified that the differentially expressed miR-185-5p was statistically significant between the two treated groups with IFN-β. Furthermore, MAPK signaling pathway was suggested as the main non-canonical pathway involved in IFN-β therapy. CONCLUSION miR-185-5p could be considered as a novel biomarker for monitoring the response to IFN-β therapy.
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Affiliation(s)
- Seyyed Reza Mousavi
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mahsa Tahmasebivand
- Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mehdi Khorrami
- Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hormoz Ayromlou
- Neurology Department, Imam Reza Hospital, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Soodabeh Khalil Khalili
- Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Fariborz Khorvash
- Neurology Department, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Reza Rikhtegar
- Aging Research Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Bahareh Khademi
- Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Zahra Bahmanpour
- Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Babak Emamalizadeh
- Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
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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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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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