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Farabi Maleki S, Yousefi M, Afshar S, Pedrammehr S, Lim CP, Jafarizadeh A, Asadi H. Artificial Intelligence for Multiple Sclerosis Management Using Retinal Images: Pearl, Peaks, and Pitfalls. Semin Ophthalmol 2024; 39:271-288. [PMID: 38088176 DOI: 10.1080/08820538.2023.2293030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 11/23/2023] [Indexed: 03/28/2024]
Abstract
Multiple sclerosis (MS) is a complex autoimmune disease characterized by inflammatory processes, demyelination, neurodegeneration, and axonal damage within the central nervous system (CNS). Retinal imaging, particularly Optical coherence tomography (OCT), has emerged as a crucial tool for investigating MS-related retinal injury. The integration of artificial intelligence(AI) has shown promise in enhancing OCT analysis for MS. Researchers are actively utilizing AI algorithms to accurately detect and classify MS-related abnormalities, leading to improved efficiency in diagnosis, monitoring, and personalized treatment planning. The prognostic value of AI in predicting MS disease progression has garnered substantial attention. Machine learning (ML) and deep learning (DL) algorithms can analyze longitudinal OCT data to forecast the course of the disease, providing critical information for personalized treatment planning and improved patient outcomes. Early detection of high-risk patients allows for targeted interventions to mitigate disability progression effectively. As such, AI-driven approaches yielded remarkable abilities in classifying distinct MS subtypes based on retinal features, aiding in disease characterization and guiding tailored therapeutic strategies. Additionally, these algorithms have enhanced the accuracy and efficiency of OCT image segmentation, streamlined diagnostic processes, and reduced human error. This study reviews the current research studies on the integration of AI,including ML and DL algorithms, with OCT in the context of MS. It examines the advancements, challenges, potential prospects, and ethical concerns of AI-powered techniques in enhancing MS diagnosis, monitoring disease progression, revolutionizing patient care, the development of patient screening tools, and supported clinical decision-making based on OCT images.
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Affiliation(s)
| | - Milad Yousefi
- Faculty of Mathematics, Statistics and Computer Sciences, University of Tabriz, Tabriz, Iran
| | - Sayeh Afshar
- Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | - Chee Peng Lim
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Burwood, Australia
| | - Ali Jafarizadeh
- Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Houshyar Asadi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Burwood, Australia
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Khodabandeh Z, Rabbani H, Ashtari F, Zimmermann HG, Motamedi S, Brandt AU, Paul F, Kafieh R. Discrimination of multiple sclerosis using OCT images from two different centers. Mult Scler Relat Disord 2023; 77:104846. [PMID: 37413855 DOI: 10.1016/j.msard.2023.104846] [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: 05/22/2023] [Accepted: 06/19/2023] [Indexed: 07/08/2023]
Abstract
BACKGROUND Multiple sclerosis (MS) is one of the most prevalent chronic inflammatory diseases caused by demyelination and axonal damage in the central nervous system. Structural retinal imaging via optical coherence tomography (OCT) shows promise as a noninvasive biomarker for monitoring of MS. There are successful reports regarding the application of Artificial Intelligence (AI) in the analysis of cross-sectional OCTs in ophthalmologic diseases. However, the alteration of thicknesses of various retinal layers in MS is noticeably subtle compared to other ophthalmologic diseases. Therefore, raw cross-sectional OCTs are replaced with multilayer segmented OCTs for discrimination of MS and healthy controls (HCs). METHODS To conform to the principles of trustworthy AI, interpretability is provided by visualizing the regional layer contribution to classification performance with the proposed occlusion sensitivity approach. The robustness of the classification is also guaranteed by showing the effectiveness of the algorithm while being tested on the new independent dataset. The most discriminative features from different topologies of the multilayer segmented OCTs are selected by the dimension reduction method. Support vector machine (SVM), random forest (RF), and artificial neural network (ANN) are used for classification. Patient-wise cross-validation (CV) is utilized to evaluate the performance of the algorithm, where the training and test folds contain records from different subjects. RESULTS The most discriminative topology is determined to square with a size of 40 pixels and the most influential layers are the ganglion cell and inner plexiform layer (GCIPL) and inner nuclear layer (INL). Linear SVM resulted in 88% Accuracy (with standard deviation (std) = 0.49 in 10 times of execution to indicate the repeatability), 78% precision (std=1.48), and 63% recall (std=1.35) in the discrimination of MS and HCs using macular multilayer segmented OCTs. CONCLUSION The proposed classification algorithm is expected to help neurologists in the early diagnosis of MS. This paper distinguishes itself from other studies by employing two distinct datasets, which enhances the robustness of its findings in comparison with previous studies with lack of external validation. This study aims to circumvent the utilization of deep learning methods due to the limited quantity of the available data and convincingly demonstrates that favorable outcomes can be achieved without relying on deep learning techniques.
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Affiliation(s)
- Zahra Khodabandeh
- School of Advanced Technologies in Medicine, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hossein Rabbani
- School of Advanced Technologies in Medicine, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Fereshteh Ashtari
- Isfahan Neurosciences Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hanna G Zimmermann
- Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine and Charité- Universitätsmedizin Berlin, Berlin, Germany; NeuroCure Clinical Research Center- Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Seyedamirhosein Motamedi
- Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine and Charité- Universitätsmedizin Berlin, Berlin, Germany; NeuroCure Clinical Research Center- Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Alexander U Brandt
- Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine and Charité- Universitätsmedizin Berlin, Berlin, Germany; NeuroCure Clinical Research Center- Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany; Department of Neurology, University of California, Irvine, CA, USA
| | - Friedemann Paul
- Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine and Charité- Universitätsmedizin Berlin, Berlin, Germany; NeuroCure Clinical Research Center- Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany; Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Rahele Kafieh
- School of Advanced Technologies in Medicine, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran; NeuroCure Clinical Research Center- Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany; Department of Engineering, Durham University, Durham, UK.
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Hernandez M, Ramon-Julvez U, Vilades E, Cordon B, Mayordomo E, Garcia-Martin E. Explainable artificial intelligence toward usable and trustworthy computer-aided diagnosis of multiple sclerosis from Optical Coherence Tomography. PLoS One 2023; 18:e0289495. [PMID: 37549174 PMCID: PMC10406231 DOI: 10.1371/journal.pone.0289495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 07/19/2023] [Indexed: 08/09/2023] Open
Abstract
BACKGROUND Several studies indicate that the anterior visual pathway provides information about the dynamics of axonal degeneration in Multiple Sclerosis (MS). Current research in the field is focused on the quest for the most discriminative features among patients and controls and the development of machine learning models that yield computer-aided solutions widely usable in clinical practice. However, most studies are conducted with small samples and the models are used as black boxes. Clinicians should not trust machine learning decisions unless they come with comprehensive and easily understandable explanations. MATERIALS AND METHODS A total of 216 eyes from 111 healthy controls and 100 eyes from 59 patients with relapsing-remitting MS were enrolled. The feature set was obtained from the thickness of the ganglion cell layer (GCL) and the retinal nerve fiber layer (RNFL). Measurements were acquired by the novel Posterior Pole protocol from Spectralis Optical Coherence Tomography (OCT) device. We compared two black-box methods (gradient boosting and random forests) with a glass-box method (explainable boosting machine). Explainability was studied using SHAP for the black-box methods and the scores of the glass-box method. RESULTS The best-performing models were obtained for the GCL layer. Explainability pointed out to the temporal location of the GCL layer that is usually broken or thinning in MS and the relationship between low thickness values and high probability of MS, which is coherent with clinical knowledge. CONCLUSIONS The insights on how to use explainability shown in this work represent a first important step toward a trustworthy computer-aided solution for the diagnosis of MS with OCT.
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Affiliation(s)
- Monica Hernandez
- Computer Science Department, University of Zaragoza, Zaragoza, Spain
- Aragon Institute on Engineering Research, Zaragoza, Spain
| | - Ubaldo Ramon-Julvez
- Computer Science Department, University of Zaragoza, Zaragoza, Spain
- Aragon Institute on Engineering Research, Zaragoza, Spain
| | - Elisa Vilades
- Ophtalmology Department, Miguel Servet Hospital, Zaragoza, Spain
| | - Beatriz Cordon
- Ophtalmology Department, Miguel Servet Hospital, Zaragoza, Spain
| | - Elvira Mayordomo
- Computer Science Department, University of Zaragoza, Zaragoza, Spain
- Aragon Institute on Engineering Research, Zaragoza, Spain
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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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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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Betzler BK, Rim TH, Sabanayagam C, Cheng CY. Artificial Intelligence in Predicting Systemic Parameters and Diseases From Ophthalmic Imaging. Front Digit Health 2022; 4:889445. [PMID: 35706971 PMCID: PMC9190759 DOI: 10.3389/fdgth.2022.889445] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/06/2022] [Indexed: 12/14/2022] Open
Abstract
Artificial Intelligence (AI) analytics has been used to predict, classify, and aid clinical management of multiple eye diseases. Its robust performances have prompted researchers to expand the use of AI into predicting systemic, non-ocular diseases and parameters based on ocular images. Herein, we discuss the reasons why the eye is well-suited for systemic applications, and review the applications of deep learning on ophthalmic images in the prediction of demographic parameters, body composition factors, and diseases of the cardiovascular, hematological, neurodegenerative, metabolic, renal, and hepatobiliary systems. Three main imaging modalities are included—retinal fundus photographs, optical coherence tomographs and external ophthalmic images. We examine the range of systemic factors studied from ophthalmic imaging in current literature and discuss areas of future research, while acknowledging current limitations of AI systems based on ophthalmic images.
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Affiliation(s)
- Bjorn Kaijun Betzler
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
| | - Tyler Hyungtaek Rim
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Charumathi Sabanayagam
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
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A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders. BIOLOGY 2022; 11:biology11030469. [PMID: 35336842 PMCID: PMC8945195 DOI: 10.3390/biology11030469] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/12/2022] [Accepted: 03/14/2022] [Indexed: 01/19/2023]
Abstract
Simple Summary This study represents a resourceful review article that can deliver resources on neurological diseases and their implemented classification algorithms to reveal the future direction of researchers. Researchers interested in studying neurological diseases and previously implemented techniques in this field can follow this article. Various challenges occur in detecting different stages of the disorders. A limited amount of labeled and unlabeled datasets and other limitations is represented in this article to assist them in finding out the directions. The authors’ purpose for composing this article is to make a straightforward and concrete path for researchers to quickly find the way and the scope in this field for implementing future research on neurological disease detection. Abstract Neurological disorders (NDs) are becoming more common, posing a concern to pregnant women, parents, healthy infants, and children. Neurological disorders arise in a wide variety of forms, each with its own set of origins, complications, and results. In recent years, the intricacy of brain functionalities has received a better understanding due to neuroimaging modalities, such as magnetic resonance imaging (MRI), magnetoencephalography (MEG), and positron emission tomography (PET), etc. With high-performance computational tools and various machine learning (ML) and deep learning (DL) methods, these modalities have discovered exciting possibilities for identifying and diagnosing neurological disorders. This study follows a computer-aided diagnosis methodology, leading to an overview of pre-processing and feature extraction techniques. The performance of existing ML and DL approaches for detecting NDs is critically reviewed and compared in this article. A comprehensive portion of this study also shows various modalities and disease-specified datasets that detect and records images, signals, and speeches, etc. Limited related works are also summarized on NDs, as this domain has significantly fewer works focused on disease and detection criteria. Some of the standard evaluation metrics are also presented in this study for better result analysis and comparison. This research has also been outlined in a consistent workflow. At the conclusion, a mandatory discussion section has been included to elaborate on open research challenges and directions for future work in this emerging field.
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Comparison of Machine Learning Methods Using Spectralis OCT for Diagnosis and Disability Progression Prognosis in Multiple Sclerosis. Ann Biomed Eng 2022; 50:507-528. [PMID: 35220529 PMCID: PMC9001622 DOI: 10.1007/s10439-022-02930-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 02/10/2022] [Indexed: 12/28/2022]
Abstract
Machine learning approaches in diagnosis and prognosis of multiple sclerosis (MS) were analysed using retinal nerve fiber layer (RNFL) thickness, measured by optical coherence tomography (OCT). A cross-sectional study (72 MS patients and 30 healthy controls) was used for diagnosis. These 72 MS patients were involved in a 10-year longitudinal follow-up study for prognostic purposes. Structural measurements of RNFL thickness were performed using different Spectralis OCT protocols: fast macular thickness protocol to measure macular RNFL, and fast RNFL thickness protocol and fast RNFL-N thickness protocol to measure peripapillary RNFL. Binary classifiers such as multiple linear regression (MLR), support vector machines (SVM), decision tree (DT), k-nearest neighbours (k-NN), Naïve Bayes (NB), ensemble classifier (EC) and long short-term memory (LSTM) recurrent neural network were tested. For MS diagnosis, the best acquisition protocol was fast macular thickness protocol using k-NN (accuracy: 95.8%; sensitivity: 94.4%; specificity: 97.2%; precision: 97.1%; AUC: 0.958). For MS prognosis, our model with a 3-year follow up to predict disability progression 8 years later was the best predictive model. DT performed best for fast macular thickness protocol (accuracy: 91.3%; sensitivity: 90.0%; specificity: 92.5%; precision: 92.3%; AUC: 0.913) and SVM for fast RNFL-N thickness protocol (accuracy: 91.3%; sensitivity: 87.5%; specificity: 95.0%; precision: 94.6%; AUC: 0.913). This work concludes that measurements of RNFL thickness obtained with Spectralis OCT have a good ability to diagnose MS and to predict disability progression in MS patients. This machine learning approach would help clinicians to have valuable information.
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Nabizadeh F, Masrouri S, Ramezannezhad E, Ghaderi A, Sharafi AM, Soraneh S, Moghadasi AN. Artificial intelligence in the diagnosis of Multiple Sclerosis: a systematic review. Mult Scler Relat Disord 2022; 59:103673. [DOI: 10.1016/j.msard.2022.103673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 01/24/2022] [Accepted: 02/05/2022] [Indexed: 10/19/2022]
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Duwairi RM, Al-Zboon SA, Al-Dwairi RA, Obaidi A. A Deep Learning Model and a Dataset for Diagnosing Ophthalmology Diseases. JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2021. [DOI: 10.1142/s0219649221500362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The rapid development of artificial neural network techniques, especially convolutional neural networks, encouraged the researchers to adapt such techniques in the medical domain. Specifically, to provide assist tools to help the professionals in patients’ diagnosis. The main problem faced by the researchers in the medical domain is the lack of available annotated datasets which can be used to train and evaluate large and complex deep neural networks. In this paper, to assist researchers who are interested in applying deep learning techniques to aid the ophthalmologists in diagnosing eye-related diseases, we provide an optical coherence tomography dataset with collaboration with ophthalmologists from the King Abdullah University Hospital, Irbid, Jordan. This dataset consists of 21,991 OCT images distributed over seven eye diseases in addition to normal images (no disease), namely, Choroidal Neovascularisation, Full Macular Hole (Full Thickness), Partial Macular Hole, Central Serous Retinopathy, Geographic atrophy, Macular Retinal Oedema, and Vitreomacular Traction. To the best of our knowledge, this dataset is the largest of its kind, where images belong to actual patients from Jordan and the annotation was carried out by ophthalmologists. Two classification tasks were applied to this dataset; a binary classification to distinguish between images which belong to healthy eyes (normal) and images which belong to diseased eyes (abnormal). The second classification task is a multi-class classification, where the deep neural network is trained to distinguish between the seven diseases listed above in addition to the normal case. In both classification tasks, the U-Net neural network was modified and subsequently utilised. This modification adds an additional block of layers to the original U-Net model to become capable of handling classification as the original network is used for image segmentation. The results of the binary classification were equal to 84.90% and 69.50% as accuracy and quadratic weighted kappa, respectively. The results of the multi-class classification, by contrast, were equal to 63.68% and 66.06% as accuracy and quadratic weighted kappa, respectively.
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Affiliation(s)
- Rehab M. Duwairi
- Department of Computer Information Systems, Jordan University of Science and Technology, Irbid, Jordan
| | - Saad A. Al-Zboon
- Department of Computer Science, Jordan University of Science and Technology, Irbid, Jordan
| | - Rami A. Al-Dwairi
- Division of Ophthalmology, Department of Special Surgery, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Ahmad Obaidi
- King Abdullah University Hospital, Irbid, Jordan
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Montolío A, Martín-Gallego A, Cegoñino J, Orduna E, Vilades E, Garcia-Martin E, Palomar APD. Machine learning in diagnosis and disability prediction of multiple sclerosis using optical coherence tomography. Comput Biol Med 2021; 133:104416. [PMID: 33946022 DOI: 10.1016/j.compbiomed.2021.104416] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 03/25/2021] [Accepted: 04/16/2021] [Indexed: 01/02/2023]
Abstract
BACKGROUND Multiple sclerosis (MS) is a neurodegenerative disease that affects the central nervous system, especially the brain, spinal cord, and optic nerve. Diagnosis of this disease is a very complex process and generally requires a lot of time. In addition, treatments are applied without any information on the disability course in each MS patient. For these two reasons, the objective of this study was to improve the MS diagnosis and predict the long-term course of disability in MS patients based on clinical data and retinal nerve fiber layer (RNFL) thickness, measured by optical coherence tomography (OCT). MATERIAL AND METHODS A total of 104 healthy controls and 108 MS patients, 82 of whom had a 10-year follow-up, were enrolled. Classification algorithms such as multiple linear regression (MLR), support vector machines (SVM), decision tree (DT), k-nearest neighbours (k-NN), Naïve Bayes (NB), ensemble classifier (EC) and long short-term memory (LSTM) recurrent neural network were tested to develop two predictive models: MS diagnosis model and MS disability course prediction model. RESULTS For MS diagnosis, the best result was obtained using EC (accuracy: 87.7%; sensitivity: 87.0%; specificity: 88.5%; precision: 88.7%; AUC: 0.8775). In line with this good performance, the accuracy was 85.4% using k-NN and 84.4% using SVM. And, for long-term prediction of MS disability course, LSTM recurrent neural network was the most appropriate classifier (accuracy: 81.7%; sensitivity: 81.1%; specificity: 82.2%; precision: 78.9%; AUC: 0.8165). The use of MLR, SVM and k-NN also showed a good performance (AUC ≥ 0.8). CONCLUSIONS This study demonstrated that machine learning techniques, using clinical and OCT data, can help establish an early diagnosis and predict the course of MS. This advance could help clinicians select more specific treatments for each MS patient. Therefore, our findings underscore the potential of RNFL thickness as a reliable MS biomarker.
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Affiliation(s)
- Alberto Montolío
- Group of Biomaterials, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain; Department of Mechanical Engineering, University of Zaragoza, Zaragoza, Spain
| | - Alejandro Martín-Gallego
- Group of Biomaterials, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain; Department of Mechanical Engineering, University of Zaragoza, Zaragoza, Spain
| | - José Cegoñino
- Group of Biomaterials, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain; Department of Mechanical Engineering, University of Zaragoza, Zaragoza, Spain
| | - Elvira Orduna
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain; GIMSO Research and Innovative Group, Aragon Institute for Health Research (IIS Aragon), Zaragoza, Spain
| | - Elisa Vilades
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain; GIMSO Research and Innovative Group, Aragon Institute for Health Research (IIS Aragon), Zaragoza, Spain
| | - Elena Garcia-Martin
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain; GIMSO Research and Innovative Group, Aragon Institute for Health Research (IIS Aragon), Zaragoza, Spain
| | - Amaya Pérez Del Palomar
- Group of Biomaterials, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain; Department of Mechanical Engineering, University of Zaragoza, Zaragoza, Spain.
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Hartmann M, Fenton N, Dobson R. Current review and next steps for artificial intelligence in multiple sclerosis risk research. Comput Biol Med 2021; 132:104337. [PMID: 33773193 DOI: 10.1016/j.compbiomed.2021.104337] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 03/09/2021] [Accepted: 03/10/2021] [Indexed: 12/30/2022]
Abstract
In the last few decades, the prevalence of multiple sclerosis (MS), a chronic inflammatory disease of the nervous system, has increased, particularly in Northern European countries, the United States, and United Kingdom. The promise of artificial intelligence (AI) and machine learning (ML) as tools to address problems in MS research has attracted increasing interest in these methods. Bayesian networks offer a clear advantage since they can integrate data and causal knowledge allowing for visualizing interactions between dependent variables and potential confounding factors. A review of AI/ML research methods applied to MS found 216 papers using terms "Multiple Sclerosis", "machine learning", "artificial intelligence", "Bayes", and "Bayesian", of which 90 were relevant and recently published. More than half of these involve the detection and segmentation of MS lesions for quantitative analysis; however clinical and lifestyle risk factor assessment and prediction have largely been ignored. Of those that address risk factors, most provide only association studies for some factors and often fail to include the potential impact of confounding factors and bias (especially where these have causal explanations) that could affect data interpretation, such as reporting quality and medical care access in various countries. To address these gaps in the literature, we propose a causal Bayesian network approach to assessing risk factors for MS, which can address deficiencies in current epidemiological methods of producing risk measurements and makes better use of observational data.
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Affiliation(s)
- Morghan Hartmann
- Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK.
| | - Norman Fenton
- Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK
| | - Ruth Dobson
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, E1 4NS, UK
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Cordon B, Vilades E, Orduna E, Satue M, Perez-Velilla J, Sebastian B, Polo V, Larrosa JM, Pablo LE, Garcia-Martin E. Angiography with optical coherence tomography as a biomarker in multiple sclerosis. PLoS One 2020; 15:e0243236. [PMID: 33290417 PMCID: PMC7723290 DOI: 10.1371/journal.pone.0243236] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 11/18/2020] [Indexed: 11/18/2022] Open
Abstract
PURPOSE To investigate superficial retinal microvascular plexuses detected by optical coherence tomography angiography (OCT-A) in multiple sclerosis (MS) subjects and compare them with healthy controls. METHODS A total of 92 eyes from 92 patients with relapsing-remitting MS and 149 control eyes were included in this prospective observational study. OCT-A imaging was performed using Triton Swept-Source OCT (Topcon Corporation, Japan). The vessel density (VD) percentage in the superficial retinal plexus and optic disc area (6 x 6 mm grid) was measured and compared between groups. RESULTS MS patients showed a significant decrease VD in the superior (p = 0.005), nasal (p = 0.029) and inferior (p = 0.040) parafoveal retina compared with healthy subjects. Patients with disease durations of more than 5 years presented lower VD in the superior (p = 0.002), nasal (p = 0.017) and inferior (p = 0.022) parafoveal areas compared with healthy subjects. Patients with past optic neuritis episodes did not show retinal microvasculature alterations, but patients with an EDSS score of less than 3 showed a significant decrease in nasal (p = 0.024) and superior (p = 0.006) perifoveal VD when compared with healthy subjects. CONCLUSIONS MS produces a decrease in retinal vascularization density in the superficial plexus of the parafoveal retina. Alterations in retinal vascularization observed in MS patients are independent of the presence of optic nerve inflammation. OCT-A has the ability to detect subclinical vascular changes and is a potential biomarker for diagnosing the presence and progression of MS.
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Affiliation(s)
- Beatriz Cordon
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), University of Zaragoza, Zaragoza, Spain
- * E-mail:
| | - Elisa Vilades
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), University of Zaragoza, Zaragoza, Spain
| | - Elvira Orduna
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), University of Zaragoza, Zaragoza, Spain
| | - María Satue
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), University of Zaragoza, Zaragoza, Spain
| | - Javier Perez-Velilla
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), University of Zaragoza, Zaragoza, Spain
| | - Berta Sebastian
- National Ocular Pathology Network (OFTARED) at the Carlos III Institute of Health, Madrid, Spain
| | - Vicente Polo
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), University of Zaragoza, Zaragoza, Spain
- Neurology Department, Miguel Servet University Hospital, Zaragoza, Spain
| | - Jose Manuel Larrosa
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), University of Zaragoza, Zaragoza, Spain
- Neurology Department, Miguel Servet University Hospital, Zaragoza, Spain
| | - Luis Emilio Pablo
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), University of Zaragoza, Zaragoza, Spain
- Neurology Department, Miguel Servet University Hospital, Zaragoza, Spain
| | - Elena Garcia-Martin
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain
- Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), University of Zaragoza, Zaragoza, Spain
- Neurology Department, Miguel Servet University Hospital, Zaragoza, Spain
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Emil Tampu I, Maintz M, Koller D, Johansson K, Gimm O, Capitanio A, Eklund A, Haj-Hosseini N. Optical coherence tomography for thyroid pathology: 3D analysis of tissue microstructure. BIOMEDICAL OPTICS EXPRESS 2020; 11:4130-4149. [PMID: 32923033 PMCID: PMC7449746 DOI: 10.1364/boe.394296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 06/20/2020] [Accepted: 06/22/2020] [Indexed: 06/11/2023]
Abstract
To investigate the potential of optical coherence tomography (OCT) to distinguish between normal and pathologic thyroid tissue, 3D OCT images were acquired on ex vivo thyroid samples from adult subjects (n=22) diagnosed with a variety of pathologies. The follicular structure was analyzed in terms of count, size, density and sphericity. Results showed that OCT images highly agreed with the corresponding histopatology and the calculated parameters were representative of the follicular structure variation. The analysis of OCT volumes provides quantitative information that could make automatic classification possible. Thus, OCT can be beneficial for intraoperative surgical guidance or in the pathology assessment routine.
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Affiliation(s)
- Iulian Emil Tampu
- Department of Biomedical Engineering, Linköping University, Linköping 581 85, Sweden
| | - Michaela Maintz
- Department of Biomedical Engineering, Linköping University, Linköping 581 85, Sweden
| | - Daniela Koller
- Department of Biomedical Engineering, Linköping University, Linköping 581 85, Sweden
| | - Kenth Johansson
- Department of Surgery, Västervik Hospital and Örebro University Hospital, Västervik and Örebro, Sweden
| | - Oliver Gimm
- Department of Surgery, and Department of Biomedical and Clinical Sciences, Linköping University Hospital and Linköping University, Linköping 581 85, Sweden
| | - Arrigo Capitanio
- Department of Clinical Pathology, and Department of Biomedical and Clinical Sciences, Linköping University, Linköping 581 85, Sweden
| | - Anders Eklund
- Department of Biomedical Engineering, Linköping University, Linköping 581 85, Sweden
- Division of Statistics & Machine Learning, Department of Computer and Information Science, Linköping University, Linköping 581 83, Sweden
- Center for Medical Image Science and Visualization, Linköping University, Linköping 581 85, Sweden
| | - Neda Haj-Hosseini
- Department of Biomedical Engineering, Linköping University, Linköping 581 85, Sweden
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Application of Artificial Neural Network for Prediction of Risk of Multiple Sclerosis Based on Single Nucleotide Polymorphism Genotypes. J Mol Neurosci 2020; 70:1081-1087. [DOI: 10.1007/s12031-020-01514-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 02/19/2020] [Indexed: 12/13/2022]
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Cavaliere C, Vilades E, Alonso-Rodríguez MC, Rodrigo MJ, Pablo LE, Miguel JM, López-Guillén E, Morla EMS, Boquete L, Garcia-Martin E. Computer-Aided Diagnosis of Multiple Sclerosis Using a Support Vector Machine and Optical Coherence Tomography Features. SENSORS 2019; 19:s19235323. [PMID: 31816925 PMCID: PMC6928765 DOI: 10.3390/s19235323] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 11/27/2019] [Accepted: 11/30/2019] [Indexed: 12/16/2022]
Abstract
The purpose of this paper is to evaluate the feasibility of diagnosing multiple sclerosis (MS) using optical coherence tomography (OCT) data and a support vector machine (SVM) as an automatic classifier. Forty-eight MS patients without symptoms of optic neuritis and forty-eight healthy control subjects were selected. Swept-source optical coherence tomography (SS-OCT) was performed using a DRI (deep-range imaging) Triton OCT device (Topcon Corp., Tokyo, Japan). Mean values (right and left eye) for macular thickness (retinal and choroidal layers) and peripapillary area (retinal nerve fibre layer, retinal, ganglion cell layer—GCL, and choroidal layers) were compared between both groups. Based on the analysis of the area under the receiver operator characteristic curve (AUC), the 3 variables with the greatest discriminant capacity were selected to form the feature vector. A SVM was used as an automatic classifier, obtaining the confusion matrix using leave-one-out cross-validation. Classification performance was assessed with Matthew’s correlation coefficient (MCC) and the AUCCLASSIFIER. The most discriminant variables were found to be the total GCL++ thickness (between inner limiting membrane to inner nuclear layer boundaries), evaluated in the peripapillary area and macular retina thickness in the nasal quadrant of the outer and inner rings. Using the SVM classifier, we obtained the following values: MCC = 0.81, sensitivity = 0.89, specificity = 0.92, accuracy = 0.91, and AUCCLASSIFIER = 0.97. Our findings suggest that it is possible to classify control subjects and MS patients without previous optic neuritis by applying machine-learning techniques to study the structural neurodegeneration in the retina.
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Affiliation(s)
- Carlo Cavaliere
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28801 Alcalá de Henares, Spain; (C.C.); (J.M.M.); (E.L.-G.)
| | - Elisa Vilades
- Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain; (E.V.); (L.E.P.)
- Aragon Institute for Health Research (IIS Aragon), Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), University of Zaragoza, 50009 Zaragoza, Spain
| | - Mª C. Alonso-Rodríguez
- Department of Physics and Mathematics, University of Alcalá, 28801 Alcalá de Henares, Spain;
| | - María Jesús Rodrigo
- Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain; (E.V.); (L.E.P.)
- Aragon Institute for Health Research (IIS Aragon), Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), University of Zaragoza, 50009 Zaragoza, Spain
- RETICS-Oftared: Thematic Networks for Co-operative Research in Health for Ocular Diseases, 28040 Madrid, Spain
- Correspondence: (M.J.R.); (L.B.); (E.G.-M.); Tel.: +34-976765558 (E.G.-M.); Fax: +34-97656623 (E.G.-M.)
| | - Luis Emilio Pablo
- Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain; (E.V.); (L.E.P.)
- Aragon Institute for Health Research (IIS Aragon), Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), University of Zaragoza, 50009 Zaragoza, Spain
| | - Juan Manuel Miguel
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28801 Alcalá de Henares, Spain; (C.C.); (J.M.M.); (E.L.-G.)
| | - Elena López-Guillén
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28801 Alcalá de Henares, Spain; (C.C.); (J.M.M.); (E.L.-G.)
| | - Eva Mª Sánchez Morla
- Department of Psychiatry, 12 Octubre University Hospital Research Institute (i+12), 28041 Madrid, Spain;
- Faculty of Medicine, Complutense University of Madrid, 28040 Madrid, Spain
- CIBERSAM: Biomedical Research Networking Centre in Mental Health, 28029 Madrid, Spain
| | - Luciano Boquete
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28801 Alcalá de Henares, Spain; (C.C.); (J.M.M.); (E.L.-G.)
- RETICS-Oftared: Thematic Networks for Co-operative Research in Health for Ocular Diseases, 28040 Madrid, Spain
- Correspondence: (M.J.R.); (L.B.); (E.G.-M.); Tel.: +34-976765558 (E.G.-M.); Fax: +34-97656623 (E.G.-M.)
| | - Elena Garcia-Martin
- Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain; (E.V.); (L.E.P.)
- Aragon Institute for Health Research (IIS Aragon), Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), University of Zaragoza, 50009 Zaragoza, Spain
- RETICS-Oftared: Thematic Networks for Co-operative Research in Health for Ocular Diseases, 28040 Madrid, Spain
- Correspondence: (M.J.R.); (L.B.); (E.G.-M.); Tel.: +34-976765558 (E.G.-M.); Fax: +34-97656623 (E.G.-M.)
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Pérez del Palomar A, Cegoñino J, Montolío A, Orduna E, Vilades E, Sebastián B, Pablo LE, Garcia-Martin E. Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques. PLoS One 2019; 14:e0216410. [PMID: 31059539 PMCID: PMC6502323 DOI: 10.1371/journal.pone.0216410] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2018] [Accepted: 04/21/2019] [Indexed: 11/18/2022] Open
Abstract
Objective To compare axonal loss in ganglion cells detected with swept-source optical coherence tomography (SS-OCT) in eyes of patients with multiple sclerosis (MS) versus healthy controls using different machine learning techniques. To analyze the capability of machine learning techniques to improve the detection of retinal nerve fiber layer (RNFL) and the complex Ganglion Cell Layer–Inner plexiform layer (GCL+) damage in patients with multiple sclerosis and to use the SS-OCT as a biomarker to early predict this disease. Methods Patients with relapsing-remitting MS (n = 80) and age-matched healthy controls (n = 180) were enrolled. Different protocols from the DRI SS-OCT Triton system were used to obtain the RNFL and GCL+ thicknesses in both eyes. Macular and peripapilar areas were analyzed to detect the zones with higher thickness decrease. The performance of different machine learning techniques (decision trees, multilayer perceptron and support vector machine) for identifying RNFL and GCL+ thickness loss in patients with MS were evaluated. Receiver-operating characteristic (ROC) curves were used to display the ability of the different tests to discriminate between MS and healthy eyes in our population. Results Machine learning techniques provided an excellent tool to predict MS disease using SS-OCT data. In particular, the decision trees obtained the best prediction (97.24%) using RNFL data in macular area and the area under the ROC curve was 0.995, while the wide protocol which covers an extended area between macula and papilla gave an accuracy of 95.3% with a ROC of 0.998. Moreover, it was obtained that the most significant area of the RNFL to predict MS is the macula just surrounding the fovea. On the other hand, in our study, GCL+ did not contribute to predict MS and the different machine learning techniques performed worse in this layer than in RNFL. Conclusions Measurements of RNFL thickness obtained with SS-OCT have an excellent ability to differentiate between healthy controls and patients with MS. Thus, the use of machine learning techniques based on these measures can be a reliable tool to help in MS diagnosis.
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Affiliation(s)
- Amaya Pérez del Palomar
- Group of Biomaterials, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain
- Department of Mechanical Engineering, University of Zaragoza, Zaragoza, Spain
- * E-mail:
| | - José Cegoñino
- Group of Biomaterials, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain
- Department of Mechanical Engineering, University of Zaragoza, Zaragoza, Spain
| | - Alberto Montolío
- Group of Biomaterials, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain
- Department of Mechanical Engineering, University of Zaragoza, Zaragoza, Spain
| | - Elvira Orduna
- Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain
- GIMSO Research and Innovative Group, Aragon Institute for Health Research (IIS Aragón), University of Zaragoza, Zaragoza, Spain
| | - Elisa Vilades
- Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain
- GIMSO Research and Innovative Group, Aragon Institute for Health Research (IIS Aragón), University of Zaragoza, Zaragoza, Spain
| | - Berta Sebastián
- Department of Neurology, Miguel Servet University Hospital, Zaragoza, Spain
| | - Luis E. Pablo
- Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain
- GIMSO Research and Innovative Group, Aragon Institute for Health Research (IIS Aragón), University of Zaragoza, Zaragoza, Spain
| | - Elena Garcia-Martin
- Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain
- GIMSO Research and Innovative Group, Aragon Institute for Health Research (IIS Aragón), University of Zaragoza, Zaragoza, Spain
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Retinal oximetry: Metabolic imaging for diseases of the retina and brain. Prog Retin Eye Res 2019; 70:1-22. [DOI: 10.1016/j.preteyeres.2019.04.001] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 03/27/2019] [Accepted: 04/10/2019] [Indexed: 12/20/2022]
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19
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de Santiago L, Sánchez Morla EM, Ortiz M, López E, Amo Usanos C, Alonso-Rodríguez MC, Barea R, Cavaliere-Ballesta C, Fernández A, Boquete L. A computer-aided diagnosis of multiple sclerosis based on mfVEP recordings. PLoS One 2019; 14:e0214662. [PMID: 30947273 PMCID: PMC6449069 DOI: 10.1371/journal.pone.0214662] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 03/18/2019] [Indexed: 01/07/2023] Open
Abstract
Introduction The aim of this study is to develop a computer-aided diagnosis system to identify subjects at differing stages of development of multiple sclerosis (MS) using multifocal visual-evoked potentials (mfVEPs). Using an automatic classifier, diagnosis is performed first on the eyes and then on the subjects. Patients MfVEP signals were obtained from patients with Radiologically Isolated Syndrome (RIS) (n = 30 eyes), patients with Clinically Isolated Syndrome (CIS) (n = 62 eyes), patients with definite MS (n = 56 eyes) and 22 control subjects (n = 44 eyes). The CIS and MS groups were divided into two subgroups: those with eyes affected by optic neuritis (ON) and those without (non-ON). Methods For individual eye diagnosis, a feature vector was formed with information about the intensity, latency and singular values of the mfVEP signals. A flat multiclass classifier (FMC) and a hierarchical classifier (HC) were tested and both were implemented using the k-Nearest Neighbour (k-NN) algorithm. The output of the best eye classifier was used to classify the subjects. In the event of divergence, the eye with the best mfVEP recording was selected. Results In the eye classifier, the HC performed better than the FMC (accuracy = 0.74 and extended Matthew Correlation Coefficient (MCC) = 0.68). In the subject classification, accuracy = 0.95 and MCC = 0.93, confirming that it may be a promising tool for MS diagnosis. Conclusion In addition to amplitude (axonal loss) and latency (demyelination), it has shown that the singular values of the mfVEP signals provide discriminatory information that may be used to identify subjects with differing degrees of the disease.
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Affiliation(s)
- Luis de Santiago
- Grupo de Ingeniería Biomédica, Departamento de Electrónica, Universidad de Alcalá, Alcalá de Henares, Spain
| | - E. M. Sánchez Morla
- Instituto de Investigación Hospital 12 de Octubre (i+12), Madrid, Spain
- Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
| | - Miguel Ortiz
- Grupo de Ingeniería Biomédica, Departamento de Electrónica, Universidad de Alcalá, Alcalá de Henares, Spain
| | - Elena López
- Grupo de Ingeniería Biomédica, Departamento de Electrónica, Universidad de Alcalá, Alcalá de Henares, Spain
| | - Carlos Amo Usanos
- Grupo de Ingeniería Biomédica, Departamento de Electrónica, Universidad de Alcalá, Alcalá de Henares, Spain
| | | | - R. Barea
- Grupo de Ingeniería Biomédica, Departamento de Electrónica, Universidad de Alcalá, Alcalá de Henares, Spain
| | - Carlo Cavaliere-Ballesta
- Grupo de Ingeniería Biomédica, Departamento de Electrónica, Universidad de Alcalá, Alcalá de Henares, Spain
| | - Alfredo Fernández
- Grupo de Ingeniería Biomédica, Departamento de Electrónica, Universidad de Alcalá, Alcalá de Henares, Spain
| | - Luciano Boquete
- Grupo de Ingeniería Biomédica, Departamento de Electrónica, Universidad de Alcalá, Alcalá de Henares, Spain
- RETICS: Red Temática de Investigación Cooperativa Sanitaria en Enfermedades Oculares Oftared, Madrid, Spain
- * E-mail:
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Kapoor R, Walters SP, Al-Aswad LA. The current state of artificial intelligence in ophthalmology. Surv Ophthalmol 2019; 64:233-240. [DOI: 10.1016/j.survophthal.2018.09.002] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 08/22/2018] [Accepted: 09/07/2018] [Indexed: 02/06/2023]
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Einarsdottir AB, Olafsdottir OB, Hjaltason H, Hardarson SH. Retinal oximetry is affected in multiple sclerosis. Acta Ophthalmol 2018; 96:528-530. [PMID: 29338134 DOI: 10.1111/aos.13682] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Accepted: 11/27/2017] [Indexed: 11/26/2022]
Abstract
PURPOSE Structural and physiological abnormalities have been reported in the retina in patients with multiple sclerosis (MS). Retinal oximetry has recently detected changes in retinal oxygen metabolism in Alzheimer's disease and mild cognitive impairment. Our goal was to determine whether oxygen saturation in retinal blood vessels of patients with patients is different from that of a healthy population. METHODS Oxygen saturation of haemoglobin was measured in retinal blood vessels, using imaging with spectrophotometric noninvasive retinal oximeter. Eight MS patients with history of optic neuritis were measured and compared to 22 healthy individuals matched in age and gender. RESULTS Venular oxygen saturation was increased in patients with MS compared to healthy individuals (70.7 ± 3.4% versus 66.2 ± 4.7; p = 0.021, mean ± SD). The arteriovenous (AV) difference was lower in patients with MS compared to healthy (26.6 ± 3.6% versus 30.5 ± 4.8%; p = 0.049). There was no difference measured in arterioles when patients with MS (97.3 ± 1.7%) and healthy individuals (96.7 ± 2.8%) were compared. CONCLUSION Increased venular oxygen saturation and lower AV difference in patients with MS may indicate reduced oxygen uptake. This may be due to less oxygen demand following atrophy and may be a useful objective biomarker for MS. Further studies are needed to confirm and expand these findings.
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Affiliation(s)
- Anna Bryndis Einarsdottir
- Department of Neurology; Landspitali - National University Hospital; Reykjavik Iceland
- Department of Neurology; Odense University Hospital; Odense Denmark
| | - Olof Birna Olafsdottir
- Department of Ophthalmology; Landspitali - National University Hospital; Reykjavik Iceland
- University of Iceland; Reykjavik Iceland
| | - Haukur Hjaltason
- Department of Neurology; Landspitali - National University Hospital; Reykjavik Iceland
- University of Iceland; Reykjavik Iceland
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Petzold A, Balcer LJ, Calabresi PA, Costello F, Frohman TC, Frohman EM, Martinez-Lapiscina EH, Green AJ, Kardon R, Outteryck O, Paul F, Schippling S, Vermersch P, Villoslada P, Balk LJ. Retinal layer segmentation in multiple sclerosis: a systematic review and meta-analysis. Lancet Neurol 2017; 16:797-812. [PMID: 28920886 DOI: 10.1016/s1474-4422(17)30278-8] [Citation(s) in RCA: 337] [Impact Index Per Article: 48.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2016] [Revised: 03/13/2017] [Accepted: 08/03/2017] [Indexed: 12/11/2022]
Abstract
BACKGROUND Structural retinal imaging biomarkers are important for early recognition and monitoring of inflammation and neurodegeneration in multiple sclerosis. With the introduction of spectral domain optical coherence tomography (SD-OCT), supervised automated segmentation of individual retinal layers is possible. We aimed to investigate which retinal layers show atrophy associated with neurodegeneration in multiple sclerosis when measured with SD-OCT. METHODS In this systematic review and meta-analysis, we searched for studies in which SD-OCT was used to look at the retina in people with multiple sclerosis with or without optic neuritis in PubMed, Web of Science, and Google Scholar between Nov 22, 1991, and April 19, 2016. Data were taken from cross-sectional cohorts and from one timepoint from longitudinal studies (at least 3 months after onset in studies of optic neuritis). We classified data on eyes into healthy controls, multiple-sclerosis-associated optic neuritis (MSON), and multiple sclerosis without optic neuritis (MSNON). We assessed thickness of the retinal layers and we rated individual layer segmentation performance by random effects meta-analysis for MSON eyes versus control eyes, MSNON eyes versus control eyes, and MSNON eyes versus MSON eyes. We excluded relevant sources of bias by funnel plots. FINDINGS Of 25 497 records identified, 110 articles were eligible and 40 reported data (in total 5776 eyes from patients with multiple sclerosis [1667 MSON eyes and 4109 MSNON eyes] and 1697 eyes from healthy controls) that met published OCT quality control criteria and were suitable for meta-analysis. Compared with control eyes, the peripapillary retinal nerve fibre layer (RNFL) showed thinning in MSON eyes (mean difference -20·10 μm, 95% CI -22·76 to -17·44; p<0·0001) and in MSNON eyes (-7·41 μm, -8·98 to -5·83; p<0·0001). The macula showed RNFL thinning of -6·18 μm (-8·07 to -4·28; p<0·0001) in MSON eyes and -2·15 μm (-3·15 to -1·15; p<0·0001) in MSNON eyes compared with control eyes. Atrophy of the macular ganglion cell layer and inner plexiform layer (GCIPL) was -16·42 μm (-19·23 to -13·60; p<0·0001) for MSON eyes and -6·31 μm (-7·75 to -4·87; p<0·0001) for MSNON eyes compared with control eyes. A small degree of inner nuclear layer (INL) thickening occurred in MSON eyes compared with control eyes (0·77 μm, 0·25 to 1·28; p=0·003). We found no statistical difference in the thickness of the combined outer nuclear layer and outer plexiform layer when we compared MSNON or MSON eyes with control eyes, but we found a small degree of thickening of the combined layer when we compared MSON eyes with MSNON eyes (1·21 μm, 0·24 to 2·19; p=0·01). INTERPRETATION The largest and most robust differences between the eyes of people with multiple sclerosis and control eyes were found in the peripapillary RNFL and macular GCIPL. Inflammatory disease activity might be captured by the INL. Because of the consistency, robustness, and large effect size, we recommend inclusion of the peripapillary RNFL and macular GCIPL for diagnosis, monitoring, and research. FUNDING None.
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Affiliation(s)
- Axel Petzold
- Moorfields Eye Hospital, London, UK; Department of Neurology, Amsterdam Neuroscience, VUmc MS Center Amsterdam and Dutch Expertise Centre for Neuro-ophthalmology, VU University Medical Center, Amsterdam, Netherlands; Institute of Neurology, University College London, London, UK.
| | - Laura J Balcer
- Department of Neurology, Department of Ophthalmology, and Department of Population Health, New York University School of Medicine, New York, NY, USA
| | | | - Fiona Costello
- Department of Clinical Neurosciences and Department of Surgery, University of Calgary, Calgary, AB, Canada
| | - Teresa C Frohman
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Elliot M Frohman
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Elena H Martinez-Lapiscina
- Center of Neuroimmunology, Institute of Biomedical Research August Pi Sunyer, Hospital Clinic of Barcelona, Barcelona, Spain
| | - Ari J Green
- Multiple Sclerosis Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Randy Kardon
- Iowa City VA Center for Prevention and Treatment of Visual Loss, Department of Veterans Affairs Hospital Iowa City, and Department of Ophthalmology and Visual Sciences, University of Iowa Hospital and Clinics, Iowa City, IA, USA
| | - Olivier Outteryck
- Department of Neurology, University of Lille Nord de France, Lille, France
| | - Friedemann Paul
- NeuroCure Clinical Research Center, Charité, Department of Neurology, Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Sven Schippling
- Neuroimmunology and Multiple Sclerosis Research Section, University Hospital Zurich, Zurich, Switzerland
| | - Patrik Vermersch
- Université Lille, CHRU Lille, LYRIC-INSERM U995, FHU Imminent, Lille, France
| | - Pablo Villoslada
- Center of Neuroimmunology, Institute of Biomedical Research August Pi Sunyer, Hospital Clinic of Barcelona, Barcelona, Spain
| | - Lisanne J Balk
- Department of Neurology, Amsterdam Neuroscience, VUmc MS Center Amsterdam and Dutch Expertise Centre for Neuro-ophthalmology, VU University Medical Center, Amsterdam, Netherlands
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Chhablani PP, Ambiya V, Nair AG, Bondalapati S, Chhablani J. Retinal Findings on OCT in Systemic Conditions. Semin Ophthalmol 2017. [DOI: 10.1080/08820538.2017.1332233] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Preeti Patil Chhablani
- Srimati Kanuri Santhamma Centre for Vitreo Retinal Diseases, KAR Campus, L. V. Prasad Eye Institute, Hyderabad, Telangana, India
| | - Vikas Ambiya
- Srimati Kanuri Santhamma Centre for Vitreo Retinal Diseases, KAR Campus, L. V. Prasad Eye Institute, Hyderabad, Telangana, India
| | - Akshay G. Nair
- Srimati Kanuri Santhamma Centre for Vitreo Retinal Diseases, KAR Campus, L. V. Prasad Eye Institute, Hyderabad, Telangana, India
| | | | - Jay Chhablani
- Srimati Kanuri Santhamma Centre for Vitreo Retinal Diseases, KAR Campus, L. V. Prasad Eye Institute, Hyderabad, Telangana, India
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24
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Einarsdottir AB, Hardarson SH, Kristjansdottir JV, Bragason DT, Snaedal J, Stefánsson E. Retinal Oximetry Imaging in Alzheimer’s Disease. J Alzheimers Dis 2015; 49:79-83. [DOI: 10.3233/jad-150457] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
| | - Sveinn Hakon Hardarson
- Department of Ophthalmology, Landspitali - National University Hospital, Reykjavik, Iceland
- University of Iceland, Reykjavik, Iceland
| | - Jona Valgerdur Kristjansdottir
- Department of Ophthalmology, Landspitali - National University Hospital, Reykjavik, Iceland
- University of Iceland, Reykjavik, Iceland
| | - David Thor Bragason
- Department of Ophthalmology, Landspitali - National University Hospital, Reykjavik, Iceland
| | - Jon Snaedal
- Department of Geriatrics, Landspitali - National University Hospital, Reykjavik, Iceland
| | - Einar Stefánsson
- Department of Ophthalmology, Landspitali - National University Hospital, Reykjavik, Iceland
- University of Iceland, Reykjavik, Iceland
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25
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Garcia-Martin E, Polo V, Larrosa JM, Marques ML, Herrero R, Martin J, Ara JR, Fernandez J, Pablo LE. Retinal layer segmentation in patients with multiple sclerosis using spectral domain optical coherence tomography. Ophthalmology 2013; 121:573-9. [PMID: 24268855 DOI: 10.1016/j.ophtha.2013.09.035] [Citation(s) in RCA: 70] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2013] [Revised: 08/20/2013] [Accepted: 09/19/2013] [Indexed: 02/05/2023] Open
Abstract
PURPOSE To evaluate the thickness of the 10 retinal layers in the paramacular area of patients with multiple sclerosis (MS) compared with healthy subjects using the new segmentation technology of spectral domain optical coherence tomography (OCT). To examine which layer has better sensitivity for detecting neurodegeneration in patients with MS. DESIGN Observational, cross-sectional study. PARTICIPANTS Patients with MS (n = 204) and age-matched healthy subjects (n = 138). METHODS The Spectralis OCT system (Heidelberg Engineering, Inc., Heidelberg, Germany) was used to obtain automated segmentation of all retinal layers in a parafoveal scan in 1 randomly selected eye of each participant, using the new segmentation application prototype. MAIN OUTCOME MEASURES The thicknesses of 512 parafoveal points in the 10 retinal layers were obtained in each eye, and the mean thickness of each layer was calculated and compared between patients with MS and healthy subjects. The analysis was repeated, comparing patients with MS with and without previous optic neuritis. Correlation analysis was performed to evaluate the association between each retinal layer mean thickness, duration of disease, and functional disability in patients with MS. A logistic regression analysis was performed to determine which layer provided better sensitivity for detecting neurodegeneration in patients with MS. RESULTS All retinal layers, except the inner limiting membrane, were thinner in patients with MS compared with healthy subjects (P < 0.05). Greater effects were observed in the inner retinal layers (nerve fiber, ganglion cells, inner plexiform, and inner nuclear layers) of eyes with previous optic neuritis (P < 0.05). The retinal nerve fiber layer and ganglion cell layer thicknesses were inversely correlated with the functional disability score in patients with MS. The ganglion cell layer and inner plexiform layer thicknesses could predict axonal damage in patients with MS. CONCLUSIONS Analysis based on the segmentation technology of the Spectralis OCT revealed retinal layer atrophy in patients with MS, especially of the inner layers. Reduction of the ganglion cell and inner plexiform layers predicted greater axonal damage in patients with MS.
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Affiliation(s)
- Elena Garcia-Martin
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain; Aragones Institute of Health Sciences, Zaragoza, Spain.
| | - Vicente Polo
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain; Aragones Institute of Health Sciences, Zaragoza, Spain
| | - Jose M Larrosa
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain; Aragones Institute of Health Sciences, Zaragoza, Spain
| | - Marcia L Marques
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain; Aragones Institute of Health Sciences, Zaragoza, Spain; Instituto de Moléstias Oculares, São Paulo, Brazil
| | - Raquel Herrero
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain; Aragones Institute of Health Sciences, Zaragoza, Spain
| | - Jesus Martin
- Aragones Institute of Health Sciences, Zaragoza, Spain; Neurology Department, Miguel Servet University Hospital, Zaragoza, Spain
| | - Jose R Ara
- Aragones Institute of Health Sciences, Zaragoza, Spain; Neurology Department, Miguel Servet University Hospital, Zaragoza, Spain
| | - Javier Fernandez
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain; Aragones Institute of Health Sciences, Zaragoza, Spain
| | - Luis E Pablo
- Ophthalmology Department, Miguel Servet University Hospital, Zaragoza, Spain; Aragones Institute of Health Sciences, Zaragoza, Spain
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