1
|
Collorone S, Coll L, Lorenzi M, Lladó X, Sastre-Garriga J, Tintoré M, Montalban X, Rovira À, Pareto D, Tur C. Artificial intelligence applied to MRI data to tackle key challenges in multiple sclerosis. Mult Scler 2024; 30:767-784. [PMID: 38738527 DOI: 10.1177/13524585241249422] [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] [Indexed: 05/14/2024]
Abstract
Artificial intelligence (AI) is the branch of science aiming at creating algorithms able to carry out tasks that typically require human intelligence. In medicine, there has been a tremendous increase in AI applications thanks to increasingly powerful computers and the emergence of big data repositories. Multiple sclerosis (MS) is a chronic autoimmune condition affecting the central nervous system with a complex pathogenesis, a challenging diagnostic process strongly relying on magnetic resonance imaging (MRI) and a high and largely unexplained variability across patients. Therefore, AI applications in MS have the great potential of helping us better support the diagnosis, find markers for prognosis to eventually design more powerful randomised clinical trials and improve patient management in clinical practice and eventually understand the mechanisms of the disease. This topical review aims to summarise the recent advances in AI applied to MRI data in MS to illustrate its achievements, limitations and future directions.
Collapse
Affiliation(s)
- Sara Collorone
- NMR Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Llucia Coll
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Marco Lorenzi
- Epione Research Project, Inria Sophia Antipolis, Université Côte d'Azur, Nice, France
| | - Xavier Lladó
- Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain
| | - Jaume Sastre-Garriga
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Mar Tintoré
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Xavier Montalban
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology (IDI), Vall d'Hebron University Hospital, Barcelona, Spain
- Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology (IDI), Vall d'Hebron University Hospital, Barcelona, Spain
- Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Carmen Tur
- NMR Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| |
Collapse
|
2
|
Prathapan V, Eipert P, Wigger N, Kipp M, Appali R, Schmitt O. Modeling and simulation for prediction of multiple sclerosis progression. Comput Biol Med 2024; 175:108416. [PMID: 38657465 DOI: 10.1016/j.compbiomed.2024.108416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 03/28/2024] [Accepted: 04/03/2024] [Indexed: 04/26/2024]
Abstract
In light of extensive work that has created a wide range of techniques for predicting the course of multiple sclerosis (MS) disease, this paper attempts to provide an overview of these approaches and put forth an alternative way to predict the disease progression. For this purpose, the existing methods for estimating and predicting the course of the disease have been categorized into clinical, radiological, biological, and computational or artificial intelligence-based markers. Weighing the weaknesses and strengths of these prognostic groups is a profound method that is yet in need and works directly at the level of diseased connectivity. Therefore, we propose using the computational models in combination with established connectomes as a predictive tool for MS disease trajectories. The fundamental conduction-based Hodgkin-Huxley model emerged as promising from examining these studies. The advantage of the Hodgkin-Huxley model is that certain properties of connectomes, such as neuronal connection weights, spatial distances, and adjustments of signal transmission rates, can be taken into account. It is precisely these properties that are particularly altered in MS and that have strong implications for processing, transmission, and interactions of neuronal signaling patterns. The Hodgkin-Huxley (HH) equations as a point-neuron model are used for signal propagation inside a small network. The objective is to change the conduction parameter of the neuron model, replicate the changes in myelin properties in MS and observe the dynamics of the signal propagation across the network. The model is initially validated for different lengths, conduction values, and connection weights through three nodal connections. Later, these individual factors are incorporated into a small network and simulated to mimic the condition of MS. The signal propagation pattern is observed after inducing changes in conduction parameters at certain nodes in the network and compared against a control model pattern obtained before the changes are applied to the network. The signal propagation pattern varies as expected by adapting to the input conditions. Similarly, when the model is applied to a connectome, the pattern changes could give an insight into disease progression. This approach has opened up a new path to explore the progression of the disease in MS. The work is in its preliminary state, but with a future vision to apply this method in a connectome, providing a better clinical tool.
Collapse
Affiliation(s)
- Vishnu Prathapan
- Medical School Hamburg University of Applied Sciences and Medical University, Am Kaiserkai 1, 20457, Hamburg, Germany.
| | - Peter Eipert
- Medical School Hamburg University of Applied Sciences and Medical University, Am Kaiserkai 1, 20457, Hamburg, Germany.
| | - Nicole Wigger
- Department of Anatomy, University of Rostock Gertrudenstr 9, 18057, Rostock, Germany.
| | - Markus Kipp
- Department of Anatomy, University of Rostock Gertrudenstr 9, 18057, Rostock, Germany.
| | - Revathi Appali
- Institute of General Electrical Engineering, University of Rostock, Albert-Einstein-Straße 2, 18059, Rostock, Germany; Department of Aging of Individuals and Society, Interdisciplinary Faculty, University of Rostock, Universitätsplatz 1, 18055, Rostock, Germany.
| | - Oliver Schmitt
- Medical School Hamburg University of Applied Sciences and Medical University, Am Kaiserkai 1, 20457, Hamburg, Germany; Department of Anatomy, University of Rostock Gertrudenstr 9, 18057, Rostock, Germany.
| |
Collapse
|
3
|
Yim D, Khuntia J, Parameswaran V, Meyers A. Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. JMIR Med Inform 2024; 12:e52073. [PMID: 38506918 PMCID: PMC10993141 DOI: 10.2196/52073] [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: 08/21/2023] [Revised: 10/12/2023] [Accepted: 01/30/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. OBJECTIVE This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. METHODS We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. RESULTS Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. CONCLUSIONS GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.
Collapse
Affiliation(s)
- Dobin Yim
- Loyola University, Maryland, MD, United States
| | - Jiban Khuntia
- University of Colorado Denver, Denver, CO, United States
| | | | - Arlen Meyers
- University of Colorado Denver, Denver, CO, United States
| |
Collapse
|
4
|
Andorra M, Freire A, Zubizarreta I, de Rosbo NK, Bos SD, Rinas M, Høgestøl EA, de Rodez Benavent SA, Berge T, Brune-Ingebretse S, Ivaldi F, Cellerino M, Pardini M, Vila G, Pulido-Valdeolivas I, Martinez-Lapiscina EH, Llufriu S, Saiz A, Blanco Y, Martinez-Heras E, Solana E, Bäcker-Koduah P, Behrens J, Kuchling J, Asseyer S, Scheel M, Chien C, Zimmermann H, Motamedi S, Kauer-Bonin J, Brandt A, Saez-Rodriguez J, Alexopoulos LG, Paul F, Harbo HF, Shams H, Oksenberg J, Uccelli A, Baeza-Yates R, Villoslada P. Predicting disease severity in multiple sclerosis using multimodal data and machine learning. J Neurol 2024; 271:1133-1149. [PMID: 38133801 PMCID: PMC10896787 DOI: 10.1007/s00415-023-12132-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 10/28/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity. METHODS We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre. RESULTS We found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts. CONCLUSION Combining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening.
Collapse
Affiliation(s)
- Magi Andorra
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | - Ana Freire
- School of Management, Pompeu Fabra University, Barcelona, Spain
- UPF Barcelona School of Management, Balmes 132, 08008, Barcelona, Spain
| | - Irati Zubizarreta
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | - Nicole Kerlero de Rosbo
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Steffan D Bos
- University of Oslo, Oslo, Norway
- Oslo University Hospital, Oslo, Norway
| | - Melanie Rinas
- Institute for Computational Biomedicine, Heidelberg University Hospital, and Heidelberg University, Heidelberg, Germany
| | - Einar A Høgestøl
- University of Oslo, Oslo, Norway
- Oslo University Hospital, Oslo, Norway
| | | | - Tone Berge
- Oslo University Hospital, Oslo, Norway
- Oslo Metropolitan University, Oslo, Norway
| | | | - Federico Ivaldi
- Department of Internal Medicine, University of Genoa, Genoa, Italy
| | - Maria Cellerino
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy
| | - Matteo Pardini
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Gemma Vila
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | - Irene Pulido-Valdeolivas
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | - Elena H Martinez-Lapiscina
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | - Sara Llufriu
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | - Albert Saiz
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | - Yolanda Blanco
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | - Eloy Martinez-Heras
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | - Elisabeth Solana
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | | | | | | | - Susanna Asseyer
- Charité Universitaetsmedizin Berlin, Berlin, Germany
- Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | | | - Claudia Chien
- Charité Universitaetsmedizin Berlin, Berlin, Germany
- Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Hanna Zimmermann
- Charité Universitaetsmedizin Berlin, Berlin, Germany
- Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | | | | | - Alex Brandt
- Charité Universitaetsmedizin Berlin, Berlin, Germany
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University Hospital, and Heidelberg University, Heidelberg, Germany
| | - Leonidas G Alexopoulos
- ProtATonce Ltd, Athens, Greece
- School of Mechanical Engineering, National Technical University of Athens, Zografou, Greece
| | - Friedemann Paul
- Charité Universitaetsmedizin Berlin, Berlin, Germany
- Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Hanne F Harbo
- University of Oslo, Oslo, Norway
- Oslo University Hospital, Oslo, Norway
| | - Hengameh Shams
- Department of Neurology, University of California, San Francisco, USA
| | - Jorge Oksenberg
- Department of Neurology, University of California, San Francisco, USA
| | - Antonio Uccelli
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | | | - Pablo Villoslada
- Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain.
- Hospital del Mar Research Institute, Barcelona, Spain.
| |
Collapse
|
5
|
Yan Z, Shi Z, Zhu Q, Feng J, Liu Y, Li Y, Zhou F, Zhuo Z, Ding S, Wang X, Yin F, Tang Y, Lin B, Li Y. Interpretable and Intuitive Machine Learning Approaches for Predicting Disability Progression in Relapsing-Remitting Multiple Sclerosis Based on Clinical and Gray Matter Atrophy Indicators. Acad Radiol 2024:S1076-6332(24)00054-0. [PMID: 38429188 DOI: 10.1016/j.acra.2024.01.032] [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: 12/29/2023] [Revised: 01/17/2024] [Accepted: 01/23/2024] [Indexed: 03/03/2024]
Abstract
RATIONALE AND OBJECTIVES To investigate whether clinical and gray matter (GM) atrophy indicators can predict disability in relapsing-remitting multiple sclerosis (RRMS) and to enhance the interpretability and intuitiveness of a predictive machine learning model. MATERIALS AND METHODS 145 and 50 RRMS patients with structural MRI and at least 1-year follow-up Expanded Disability Status Scale (EDSS) results were retrospectively enrolled and placed in the discovery and external test cohorts, respectively. Six clinical and radiomics feature-based machine learning classifiers were trained and tested to predict disability progression in the discovery cohort and validated in the external test set. Partial dependence plot (PDP) analysis and a Shiny web application were conducted to enhance the interpretability and intuitiveness. RESULTS In the discovery cohort, 98 patients had disability stability, and 47 patients were classified as having disability progression. In the external test set, 35 patients were disability stable, and 15 patients had disability progression. Models trained with both clinical and radiomics features (area under the curve (AUC), 0.725-0.950) outperformed those trained with clinical (AUC, 0.600-0.740) or radiomics features only (AUC, 0.615-0.945). Among clinical+ radiomics feature models, the logistic regression (LR) classifier-based model performed best, with an AUC of 0.950. Only the radiomics feature-only models were applied in the external test set due to the data collection problem and showed fair performance, with AUCs ranging from 0.617 to 0.753. PDP analysis showed that female patients and those with lower volume, surface area, and symbol digit modalities test (SDMT) scores; greater mean curvature and age; and no disease modifying therapy (DMT) had increased probabilities of disease progression. Finally, a Shiny web application (https://lauralin1104.shinyapps.io/LRshiny/) was developed to calculate the risk of disability progression. CONCLUSION Interpretable and intuitive machine learning approaches based on clinical and GM atrophy indicators can help physicians predict disability progression in RRMS patients for clinical decision-making and patient management.
Collapse
Affiliation(s)
- Zichun Yan
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhuowei Shi
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qiyuan Zhu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jinzhou Feng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shuang Ding
- Department of Radiology, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaohua Wang
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
| | - Feiyue Yin
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yang Tang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Bing Lin
- College of Public Health, Chongqing Medical University, Chongqing, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| |
Collapse
|
6
|
Kelly BS, Mathur P, McGuinness G, Dillon H, Lee EH, Yeom KW, Lawlor A, Killeen RP. A Radiomic "Warning Sign" of Progression on Brain MRI in Individuals with MS. AJNR Am J Neuroradiol 2024; 45:236-243. [PMID: 38216299 DOI: 10.3174/ajnr.a8104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 11/08/2023] [Indexed: 01/14/2024]
Abstract
BACKGROUND AND PURPOSE MS is a chronic progressive, idiopathic, demyelinating disorder whose diagnosis is contingent on the interpretation of MR imaging. New MR imaging lesions are an early biomarker of disease progression. We aimed to evaluate a machine learning model based on radiomics features in predicting progression on MR imaging of the brain in individuals with MS. MATERIALS AND METHODS This retrospective cohort study with external validation on open-access data obtained full ethics approval. Longitudinal MR imaging data for patients with MS were collected and processed for machine learning. Radiomics features were extracted at the future location of a new lesion in the patients' prior MR imaging ("prelesion"). Additionally, "control" samples were obtained from the normal-appearing white matter for each participant. Machine learning models for binary classification were trained and tested and then evaluated the external data of the model. RESULTS The total number of participants was 167. Of the 147 in the training/test set, 102 were women and 45 were men. The average age was 42 (range, 21-74 years). The best-performing radiomics-based model was XGBoost, with accuracy, precision, recall, and F1-score of 0.91, 0.91, 0.91, and 0.91 on the test set, and 0.74, 0.74, 0.74, and 0.70 on the external validation set. The 5 most important radiomics features to the XGBoost model were associated with the overall heterogeneity and low gray-level emphasis of the segmented regions. Probability maps were produced to illustrate potential future clinical applications. CONCLUSIONS Our machine learning model based on radiomics features successfully differentiated prelesions from normal-appearing white matter. This outcome suggests that radiomics features from normal-appearing white matter could serve as an imaging biomarker for progression of MS on MR imaging.
Collapse
Affiliation(s)
- Brendan S Kelly
- From the Department of Radiology (B.S.K., G.M., H.D., R.P.K.), St. Vincent's University Hospital, Dublin, Ireland
- Insight Centre for Data Analytics (B.S.K., P.M., A.L.), University College Dublin, Dublin, Ireland
- Wellcome Trust and Health Research Board (B.S.K.), Irish Clinical Academic Training, Dublin, Ireland
- School of Medicine (B.S.K.), University College Dublin, Dublin, Ireland
| | - Prateek Mathur
- Insight Centre for Data Analytics (B.S.K., P.M., A.L.), University College Dublin, Dublin, Ireland
| | - Gerard McGuinness
- From the Department of Radiology (B.S.K., G.M., H.D., R.P.K.), St. Vincent's University Hospital, Dublin, Ireland
| | - Henry Dillon
- From the Department of Radiology (B.S.K., G.M., H.D., R.P.K.), St. Vincent's University Hospital, Dublin, Ireland
| | - Edward H Lee
- Lucille Packard Children's Hospital at Stanford (E.H.L., K.W.Y.), Stanford, California
| | - Kristen W Yeom
- Lucille Packard Children's Hospital at Stanford (E.H.L., K.W.Y.), Stanford, California
| | - Aonghus Lawlor
- Insight Centre for Data Analytics (B.S.K., P.M., A.L.), University College Dublin, Dublin, Ireland
| | - Ronan P Killeen
- From the Department of Radiology (B.S.K., G.M., H.D., R.P.K.), St. Vincent's University Hospital, Dublin, Ireland
| |
Collapse
|
7
|
Park HC, Joo Y, Lee OJ, Lee K, Song TK, Choi C, Choi MH, Yoon C. Automated classification of liver fibrosis stages using ultrasound imaging. BMC Med Imaging 2024; 24:36. [PMID: 38321373 PMCID: PMC10848434 DOI: 10.1186/s12880-024-01209-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 01/21/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Ultrasound imaging is the most frequently performed for the patients with chronic hepatitis or liver cirrhosis. However, ultrasound imaging is highly operator dependent and interpretation of ultrasound images is subjective, thus well-trained radiologist is required for evaluation. Automated classification of liver fibrosis could alleviate the shortage of skilled radiologist especially in low-to-middle income countries. The purposed of this study is to evaluate deep convolutional neural networks (DCNNs) for classifying the degree of liver fibrosis according to the METAVIR score using US images. METHODS We used ultrasound (US) images from two tertiary university hospitals. A total of 7920 US images from 933 patients were used for training/validation of DCNNs. All patient were underwent liver biopsy or hepatectomy, and liver fibrosis was categorized based on pathology results using the METAVIR score. Five well-established DCNNs (VGGNet, ResNet, DenseNet, EfficientNet and ViT) was implemented to predict the METAVIR score. The performance of DCNNs for five-level (F0/F1/F2/F3/F4) classification was evaluated through area under the receiver operating characteristic curve (AUC) with 95% confidential interval, accuracy, sensitivity, specificity, positive and negative likelihood ratio. RESULTS Similar mean AUC values were achieved for five models; VGGNet (0.96), ResNet (0.96), DenseNet (0.95), EfficientNet (0.96), and ViT (0.95). The same mean accuracy (0.94) and specificity values (0.96) were yielded for all models. In terms of sensitivity, EffcientNet achieved highest mean value (0.85) while the other models produced slightly lower values range from 0.82 to 0.84. CONCLUSION In this study, we demonstrated that DCNNs can classify the staging of liver fibrosis according to METAVIR score with high performance using conventional B-mode images. Among them, EfficientNET that have fewer parameters and computation cost produced highest performance. From the results, we believe that DCNNs based classification of liver fibrosis may allow fast and accurate diagnosis of liver fibrosis without needs of additional equipment for add-on test and may be powerful tool for supporting radiologists in clinical practice.
Collapse
Grants
- NTIS Number: 9991007146 the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety
- HI21C0940110021 the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea
- No. 2022-0-00101 the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT)
- the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety
- the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea
- the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT)
Collapse
Affiliation(s)
- Hyun-Cheol Park
- Division of Industrial Mathematics, National Institute for Mathematical Sciences, 70, Yuseong-daero, Yuseong-gu, 34047, Daejeon, Republic of Korea
| | - YunSang Joo
- Department of Computer Engineering, Gachon University, 1342, Seongnam-daero, Sujeong-gu, 13120, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - O-Joun Lee
- Department of Artificial Intelligence, The Catholic University of Korea, 43, Jibong-ro, 14662, Bucheon-si, Gyeonggi-do, Republic of Korea
| | - Kunkyu Lee
- Department of Electronic Engineering, Sogang University, 35 Baekbeom-ro, 04107, Seoul, Republic of Korea
| | - Tai-Kyong Song
- Department of Electronic Engineering, Sogang University, 35 Baekbeom-ro, 04107, Seoul, Republic of Korea
| | - Chang Choi
- Department of Computer Engineering, Gachon University, 1342, Seongnam-daero, Sujeong-gu, 13120, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Moon Hyung Choi
- Department of Radiology, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seoul, Republic of Korea.
| | - Changhan Yoon
- Department of Biomedical Engineering, Department of Nanoscience and Engineering, Inje University, Inje-ro 197, 50834, Gimhae, Gyeongnam, Republic of Korea.
| |
Collapse
|
8
|
Amin M, Nakamura K, Ontaneda D. Differentiating multiple sclerosis from non-specific white matter changes using a convolutional neural network image classification model. Mult Scler Relat Disord 2024; 82:105420. [PMID: 38183693 DOI: 10.1016/j.msard.2023.105420] [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: 08/14/2023] [Revised: 11/07/2023] [Accepted: 12/30/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND The diagnosis of multiple sclerosis (MS) relies heavily on neuroimaging with magnetic resonance imaging (MRI) and exclusion of mimics. This can be a challenging task due to radiological overlap in several disorders and may require ancillary testing or longitudinal follow up. One of the most common radiological MS mimickers is non-specific white matter disease (NSWMD). We aimed to develop and evaluate models leveraging machine learning algorithms to help distinguish MS and NSWMD. METHODS All adult patients who underwent MRI brain using a demyelinating protocol with available electronic medical records between 2015 and 2019 at Cleveland Clinic affiliated facilities were included. Diagnosis of MS and NSWMD were assessed from clinical documentation. Those with a diagnosis of MS and NSWMD were matched using total T2 lesion volume (T2LV) and used to train models with logistic regression and convolutional neural networks (CNN). Performance metrices were reported for each model. RESULTS A total of 250 NSWMD MRI scans were identified, and 250 unique MS MRI scans were matched on T2LV. Cross validated logistic regression model was able to use 20 variables (including spinal cord area, regional volumes, and fractions) to predict MS compared to NSWMD with 68.0% accuracy while the CNN model was able to classify MS compared to NSWMD in two independent validation and testing cohorts with 77% and 78% accuracy on average. CONCLUSION Automated methods can be used to differentiate MS compared to NSWMD. These methods can be used to supplement currently available diagnostic tools for patients being evaluated for MS.
Collapse
Affiliation(s)
- Moein Amin
- Mellen Center for Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Kunio Nakamura
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio, USA
| | - Daniel Ontaneda
- Mellen Center for Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA.
| |
Collapse
|
9
|
Voigt I, Inojosa H, Wenk J, Akgün K, Ziemssen T. Building a monitoring matrix for the management of multiple sclerosis. Autoimmun Rev 2023; 22:103358. [PMID: 37178996 DOI: 10.1016/j.autrev.2023.103358] [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: 04/27/2023] [Accepted: 05/09/2023] [Indexed: 05/15/2023]
Abstract
Multiple sclerosis (MS) has a longitudinal and heterogeneous course, with an increasing number of therapy options and associated risk profiles, leading to a constant increase in the number of parameters to be monitored. Even though important clinical and subclinical data are being generated, treating neurologists may not always be able to use them adequately for MS management. In contrast to the monitoring of other diseases in different medical fields, no target-based approach for a standardized monitoring in MS has been established yet. Therefore, there is an urgent need for a standardized and structured monitoring as part of MS management that is adaptive, individualized, agile, and multimodal-integrative. We discuss the development of an MS monitoring matrix which can help facilitate data collection over time from different dimensions and perspectives to optimize the treatment of people with MS (pwMS). In doing so, we show how different measurement tools can combined to enhance MS treatment. We propose to apply the concept of patient pathways to disease and intervention monitoring, not losing track of their interrelation. We also discuss the use of artificial intelligence (AI) to improve the quality of processes, outcomes, and patient safety, as well as personalized and patient-centered care. Patient pathways allow us to track the patient's journey over time and can always change (e.g., when there is a switch in therapy). They therefore may assist us in the continuous improvement of monitoring in an iterative process. Improving the monitoring process means improving the care of pwMS.
Collapse
Affiliation(s)
- Isabel Voigt
- Center of Clinical Neuroscience, Department of Neurology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Hernan Inojosa
- Center of Clinical Neuroscience, Department of Neurology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Judith Wenk
- Center of Clinical Neuroscience, Department of Neurology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Katja Akgün
- Center of Clinical Neuroscience, Department of Neurology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Tjalf Ziemssen
- Center of Clinical Neuroscience, Department of Neurology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany.
| |
Collapse
|
10
|
Labiano-Fontcuberta A, Costa-Frossard L, Sainz de la Maza S, Rodríguez-Jorge F, Chico-García JL, González PN, Monreal E. Predictive models of multiple sclerosis-related cognitive performance using routine clinical practice predictors. Mult Scler Relat Disord 2023; 76:104849. [PMID: 37356257 DOI: 10.1016/j.msard.2023.104849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 05/25/2023] [Accepted: 06/19/2023] [Indexed: 06/27/2023]
Abstract
BACKGROUND The application of machine learning (ML) to predict cognitive evolution is exceptionally scarce. Computer-based self-administered cognitive tests provide the opportunity to set up large longitudinal datasets to aid in developing ML prediction models of risk for Multiple Sclerosis-related cognitive decline. OBJECTIVE to analyze to what extent clinically feasible models can be built with standard clinical practice features and subsequently used for reliable prediction of cognitive evolution. METHODS This prospective longitudinal study includes 1184 people with MS who received a Processing Speed (PS) evaluation at 12 months of follow-up measured by the iPad®-based Processing Speed Test (PST). Six of the most potent classification models built with routine clinical practice features were trained and tested to predict the 12-month patient class label (PST worsening (PSTw) versus PST stable). A rigorous scheme of all the preprocessing steps run to obtain reliable generalization performance is detailed. RESULTS Based on a 12-month reduction of 10% of the PST raw score, 187/1184 (15.8%) people with MS were classified as PSTw. The trees-based models (random forest and the eXtreme Gradient Boosting) achieved the best performance, with an area under the receiver operating characteristic curve (AUC) of 0.90 and 0.89, respectively. The timing of high-efficacy disease-modifying therapies (heDMTs) was identified as one of the top importance predictors in all the models evaluated. CONCLUSION Using trees-based machine learning models to predict individual future information processing speed deterioration in MS could become a reality in clinical practice.
Collapse
Affiliation(s)
- Andrés Labiano-Fontcuberta
- Department of Neurology, University Hospital12 de Octubre, Avenida de Córdoba 41, Community of Madrid 28026, Spain.
| | - Lucienne Costa-Frossard
- Department of Neurology, University Hospital Ramón y Cajal, Universidad de Alcalá, Ramón y Cajal Institute for Health Research (IRYCIS), Spanish Network of Multiple Sclerosis (REEM), Colmenar Viejo, km 9,100, Community of Madrid 28034, Spain
| | - Susana Sainz de la Maza
- Department of Neurology, University Hospital Ramón y Cajal, Universidad de Alcalá, Ramón y Cajal Institute for Health Research (IRYCIS), Spanish Network of Multiple Sclerosis (REEM), Colmenar Viejo, km 9,100, Community of Madrid 28034, Spain
| | - Fernando Rodríguez-Jorge
- Department of Neurology, University Hospital Ramón y Cajal, Universidad de Alcalá, Ramón y Cajal Institute for Health Research (IRYCIS), Spanish Network of Multiple Sclerosis (REEM), Colmenar Viejo, km 9,100, Community of Madrid 28034, Spain
| | - Juan Luis Chico-García
- Department of Neurology, University Hospital Ramón y Cajal, Universidad de Alcalá, Ramón y Cajal Institute for Health Research (IRYCIS), Spanish Network of Multiple Sclerosis (REEM), Colmenar Viejo, km 9,100, Community of Madrid 28034, Spain
| | - Pablo Nieto González
- Department of Neurology, University Hospital Infanta Elena, Avda. de los Reyes Católicos 21Valdemoro, Community of Madrid 28342, Spain
| | - Enric Monreal
- Department of Neurology, University Hospital Ramón y Cajal, Universidad de Alcalá, Ramón y Cajal Institute for Health Research (IRYCIS), Spanish Network of Multiple Sclerosis (REEM), Colmenar Viejo, km 9,100, Community of Madrid 28034, Spain
| |
Collapse
|
11
|
Du S, Yuan C, Zhou Q, Huang X, Meng H, Chen M, Wang H, Huang Q, Xiang S, Qian D, Li B, Chen S, Zhang M. Deep learning-based PET/MR radiomics for the classification of annualized relapse rate in multiple sclerosis. Mult Scler Relat Disord 2023; 75:104750. [PMID: 37196386 DOI: 10.1016/j.msard.2023.104750] [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: 01/18/2023] [Revised: 04/03/2023] [Accepted: 05/06/2023] [Indexed: 05/19/2023]
Abstract
Background Annualized Relapse Rate (ARR) is one of the most important indicators of disease progression in patients with Multiple Sclerosis (MS). However, imaging markers that can effectively predict ARR are currently unavailable. In this study, we developed a deep learning-based method for the automated extraction of radiomics features from Positron Emission Computed Tomography (PET) and Magnetic Resonance (MR) images to predict ARR in patients with MS. Methods Twenty-five patients with a definite diagnosis of Relapsing-Remitting MS (RRMS) were enrolled in this study. We designed a multi-branch fully convolutional neural network to segment lesions from PET/MR images. After that, radiomics features were extracted from the obtained lesion volume of interest. Three feature selection methods were used to retain features highly correlated with ARR. We combined four classifiers with different feature selection methods to form twelve models for ARR classification. Finally, the model with the best performance was chosen. Results Our network achieved precise automatic lesion segmentation with a Dice Similarity Coefficient (DSC) of 0.81 and a precision of 0.86. Radiomics features from lesions filtered by Recursive Feature Elimination (RFE) achieved the best performance in the Support Vector Machines (SVM) classifier. The classification model performance was best when radiomics from both PET and MR were combined to predict ARR, with high accuracy at 0.88 and Area Under the ROC curves (AUC) at 0.96, which outperformed MR or PET-based model and clinical indicators-based model. Conclusion Our automatic segmentation masks can replace manual ones with excellent performance. Furthermore, the deep learning and PET/MR radiomics-based model in our research is an effective tool in assisting ARR classification of MS patients.
Collapse
Affiliation(s)
- Sijia Du
- School of Biomedical Engineering, Shanghai Jiao Tong University, China; Department of Nuclear Medicine, Ruijin Hospital,Shanghai Jiao Tong University School of Medicine, China
| | - Cheng Yuan
- School of Biomedical Engineering, Shanghai Jiao Tong University, China; College of Medical Imaging, Shanghai University of Medicine and Health Sciences, China
| | - Qinming Zhou
- Department of Neurology and Institute of Neurology, Ruijin Hospital,Shanghai Jiao Tong University School of Medicine, China
| | - Xinyun Huang
- Department of Nuclear Medicine, Ruijin Hospital,Shanghai Jiao Tong University School of Medicine, China
| | - Hongping Meng
- Department of Nuclear Medicine, Ruijin Hospital,Shanghai Jiao Tong University School of Medicine, China
| | - Meidi Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, China
| | - Hanzhong Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, China; Department of Nuclear Medicine, Ruijin Hospital,Shanghai Jiao Tong University School of Medicine, China
| | - Qiu Huang
- School of Biomedical Engineering, Shanghai Jiao Tong University, China
| | - Suncheng Xiang
- School of Biomedical Engineering, Shanghai Jiao Tong University, China
| | - Dahong Qian
- School of Biomedical Engineering, Shanghai Jiao Tong University, China
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital,Shanghai Jiao Tong University School of Medicine, China.
| | - Sheng Chen
- Department of Neurology and Institute of Neurology, Ruijin Hospital,Shanghai Jiao Tong University School of Medicine, China; Co-innovation Center of Neuroregeneration, Nantong University, China.
| | - Min Zhang
- Department of Nuclear Medicine, Ruijin Hospital,Shanghai Jiao Tong University School of Medicine, China.
| |
Collapse
|
12
|
Coll L, Pareto D, Carbonell-Mirabent P, Cobo-Calvo Á, Arrambide G, Vidal-Jordana Á, Comabella M, Castilló J, Rodríguez-Acevedo B, Zabalza A, Galán I, Midaglia L, Nos C, Salerno A, Auger C, Alberich M, Río J, Sastre-Garriga J, Oliver A, Montalban X, Rovira À, Tintoré M, Lladó X, Tur C. Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI. Neuroimage Clin 2023; 38:103376. [PMID: 36940621 PMCID: PMC10034138 DOI: 10.1016/j.nicl.2023.103376] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 03/09/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023]
Abstract
The application of convolutional neural networks (CNNs) to MRI data has emerged as a promising approach to achieving unprecedented levels of accuracy when predicting the course of neurological conditions, including multiple sclerosis, by means of extracting image features not detectable through conventional methods. Additionally, the study of CNN-derived attention maps, which indicate the most relevant anatomical features for CNN-based decisions, has the potential to uncover key disease mechanisms leading to disability accumulation. From a cohort of patients prospectively followed up after a first demyelinating attack, we selected those with T1-weighted and T2-FLAIR brain MRI sequences available for image analysis and a clinical assessment performed within the following six months (N = 319). Patients were divided into two groups according to expanded disability status scale (EDSS) score: ≥3.0 and < 3.0. A 3D-CNN model predicted the class using whole-brain MRI scans as input. A comparison with a logistic regression (LR) model using volumetric measurements as explanatory variables and a validation of the CNN model on an independent dataset with similar characteristics (N = 440) were also performed. The layer-wise relevance propagation method was used to obtain individual attention maps. The CNN model achieved a mean accuracy of 79% and proved to be superior to the equivalent LR-model (77%). Additionally, the model was successfully validated in the independent external cohort without any re-training (accuracy = 71%). Attention-map analyses revealed the predominant role of frontotemporal cortex and cerebellum for CNN decisions, suggesting that the mechanisms leading to disability accrual exceed the mere presence of brain lesions or atrophy and probably involve how damage is distributed in the central nervous system.
Collapse
Affiliation(s)
- Llucia Coll
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology (IDI), Vall d'Hebron University Hospital, Spain, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Pere Carbonell-Mirabent
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Álvaro Cobo-Calvo
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Georgina Arrambide
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Ángela Vidal-Jordana
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Manuel Comabella
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Joaquín Castilló
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Breogán Rodríguez-Acevedo
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Ana Zabalza
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Ingrid Galán
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Luciana Midaglia
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Carlos Nos
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Annalaura Salerno
- Section of Neuroradiology, Department of Radiology (IDI), Vall d'Hebron University Hospital, Spain, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Cristina Auger
- Section of Neuroradiology, Department of Radiology (IDI), Vall d'Hebron University Hospital, Spain, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Manel Alberich
- Section of Neuroradiology, Department of Radiology (IDI), Vall d'Hebron University Hospital, Spain, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jordi Río
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jaume Sastre-Garriga
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Arnau Oliver
- Research institute of Computer Vision and Robotics, University of Girona, Girona, Spain
| | - Xavier Montalban
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology (IDI), Vall d'Hebron University Hospital, Spain, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Mar Tintoré
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Xavier Lladó
- Research institute of Computer Vision and Robotics, University of Girona, Girona, Spain
| | - Carmen Tur
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
| |
Collapse
|
13
|
Olatunji SO, Alsheikh N, Alnajrani L, Alanazy A, Almusairii M, Alshammasi S, Alansari A, Zaghdoud R, Alahmadi A, Basheer Ahmed MI, Ahmed MS, Alhiyafi J. Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern Province of Saudi Arabia. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4261. [PMID: 36901273 PMCID: PMC10002108 DOI: 10.3390/ijerph20054261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/22/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
Multiple Sclerosis (MS) is characterized by chronic deterioration of the nervous system, mainly the brain and the spinal cord. An individual with MS develops the condition when the immune system begins attacking nerve fibers and the myelin sheathing that covers them, affecting the communication between the brain and the rest of the body and eventually causing permanent damage to the nerve. Patients with MS (pwMS) might experience different symptoms depending on which nerve was damaged and how much damage it has sustained. Currently, there is no cure for MS; however, there are clinical guidelines that help control the disease and its accompanying symptoms. Additionally, no specific laboratory biomarker can precisely identify the presence of MS, leaving specialists with a differential diagnosis that relies on ruling out other possible diseases with similar symptoms. Since the emergence of Machine Learning (ML) in the healthcare industry, it has become an effective tool for uncovering hidden patterns that aid in diagnosing several ailments. Several studies have been conducted to diagnose MS using ML and Deep Learning (DL) models trained using MRI images, achieving promising results. However, complex and expensive diagnostic tools are needed to collect and examine imaging data. Thus, the intention of this study is to implement a cost-effective, clinical data-driven model that is capable of diagnosing pwMS. The dataset was obtained from King Fahad Specialty Hospital (KFSH) in Dammam, Saudi Arabia. Several ML algorithms were compared, namely Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extra Trees (ET). The results indicated that the ET model outpaced the rest with an accuracy of 94.74%, recall of 97.26%, and precision of 94.67%.
Collapse
Affiliation(s)
- Sunday O. Olatunji
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Nawal Alsheikh
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Lujain Alnajrani
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Alhatoon Alanazy
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Meshael Almusairii
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Salam Alshammasi
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Aisha Alansari
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Rim Zaghdoud
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Alaa Alahmadi
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Mohammed Imran Basheer Ahmed
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Mohammed Salih Ahmed
- College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Jamal Alhiyafi
- Department of Computer Science, Kettering University, Flint, MI 48504, USA
| |
Collapse
|
14
|
Ziccardi S, Pisani AI, Schiavi GM, Guandalini M, Crescenzo F, Colombi A, Peloso A, Tamanti A, Bertolazzo M, Marastoni D, Calabrese M. Cortical lesions at diagnosis predict long-term cognitive impairment in multiple sclerosis: A 20-year study. Eur J Neurol 2023; 30:1378-1388. [PMID: 36692863 DOI: 10.1111/ene.15697] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/05/2022] [Accepted: 01/18/2023] [Indexed: 01/25/2023]
Abstract
BACKGROUND AND PURPOSE Although cognitive impairment (CI) is frequent in multiple sclerosis (MS) patients, few studies (and with conflicting results) have evaluated early predictors of CI in the long term. We aimed at determining associations between early clinical/neuroradiological variables with reference to CI after 20 years of MS. METHODS We investigated in 170 MS patients the relationship between clinical/magnetic resonance imaging (MRI) data at diagnosis and cognitive status almost 20 years after MS onset. Among others, number and volume of both white matter lesions (WMLs) and cortical lesions (CLs) were evaluated at diagnosis and after 2 years. All MS patients were followed over time and underwent a comprehensive neuropsychological assessment at the end of study. Advanced statistical methods (unsupervised cluster analysis and random forest model) were conducted. RESULTS CI patients showed higher focal cortical pathology at diagnosis compared to cognitively normal subjects (p < 0.001). Volumes of both WMLs and CLs emerged as the MRI metrics most associated with long-term CI. Moreover, number of CLs (especially ≥3) was also strongly associated with long-term CI (≥3 CLs: odds ratio [OR] = 3.7, 95% confidence interval = 1.8-7.5, p < 0.001), more than number of WMLs; the optimal cutoff of three CLs (area under the curve = 0.67, specificity = 75%, sensitivity = 55%) was estimated according to the risk of developing CI. CONCLUSIONS These results highlight the impact of considering both white and gray matter focal damage from early MS stages. Given the low predictive value of WML number and the poor clinical applicability of lesion volume estimation in the daily clinical context, the evaluation of number of CLs could represent a reliable prognostic marker of CI.
Collapse
Affiliation(s)
- Stefano Ziccardi
- Neurology Section, Department of Neuroscience, Biomedicine, and Movement Sciences, University of Verona, Verona, Italy
| | - Anna Isabella Pisani
- Neurology Section, Department of Neuroscience, Biomedicine, and Movement Sciences, University of Verona, Verona, Italy
| | - Gian Marco Schiavi
- Neurology Section, Department of Neuroscience, Biomedicine, and Movement Sciences, University of Verona, Verona, Italy
| | - Maddalena Guandalini
- Neurology Section, Department of Neuroscience, Biomedicine, and Movement Sciences, University of Verona, Verona, Italy
| | | | - Annalisa Colombi
- Neurology Section, Department of Neuroscience, Biomedicine, and Movement Sciences, University of Verona, Verona, Italy
| | - Angela Peloso
- Neurology Section, Department of Neuroscience, Biomedicine, and Movement Sciences, University of Verona, Verona, Italy
| | - Agnese Tamanti
- Neurology Section, Department of Neuroscience, Biomedicine, and Movement Sciences, University of Verona, Verona, Italy
| | - Maddalena Bertolazzo
- Neurology Section, Department of Neuroscience, Biomedicine, and Movement Sciences, University of Verona, Verona, Italy
| | - Damiano Marastoni
- Neurology Section, Department of Neuroscience, Biomedicine, and Movement Sciences, University of Verona, Verona, Italy
| | - Massimiliano Calabrese
- Neurology Section, Department of Neuroscience, Biomedicine, and Movement Sciences, University of Verona, Verona, Italy
| |
Collapse
|
15
|
Taloni A, Farrelly FA, Pontillo G, Petsas N, Giannì C, Ruggieri S, Petracca M, Brunetti A, Pozzilli C, Pantano P, Tommasin S. Evaluation of Disability Progression in Multiple Sclerosis via Magnetic-Resonance-Based Deep Learning Techniques. Int J Mol Sci 2022; 23:ijms231810651. [PMID: 36142563 PMCID: PMC9505100 DOI: 10.3390/ijms231810651] [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: 08/06/2022] [Revised: 09/02/2022] [Accepted: 09/05/2022] [Indexed: 11/16/2022] Open
Abstract
Short-term disability progression was predicted from a baseline evaluation in patients with multiple sclerosis (MS) using their three-dimensional T1-weighted (3DT1) magnetic resonance images (MRI). One-hundred-and-eighty-one subjects diagnosed with MS underwent 3T-MRI and were followed up for two to six years at two sites, with disability progression defined according to the expanded-disability-status-scale (EDSS) increment at the follow-up. The patients’ 3DT1 images were bias-corrected, brain-extracted, registered onto MNI space, and divided into slices along coronal, sagittal, and axial projections. Deep learning image classification models were applied on slices and devised as ResNet50 fine-tuned adaptations at first on a large independent dataset and secondly on the study sample. The final classifiers’ performance was evaluated via the area under the curve (AUC) of the false versus true positive diagram. Each model was also tested against its null model, obtained by reshuffling patients’ labels in the training set. Informative areas were found by intersecting slices corresponding to models fulfilling the disability progression prediction criteria. At follow-up, 34% of patients had disability progression. Five coronal and five sagittal slices had one classifier surviving the AUC evaluation and null test and predicted disability progression (AUC > 0.72 and AUC > 0.81, respectively). Likewise, fifteen combinations of classifiers and axial slices predicted disability progression in patients (AUC > 0.69). Informative areas were the frontal areas, mainly within the grey matter. Briefly, 3DT1 images may give hints on disability progression in MS patients, exploiting the information hidden in the MRI of specific areas of the brain.
Collapse
Affiliation(s)
- Alessandro Taloni
- Institute for Complex Systems, National Research Council (ISC-CNR), 00185 Rome, Italy
| | | | - Giuseppe Pontillo
- Department of Advanced Biomedical Sciences, Federico II University of Naples, 80131 Naples, Italy
- Department of Electrical Engineering and Information Technology, Federico II University of Naples, 80125 Naples, Italy
| | - Nikolaos Petsas
- Department of Radiology, IRCCS NEUROMED, 86077 Pozzilli, Italy
| | - Costanza Giannì
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Serena Ruggieri
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
- Neuroimmunology Unit, IRCSS Fondazione Santa Lucia, 00179 Rome, Italy
| | - Maria Petracca
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
- Department of Neuroscience, Reproductive Sciences and Odontostomatology, Federico II University of Naples, 80131 Naples, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, Federico II University of Naples, 80131 Naples, Italy
| | - Carlo Pozzilli
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Patrizia Pantano
- Department of Radiology, IRCCS NEUROMED, 86077 Pozzilli, Italy
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Silvia Tommasin
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
- Correspondence:
| |
Collapse
|
16
|
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.
Collapse
|