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Güllmar D, Hsu WC, Reichenbach JR. Predicting disease-related MRI patterns of multiple sclerosis through GAN-based image editing. Z Med Phys 2024; 34:318-329. [PMID: 38143166 PMCID: PMC11156773 DOI: 10.1016/j.zemedi.2023.12.001] [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: 02/28/2023] [Revised: 11/15/2023] [Accepted: 12/01/2023] [Indexed: 12/26/2023]
Abstract
INTRODUCTION Multiple sclerosis (MS) is a complex neurodegenerative disorder that affects the brain and spinal cord. In this study, we applied a deep learning-based approach using the StyleGAN model to explore patterns related to MS and predict disease progression in magnetic resonance images (MRI). METHODS We trained the StyleGAN model unsupervised using T1-weighted GRE MR images and diffusion-based ADC maps of MS patients and healthy controls. We then used the trained model to resample MR images from real input data and modified them by manipulations in the latent space to simulate MS progression. We analyzed the resulting simulation-related patterns mimicking disease progression by comparing the intensity profiles of the original and manipulated images and determined the brain parenchymal fraction (BPF). RESULTS Our results show that MS progression can be simulated by manipulating MR images in the latent space, as evidenced by brain volume loss on both T1-weighted and ADC maps and increasing lesion extent on ADC maps. CONCLUSION Overall, this study demonstrates the potential of the StyleGAN model in medical imaging to study image markers and to shed more light on the relationship between brain atrophy and MS progression through corresponding manipulations in the latent space.
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Affiliation(s)
- Daniel Güllmar
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Jena 07743, Germany; Michael Stifel Center for Data-Driven and Simulation Science, Jena 07743, Germany.
| | - Wei-Chan Hsu
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Jena 07743, Germany; Michael Stifel Center for Data-Driven and Simulation Science, Jena 07743, Germany
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Jena 07743, Germany; Michael Stifel Center for Data-Driven and Simulation Science, Jena 07743, Germany
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Casella B, Riviera W, Aldinucci M, Menegaz G. MERGE: A model for multi-input biomedical federated learning. PATTERNS (NEW YORK, N.Y.) 2023; 4:100856. [PMID: 38035188 PMCID: PMC10682752 DOI: 10.1016/j.patter.2023.100856] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 09/11/2023] [Accepted: 09/13/2023] [Indexed: 12/02/2023]
Abstract
Driven by the deep learning (DL) revolution, artificial intelligence (AI) has become a fundamental tool for many biomedical tasks, including analyzing and classifying diagnostic images. Imaging, however, is not the only source of information. Tabular data, such as personal and genomic data and blood test results, are routinely collected but rarely considered in DL pipelines. Nevertheless, DL requires large datasets that often must be pooled from different institutions, raising non-trivial privacy concerns. Federated learning (FL) is a cooperative learning paradigm that aims to address these issues by moving models instead of data across different institutions. Here, we present a federated multi-input architecture using images and tabular data as a methodology to enhance model performance while preserving data privacy. We evaluated it on two showcases: the prognosis of COVID-19 and patients' stratification in Alzheimer's disease, providing evidence of enhanced accuracy and F1 scores against single-input models and improved generalizability against non-federated models.
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Affiliation(s)
- Bruno Casella
- Department of Computer Science, University of Turin, 10149 Turin, Italy
| | - Walter Riviera
- Department of Computer Science, University of Verona, 37134 Verona, Italy
| | - Marco Aldinucci
- Department of Computer Science, University of Turin, 10149 Turin, Italy
| | - Gloria Menegaz
- Department of Engineering for Innovation Medicine, University of Verona, 37134 Verona, Italy
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Naji Y, Mahdaoui M, Klevor R, Kissani N. Artificial Intelligence and Multiple Sclerosis: Up-to-Date Review. Cureus 2023; 15:e45412. [PMID: 37854769 PMCID: PMC10581506 DOI: 10.7759/cureus.45412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/17/2023] [Indexed: 10/20/2023] Open
Abstract
Multiple sclerosis (MS) remains a challenging neurological disorder for the clinician in terms of diagnosis and management. The growing integration of AI-based algorithms in healthcare offers a golden opportunity for clinicians and patients with MS. AI models are based on statistical analyses of large quantities of data from patients including "demographics, genetics, clinical and radiological presentation." These approaches are promising in the quest for greater diagnostic accuracy, tailored management plans, and better prognostication of disease. The use of AI in multiple sclerosis represents a paradigm shift in disease management. With ongoing advancements in AI technologies and the increasing availability of large-scale datasets, the potential for further innovation is immense. As AI continues to evolve, its integration into clinical practice will play a vital role in improving diagnostics, optimizing treatment strategies, and enhancing patient outcomes for MS. This review is about conducting a literature review to identify relevant studies on AI applications in MS. Only peer-reviewed studies published in the last four years have been selected. Data related to AI techniques, advancements, and implications are extracted. Through data analysis, key themes and tendencies are identified. The review presents a cohesive synthesis of the current state of AI and MS, highlighting potential implications and new advancements.
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Affiliation(s)
- Yahya Naji
- Neurology Department, REGNE Research Laboratory, Faculty of Medicine and Pharmacy, Ibn Zohr University, Agadir, MAR
- Neurology Department, Agadir University Hospital, Agadir, MAR
| | - Mohamed Mahdaoui
- Neurology Department, University Hospital Mohammed VI, Marrakech, MAR
- Neuroscience Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech, MAR
| | - Raymond Klevor
- Neurology Department, University Hospital Mohammed VI, Marrakech, MAR
- Neuroscience Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech, MAR
| | - Najib Kissani
- Neurology Department, University Hospital Mohammed VI, Marrakech, MAR
- Neuroscience Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech, MAR
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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.
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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.
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