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3. Explainable artificial intelligence toward usable and trustworthy computer-aided early diagnosis of multiple sclerosis from Optical Coherence Tomography

Author

Hernandez, Monica, Ramon-Julvez, Ubaldo, Vilades, Elisa, Cordon, Beatriz, Mayordomo, Elvira, and Garcia-Martin, Elena

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
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.

Published
2023

8. Artificial intelligence extension of the OSCAR‐IB criteria

Author

Petzold, Axel, Albrecht, Philipp, Balcer, Laura, Bekkers, Erik, Brandt, Alexander U, Calabresi, Peter A, Deborah, Orla Galvin, Graves, Jennifer S, Green, Ari, Keane, Pearse A, Bijvank, Jenny A Nij, Sander, Josemir W, Paul, Friedemann, Saidha, Shiv, Villoslada, Pablo, Wagner, Siegfried K, Yeh, E Ann, Aktas, Orhan, Antel, Jack, Asgari, Nasrin, Audo, Isabelle, Avasarala, Jagannadha, Avril, Daly, Bagnato, Francesca R, Banwell, Brenda, Bar‐Or, Amit, Behbehani, Raed, Manterola, Arnaldo Belzunce, Bennett, Jeffrey, Benson, Leslie, Bernard, Jacqueline, Bremond‐Gignac, Dominique, Britze, Josefine, Burton, Jodie, Calkwood, Jonathan, Carroll, William, Chandratheva, Arvind, Cohen, Jeffrey, Comi, Giancarlo, Cordano, Christian, Costa, Silvana, Costello, Fiona, Courtney, Ardith, Cruz‐Herranz, Anes, Cutter, Gary, Crabb, David, Delott, Lindsey, De Seze, Jerome, Diem, Ricarda, Dollfuss, Helene, Ayoubi, Nabil K El, Fasser, Christina, Finke, Carsten, Fischer, Dominik, Fitzgerald, Kathryn, Fonseca, Pedro, Frederiksen, Jette L, Frohman, Elliot, Frohman, Teresa, Fujihara, Kazuo, Cuellar, Iñigo Gabilondo, Galetta, Steven, Garcia‐Martin, Elena, Giovannoni, Gavin, Glebauskiene, Brigita, Suárez, Inés González, Jensen, Gorm Pihl, Hamann, Steffen, Hartung, Hans‐Peter, Havla, Joachim, Hemmer, Bernhard, Huang, Su‐Chun, Imitola, Jaime, Jasinskas, Vytautas, Jiang, Hong, Kafieh, Rahele, Kappos, Ludwig, Kardon, Randy, Keegan, David, Kildebeck, Eric, Kim, Ungsoo Samuel, Klistorner, Sasha, Knier, Benjamin, Kolbe, Scott, Korn, Thomas, Krupp, Lauren, Lagrèze, Wolf, Leocani, Letizia, Levin, Netta, Liskova, Petra, Preiningerova, Jana Lizrova, Lorenz, Birgit, May, Eugene, Miller, David, Mikolajczak, Janine, Saïd, Saddek Mohand, Montalban, Xavier, Morrow, Mark, Mowry, Ellen, and Murta, Joaquim

Subjects
Biomedical and Clinical Sciences, Neurosciences, Bioengineering, Biomedical Imaging, Autoimmune Disease, Neurodegenerative, Aging, Brain Disorders, Neurological, Algorithms, Artificial Intelligence, Big Data, Cohort Studies, Humans, Nervous System Diseases, Retina, Tomography, Optical Coherence, IMSVISUAL, ERN-EYE Consortium, Clinical Sciences, Clinical and health psychology
Abstract

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.

Published
2021

16. Exome-wide rare variant analysis in familial essential tremor

Author

Diez-Fairen, Monica, Houle, Gabrielle, Ortega-Cubero, Sara, Bandres-Ciga, Sara, Alvarez, Ignacio, Carcel, Maria, Ibañez, Laura, Fernandez, Maria Victoria, Budde, John P., Trotta, Jean-Rémi, Tonda, Raúl, Chong, Jessica X., Bamshad, Michael J., Nickerson, Deborah A., Aguilar, Miquel, Tartari, Juan P., Gironell, Alexandre, García-Martín, Elena, Agundez, Jose AG., Alonso-Navarro, Hortensia, Jimenez-Jimenez, Felix Javier, Fernandez, Manel, Valldeoriola, Francesc, Marti, Maria Jose, Tolosa, Eduard, Coria, Francisco, Pastor, Maria A., Vilariño-Güell, Carles, Rajput, Alex, Dion, Patrick A., Cruchaga, Carlos, Rouleau, Guy A., and Pastor, Pau

Published
2021
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17. Individual Prognostication of Disease Activity and Disability Worsening in Multiple Sclerosis With Retinal Layer Thickness z Scores.

Author

Ting-Yi Lin, Motamedi, Seyedamirhosein, Asseyer, Susanna, Chien, Claudia, Saidha, Shiv, Calabresi, Peter A., Fitzgerald, Kathryn C., Samadzadeh, Sara, Villoslada, Pablo, Llufriu, Sara, Green, Ari J., Lizrova Preiningerova, Jana, Petzold, Axel, Leocani, Letizia, Garcia-Martin, Elena, Oreja-Guevara, Celia, Outteryck, Olivier, Vermersch, Patrick, Balcer, Laura J., and Kenney, Rachel

Published
2024
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18. 3D Point Cloud Analysis via Transformer-Based Graph Learning for Multiple Sclerosis Screening in OCT Images.

Author

Álvarez-Rodríguez, Lorena, Prego, Iván García, de Moura, Joaquim, Pueyo, Ana, Vilades, Elisa, Garcia-Martin, Elena, Sánchez, Clara I., Novo, Jorge, and Ortega, Marcos

Subjects
GRAPH neural networks, OPTICAL coherence tomography, TRANSFORMER models, POINT cloud, YOUNG adults
Abstract

Multiple Sclerosis (MS), the leading cause of non-traumatic neurological impairment in young adults, manifests morphological changes in the retina observable in Optical Coherence Tomography (OCT) images. These changes in the Retinal Nerve Fibre Layer (RNFL) and the Ganglion Cell Layer - Bruch's Membrane (GCL-BM) serve as potential computational biomarkers for MS. In this work, we propose a transformer-based graph learning approach for analyzing 3D point clouds generated from RNFL and GCL-BM contours, marking a first in the application of geometric deep learning (GDL) to MS diagnosis via OCT scans. Our proposal, tailored for efficiency, synergizes the global contextual strengths of transformers with the detailed, structure-aware capabilities of graph neural networks. Such integration allows for the nuanced analysis of complex retinal structures, significantly boosting the precision of MS detection by uncovering patterns not discernible to the human eye. Additionally, we conducted a comprehensive study on the optimal downsampling size of input 3D point clouds, ensuring efficient data processing without compromising diagnostic accuracy. Our optimal configuration achieved a test F1-Score of 0.88, using only 4.0% of total 3D points, showcasing the effectiveness of our method despite the higher computational demands compared to less complex, albeit less precise, configurations. These promising results are the first in the study of 3D analysis and transformer-based geometric deep learning for MS screening based on OCT images, which are revolutionizing neurophtalmological research. [ABSTRACT FROM AUTHOR]

Published
2024
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21. Immune Analysis Using Vitreous Optical Coherence Tomography Imaging in Rats with Steroid-Induced Glaucoma

Author

Rodrigo, Maria J., primary, Subías, Manuel, additional, Montolío, Alberto, additional, Martínez-Rincón, Teresa, additional, Aragón-Navas, Alba, additional, Bravo-Osuna, Irene, additional, Pablo, Luis E., additional, Cegoñino, Jose, additional, Herrero-Vanrell, Rocío, additional, Garcia-Martin, Elena, additional, and Pérez del Palomar, Amaya, additional

Published
2024
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25. The macular retinal ganglion cell layer as a biomarker for diagnosis and prognosis in multiple sclerosis: A deep learning approach.

Author

Montolío, Alberto, Cegoñino, José, Garcia‐Martin, Elena, and Pérez del Palomar, Amaya

Subjects
RETINAL ganglion cells, ARTIFICIAL neural networks, DEEP learning, MULTIPLE sclerosis, OPTICAL coherence tomography, RETINAL blood vessels, MACULA lutea
Abstract

Purpose: The macular ganglion cell layer (mGCL) is a strong potential biomarker of axonal degeneration in multiple sclerosis (MS). For this reason, this study aims to develop a computer‐aided method to facilitate diagnosis and prognosis in MS. Methods: This paper combines a cross‐sectional study of 72 MS patients and 30 healthy control subjects for diagnosis and a 10‐year longitudinal study of the same MS patients for the prediction of disability progression, during which the mGCL was measured using optical coherence tomography (OCT). Deep neural networks were used as an automatic classifier. Results: For MS diagnosis, greatest accuracy (90.3%) was achieved using 17 features as inputs. The neural network architecture comprised the input layer, two hidden layers and the output layer with softmax activation. For the prediction of disability progression 8 years later, accuracy of 81.9% was achieved with a neural network comprising two hidden layers and 400 epochs. Conclusion: We present evidence that by applying deep learning techniques to clinical and mGCL thickness data it is possible to identify MS and predict the course of the disease. This approach potentially constitutes a non‐invasive, low‐cost, easy‐to‐implement and effective method. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Chronic Glaucoma Induced in Rats by a Single Injection of Fibronectin-Loaded PLGA Microspheres: IOP-Dependent and IOP-Independent Neurodegeneration

Author

Munuera, Ines, primary, Aragon-Navas, Alba, additional, Villacampa, Pilar, additional, Gonzalez-Cela, Miriam A., additional, Subías, Manuel, additional, Pablo, Luis E., additional, Garcia-Feijoo, Julian, additional, Herrero-Vanrell, Rocio, additional, Garcia-Martin, Elena, additional, Bravo-Osuna, Irene, additional, and Rodrigo, Maria J., additional

Published
2023
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28. Differential Study of Retinal Thicknesses in the Eyes of Alzheimer’s Patients, Multiple Sclerosis Patients and Healthy Subjects

Author

Garcia-Martin, Elena, primary, Jimeno-Huete, Daniel, additional, Dongil-Moreno, Francisco J., additional, Boquete, Luciano, additional, Sánchez-Morla, Eva M., additional, Miguel-Jiménez, Juan M., additional, López-Dorado, Almudena, additional, Vilades, Elisa, additional, Fuertes, Maria I., additional, Pueyo, Ana, additional, and Ortiz del Castillo, Miguel, additional

Published
2023
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30. The OSCAR-MP Consensus Criteria for Quality Assessment of Retinal Optical Coherence Tomography Angiography

Author

Wicklein, Rebecca, primary, Yam, Charmaine, additional, Noll, Christina, additional, Aly, Lilian, additional, Banze, Nicolas, additional, Romahn, Eva Feodora, additional, Wolf, Elisabeth, additional, Hemmer, Bernhard, additional, Oertel, Frederike C., additional, Zimmermann, Hanna, additional, Albrecht, Philipp, additional, Ringelstein, Marius, additional, Baumann, Carmen, additional, Feucht, Nikolaus, additional, Penkava, Josef, additional, Havla, Joachim, additional, Gernert, Jonathan A., additional, Mardin, Christian, additional, Vasileiou, Eleni S., additional, Van Der Walt, Anneke, additional, Al-Louzi, Omar, additional, Cabello, Sergio, additional, Vidal-Jordana, Angela, additional, Krämer, Julia, additional, Wiendl, Heinz, additional, Preiningerova, Jana Lizrova, additional, Ciccarelli, Olga, additional, Garcia-Martin, Elena, additional, Kana, Veronika, additional, Calabresi, Peter A., additional, Paul, Friedemann, additional, Saidha, Shiv, additional, Petzold, Axel, additional, Toosy, Ahmed T., additional, and Knier, Benjamin, additional

Published
2023
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44. Chronic Glaucoma Induced in Rats by a Single Injection of Fibronectin-Loaded PLGA Microspheres: IOP-Dependent and IOP-Independent Neurodegeneration.

Author

Munuera, Ines, Aragon-Navas, Alba, Villacampa, Pilar, Gonzalez-Cela, Miriam A., Subías, Manuel, Pablo, Luis E., Garcia-Feijoo, Julian, Herrero-Vanrell, Rocio, Garcia-Martin, Elena, Bravo-Osuna, Irene, and Rodrigo, Maria J.

Subjects
FIBRONECTINS, OPTICAL coherence tomography, MICROSPHERES, INTRAOCULAR pressure, INJECTIONS, GLAUCOMA
Abstract

To evaluate a new animal model of chronic glaucoma induced using a single injection of fibronectin-loaded biodegradable PLGA microspheres (Ms) to test prolonged therapies. 30 rats received a single injection of fibronectin-PLGA-Ms suspension (MsF) in the right eye, 10 received non-loaded PLGA-Ms suspension (Control), and 17 were non-injected (Healthy). Follow-up was performed (24 weeks), evaluating intraocular pressure (IOP), optical coherence tomography (OCT), histology and electroretinography. The right eyes underwent a progressive increase in IOP, but only induced cohorts reached hypertensive values. The three cohorts presented a progressive decrease in ganglion cell layer (GCL) thickness, corroborating physiological age-related loss of ganglion cells. Injected cohorts (MsF > Control) presented greater final GCL thickness. Histological exams explain this paradox: the MsF cohort showed lower ganglion cell counts but higher astrogliosis and immune response. A sequential trend of functional damage was recorded using scotopic electroretinography (MsF > Control > Healthy). It seems to be a function–structure correlation: in significant astrogliosis, early functional damage can be detected by electroretinography, and structural damage can be detected by histological exams but not by OCT. Males presented higher IOP and retinal and GCL thicknesses and lower electroretinography. A minimally invasive chronic glaucoma model was induced by a single injection of biodegradable Ms. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Retinal thickness measured with optical coherence tomography and risk of disability worsening in multiple sclerosis: a cohort study

Author

Martinez-Lapiscina, Elena H, Arnow, Sam, Wilson, James A, Saidha, Shiv, Preiningerova, Jana Lizrova, Oberwahrenbrock, Timm, Brandt, Alexander U, Pablo, Luis E, Guerrieri, Simone, Gonzalez, Ines, Outteryck, Olivier, Mueller, Ann-Kristin, Albrecht, Phillip, Chan, Wesley, Lukas, Sebastian, Balk, Lisanne J, Fraser, Clare, Frederiksen, Jette L, Resto, Jennifer, Frohman, Teresa, Cordano, Christian, Zubizarreta, Irati, Andorra, Magi, Sanchez-Dalmau, Bernardo, Saiz, Albert, Bermel, Robert, Klistorner, Alexander, Petzold, Axel, Schippling, Sven, Costello, Fiona, Aktas, Orhan, Vermersch, Patrick, Oreja-Guevara, Celia, Comi, Giancarlo, Leocani, Letizia, Garcia-Martin, Elena, Paul, Friedemann, Havrdova, Eva, Frohman, Elliot, Balcer, Laura J, Green, Ari J, Calabresi, Peter A, and Villoslada, Pablo

Published
2016
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