Diagnosis of Multiple Sclerosis using Optical Coherence Tomography Supported by Explainable Artificial Intelligence Article Swipe
Elena García‐Martín
,
Francisco J. Dongil-Moreno
,
Miguel Ortiz del Castillo
,
Olga Ciubotaru
,
Luciano Boquete
,
Eva María Sánchez‐Morla
,
Daniel Jimeno-Huete
,
Juan Manuel Miguel
,
Rafael Barea
,
Elisa Viladés
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3168667/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3168667/v1
Background/Objectives: Study of retinal structure based on optical coherence tomography (OCT) data can facilitate early diagnosis of relapsing-remitting multiple sclerosis (RRMS). Although artificial intelligence can provide highly reliable diagnoses, the results obtained must be explainable. Subjects/Methods: The study included 79 recently diagnosed RRMS patients and 69 age matched healthy control subjects. Thickness (Avg) and inter-eye difference (Diff) features are obtained in 4 retinal layers using the posterior pole protocol. Each layer is divided into 6 analysis zones. The Support Vector Machine plus Recursive Feature Elimination with Leave-One-Out Cross Validation (SVM-RFE-LOOCV) approach is used to find the subset of features that reduces dimensionality and optimizes the performance of the classifier. Results SVM-RFE-LOOCV was used to identify OCT features with greatest capacity for early diagnosis, determining the area of the papillomacular bundle to be the most influential. A correlation was observed between loss of layer thickness and increase in functional disability. There was also greater functional deterioration in patients with greater asymmetry between left and right eyes. The classifier based on the top-ranked features obtained sensitivity = 0.86 and specificity = 0.90. Conclusions There was consistency between the features identified as relevant by the SVM-RFE-LOOCV approach and the retinotopic distribution of the retinal nerve fibers and the optic nerve head. This simple method contributes to implementation of an assisted diagnosis system and its accuracy exceeds that achieved with magnetic resonance imaging of the central nervous system, the current gold standard. This paper provides novel insights into RRMS affectation of the neuroretina.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-3168667/v1
- https://www.researchsquare.com/article/rs-3168667/latest.pdf
- OA Status
- gold
- Cited By
- 4
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385749420
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385749420Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-3168667/v1Digital Object Identifier
- Title
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Diagnosis of Multiple Sclerosis using Optical Coherence Tomography Supported by Explainable Artificial IntelligenceWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-08-11Full publication date if available
- Authors
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Elena García‐Martín, Francisco J. Dongil-Moreno, Miguel Ortiz del Castillo, Olga Ciubotaru, Luciano Boquete, Eva María Sánchez‐Morla, Daniel Jimeno-Huete, Juan Manuel Miguel, Rafael Barea, Elisa ViladésList of authors in order
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https://doi.org/10.21203/rs.3.rs-3168667/v1Publisher landing page
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https://www.researchsquare.com/article/rs-3168667/latest.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-3168667/latest.pdfDirect OA link when available
- Concepts
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Optical coherence tomography, Multiple sclerosis, Coherence (philosophical gambling strategy), Artificial intelligence, Tomography, Computer science, Medicine, Physics, Radiology, Psychiatry, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 1, 2023: 1Per-year citation counts (last 5 years)
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22Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.highly | 27 |
| abstract_inverted_index.layers | 64 |
| abstract_inverted_index.method | 211 |
| abstract_inverted_index.simple | 210 |
| abstract_inverted_index.subset | 97 |
| abstract_inverted_index.system | 219 |
| abstract_inverted_index.zones. | 77 |
| abstract_inverted_index.(RRMS). | 21 |
| abstract_inverted_index.Feature | 84 |
| abstract_inverted_index.Machine | 81 |
| abstract_inverted_index.Results | 110 |
| abstract_inverted_index.Support | 79 |
| abstract_inverted_index.between | 140, 161, 185 |
| abstract_inverted_index.central | 232 |
| abstract_inverted_index.control | 50 |
| abstract_inverted_index.current | 236 |
| abstract_inverted_index.divided | 73 |
| abstract_inverted_index.exceeds | 223 |
| abstract_inverted_index.greater | 153, 159 |
| abstract_inverted_index.healthy | 49 |
| abstract_inverted_index.imaging | 229 |
| abstract_inverted_index.matched | 48 |
| abstract_inverted_index.nervous | 233 |
| abstract_inverted_index.optical | 8 |
| abstract_inverted_index.provide | 26 |
| abstract_inverted_index.reduces | 101 |
| abstract_inverted_index.results | 31 |
| abstract_inverted_index.retinal | 4, 63, 201 |
| abstract_inverted_index.system, | 234 |
| abstract_inverted_index.Although | 22 |
| abstract_inverted_index.accuracy | 222 |
| abstract_inverted_index.achieved | 225 |
| abstract_inverted_index.analysis | 76 |
| abstract_inverted_index.approach | 91, 194 |
| abstract_inverted_index.assisted | 217 |
| abstract_inverted_index.capacity | 120 |
| abstract_inverted_index.features | 58, 99, 117, 172, 187 |
| abstract_inverted_index.greatest | 119 |
| abstract_inverted_index.identify | 115 |
| abstract_inverted_index.included | 39 |
| abstract_inverted_index.increase | 146 |
| abstract_inverted_index.insights | 243 |
| abstract_inverted_index.magnetic | 227 |
| abstract_inverted_index.multiple | 19 |
| abstract_inverted_index.observed | 139 |
| abstract_inverted_index.obtained | 32, 60, 173 |
| abstract_inverted_index.patients | 44, 157 |
| abstract_inverted_index.provides | 241 |
| abstract_inverted_index.recently | 41 |
| abstract_inverted_index.relevant | 190 |
| abstract_inverted_index.reliable | 28 |
| abstract_inverted_index.Recursive | 83 |
| abstract_inverted_index.Thickness | 52 |
| abstract_inverted_index.asymmetry | 160 |
| abstract_inverted_index.coherence | 9 |
| abstract_inverted_index.diagnosed | 42 |
| abstract_inverted_index.diagnosis | 16, 218 |
| abstract_inverted_index.inter-eye | 55 |
| abstract_inverted_index.optimizes | 104 |
| abstract_inverted_index.posterior | 67 |
| abstract_inverted_index.protocol. | 69 |
| abstract_inverted_index.resonance | 228 |
| abstract_inverted_index.sclerosis | 20 |
| abstract_inverted_index.standard. | 238 |
| abstract_inverted_index.structure | 5 |
| abstract_inverted_index.subjects. | 51 |
| abstract_inverted_index.thickness | 144 |
| abstract_inverted_index.Validation | 89 |
| abstract_inverted_index.artificial | 23 |
| abstract_inverted_index.classifier | 167 |
| abstract_inverted_index.diagnoses, | 29 |
| abstract_inverted_index.diagnosis, | 123 |
| abstract_inverted_index.difference | 56 |
| abstract_inverted_index.facilitate | 14 |
| abstract_inverted_index.functional | 148, 154 |
| abstract_inverted_index.identified | 188 |
| abstract_inverted_index.tomography | 10 |
| abstract_inverted_index.top-ranked | 171 |
| abstract_inverted_index.Conclusions | 181 |
| abstract_inverted_index.Elimination | 85 |
| abstract_inverted_index.affectation | 246 |
| abstract_inverted_index.classifier. | 109 |
| abstract_inverted_index.consistency | 184 |
| abstract_inverted_index.contributes | 212 |
| abstract_inverted_index.correlation | 137 |
| abstract_inverted_index.determining | 124 |
| abstract_inverted_index.disability. | 149 |
| abstract_inverted_index.performance | 106 |
| abstract_inverted_index.retinotopic | 197 |
| abstract_inverted_index.sensitivity | 174 |
| abstract_inverted_index.specificity | 178 |
| abstract_inverted_index.distribution | 198 |
| abstract_inverted_index.explainable. | 35 |
| abstract_inverted_index.influential. | 135 |
| abstract_inverted_index.intelligence | 24 |
| abstract_inverted_index.neuroretina. | 249 |
| abstract_inverted_index.Leave-One-Out | 87 |
| abstract_inverted_index.SVM-RFE-LOOCV | 111, 193 |
| abstract_inverted_index.deterioration | 155 |
| abstract_inverted_index.dimensionality | 102 |
| abstract_inverted_index.implementation | 214 |
| abstract_inverted_index.papillomacular | 129 |
| abstract_inverted_index.(SVM-RFE-LOOCV) | 90 |
| abstract_inverted_index.Subjects/Methods: | 36 |
| abstract_inverted_index.relapsing-remitting | 18 |
| abstract_inverted_index.Background/Objectives: | 1 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile.value | 0.77768802 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |