Machine learning classification of multiple sclerosis in children using optical coherence tomography
BACKGROUND: In children, multiple sclerosis (MS) is the ultimate diagnosis in only 1/5 to 1/3 of cases after a first episode of central nervous system (CNS) demyelination. As the visual pathway is frequently affected in MS and other CNS demyelinating disorders (DDs), structural retinal imaging such as optical coherence tomography (OCT) can be used to differentiate MS.
OBJECTIVE: This study aimed to investigate the utility of machine learning (ML) based on OCT features to identify distinct structural retinal features in children with DDs.
METHODS: This study included 512 eyes from 187 (neyes = 374) children with demyelinating diseases and 69 (neyes = 138) controls. Input features of the analysis comprised of 24 auto-segmented OCT features.
RESULTS: Random Forest classifier with recursive feature elimination yielded the highest predictive values and identified DDs with 75% and MS with 80% accuracy, while multiclass distinction between MS and monophasic DD was performed with 64% accuracy. A set of eight retinal features were identified as the most important features in this classification.
CONCLUSION: This study demonstrates that ML based on OCT features can be used to support a diagnosis of MS in children.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:28 |
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| Enthalten in: |
Multiple sclerosis (Houndmills, Basingstoke, England) - 28(2022), 14 vom: 14. Dez., Seite 2253-2262 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ciftci Kavaklioglu, Beyza [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 22.11.2022 Date Revised 19.12.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1177/13524585221112605 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM34465463X |
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| 100 | 1 | |a Ciftci Kavaklioglu, Beyza |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Machine learning classification of multiple sclerosis in children using optical coherence tomography |
| 264 | 1 | |c 2022 | |
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| 500 | |a Date Completed 22.11.2022 | ||
| 500 | |a Date Revised 19.12.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a BACKGROUND: In children, multiple sclerosis (MS) is the ultimate diagnosis in only 1/5 to 1/3 of cases after a first episode of central nervous system (CNS) demyelination. As the visual pathway is frequently affected in MS and other CNS demyelinating disorders (DDs), structural retinal imaging such as optical coherence tomography (OCT) can be used to differentiate MS | ||
| 520 | |a OBJECTIVE: This study aimed to investigate the utility of machine learning (ML) based on OCT features to identify distinct structural retinal features in children with DDs | ||
| 520 | |a METHODS: This study included 512 eyes from 187 (neyes = 374) children with demyelinating diseases and 69 (neyes = 138) controls. Input features of the analysis comprised of 24 auto-segmented OCT features | ||
| 520 | |a RESULTS: Random Forest classifier with recursive feature elimination yielded the highest predictive values and identified DDs with 75% and MS with 80% accuracy, while multiclass distinction between MS and monophasic DD was performed with 64% accuracy. A set of eight retinal features were identified as the most important features in this classification | ||
| 520 | |a CONCLUSION: This study demonstrates that ML based on OCT features can be used to support a diagnosis of MS in children | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Multiple sclerosis | |
| 650 | 4 | |a optical coherence tomography | |
| 650 | 4 | |a pediatric | |
| 650 | 4 | |a retinal nerve fiber layer thickness | |
| 650 | 4 | |a supervised learning | |
| 700 | 1 | |a Erdman, Lauren |e verfasserin |4 aut | |
| 700 | 1 | |a Goldenberg, Anna |e verfasserin |4 aut | |
| 700 | 1 | |a Kavaklioglu, Can |e verfasserin |4 aut | |
| 700 | 1 | |a Alexander, Cara |e verfasserin |4 aut | |
| 700 | 1 | |a Oppermann, Hannah M |e verfasserin |4 aut | |
| 700 | 1 | |a Patel, Amish |e verfasserin |4 aut | |
| 700 | 1 | |a Hossain, Soaad |e verfasserin |4 aut | |
| 700 | 1 | |a Berenbaum, Tara |e verfasserin |4 aut | |
| 700 | 1 | |a Yau, Olivia |e verfasserin |4 aut | |
| 700 | 1 | |a Yea, Carmen |e verfasserin |4 aut | |
| 700 | 1 | |a Ly, Mina |e verfasserin |4 aut | |
| 700 | 1 | |a Costello, Fiona |e verfasserin |4 aut | |
| 700 | 1 | |a Mah, Jean K |e verfasserin |4 aut | |
| 700 | 1 | |a Reginald, Arun |e verfasserin |4 aut | |
| 700 | 1 | |a Banwell, Brenda |e verfasserin |4 aut | |
| 700 | 1 | |a Longoni, Giulia |e verfasserin |4 aut | |
| 700 | 1 | |a Ann Yeh, E |e verfasserin |4 aut | |
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