Multi-parameter-based radiological diagnosis of Chiari Malformation using Machine Learning Technology

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dc.authoridGÜLDOĞAN, Emek/0000-0002-5436-8164
dc.authoridPAŞAHAN, RAMAZAN/0000-0002-3221-1422
dc.authoriddurak, mehmet akif akif/0000-0003-0827-2708
dc.authoridtetik, bora/0000-0001-7696-7785
dc.authoridOnal, Selami Cagatay/0000-0002-1216-2301
dc.authorwosidSaraç, Kaya/ABI-1091-2020
dc.authorwosidGÜLDOĞAN, Emek/ABH-5460-2020
dc.authorwosidPAŞAHAN, RAMAZAN/AAB-3576-2021
dc.authorwosiddurak, mehmet akif akif/ABI-1169-2020
dc.authorwosidtetik, bora/AAA-8841-2021
dc.authorwosidYıldırım, İsmail Okan/AFR-8243-2022
dc.contributor.authorTetik, Bora
dc.contributor.authorDogan, Gulec Mert
dc.contributor.authorPasahan, Ramazan
dc.contributor.authorDurak, Mehmet Akif
dc.contributor.authorGuldogan, Emek
dc.contributor.authorSarac, Kaya
dc.contributor.authorOnal, Cagatay
dc.date.accessioned2024-08-04T20:50:36Z
dc.date.available2024-08-04T20:50:36Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground The known primary radiological diagnosis of Chiari Malformation-I (CM-I) is based on the degree of tonsillar herniation (TH) below the Foramen Magnum (FM). However, recent data also shows the association of such malformation with smaller posterior cranial fossa (PCF) volume and the anatomical issues regarding the Odontoid. This study presents the achieved result regarding some detected potential radiological findings that may aid CM-I diagnosis using several machine learning (ML) algorithms. Materials and Methods Midsagittal T1-weighted MR images were collected in 241 adult patients diagnosed with CM, eleven morphometric measures of the posterior cerebral fossa were performed. Patients whose imaging was performed in the same centre and on the same device were included in the study. By matching age and gender, radiological exams of 100 clinically/radiologically proven symptomatic CM-I cases and 100 healthy controls were assessed. Eleven morphometric measures of the posterior cerebral fossa were examined using 5 designed ML algorithms. Results The mean age of patients was 29.92 +/- 15.03 years. The primary presenting symptoms were headaches (62%). Syringomyelia and retrocurved-odontoid were detected in 34% and 8% of patients, respectively. All of the morphometric measures were significantly different between the groups, except for the distance from the dens axis to the posterior margin of FM. The Radom Forest model is found to have the best 1.0 (14 of 14) ratio of accuracy in regard to 14 different combinations of morphometric features. Conclusion Our study indicates the potential usefulness of ML-guided PCF measurements, other than TH, that may be used to predict and diagnose CM-I accurately. Combining two or three preferable osseous structure-based measurements may increase the accuracy of radiological diagnosis of CM-I.en_US
dc.identifier.doi10.1111/ijcp.14746
dc.identifier.issn1368-5031
dc.identifier.issn1742-1241
dc.identifier.issue11en_US
dc.identifier.pmid34428317en_US
dc.identifier.scopus2-s2.0-85113799322en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1111/ijcp.14746
dc.identifier.urihttps://hdl.handle.net/11616/100171
dc.identifier.volume75en_US
dc.identifier.wosWOS:000690793100001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherWiley-Hindawien_US
dc.relation.ispartofInternational Journal of Clinical Practiceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPosterior-Fossa Decompressionen_US
dc.subjectI Malformationen_US
dc.subjectMorphometric-Analysisen_US
dc.subjectPredictionen_US
dc.subjectDuraplastyen_US
dc.subjectOutcomesen_US
dc.subjectAdultsen_US
dc.titleMulti-parameter-based radiological diagnosis of Chiari Malformation using Machine Learning Technologyen_US
dc.typeArticleen_US

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