Using artificial intelligence models to evaluate envisaged points initially: A pilot study

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dc.authorid0000-0003-0176-8970
dc.contributor.authorAmasya, Hakan
dc.contributor.authorAydoğan, Turgay
dc.contributor.authorCesur, Emre
dc.contributor.authorKemaloğlu Alagöz, Nazan
dc.contributor.authorUğurlu, Mehmet
dc.contributor.authorBayrakdar, İbrahim Şevki
dc.contributor.authorOrhan, Kaan
dc.date.accessioned2023-07-06T08:01:35Z
dc.date.available2023-07-06T08:01:35Z
dc.date.issued2023
dc.departmentİstanbul Medipol Üniversitesi, Diş Hekimliği Fakültesi, Ortodonti Ana Bilim Dalı
dc.description.abstractThe morphology of the finger bones in hand-wrist radiographs (HWRs) can be considered as a radiological skeletal maturity indicator, along with the other indicators. This study aims to validate the anatomical landmarks envisaged to be used for classification of the morphology of the phalanges, by developing classical neural network (NN) classifiers based on a sub-dataset of 136 HWRs. A web-based tool was developed and 22 anatomical landmarks were labeled on four region of interests (proximal (PP3), medial (MP3), distal (DP3) phalanges of the third and medial phalanx (MP5) of the fifth finger) and the epiphysis-diaphysis relationships were saved as "narrow,'' "equal,'' "capping'' or "fusion'' by three observers. In each region, 18 ratios and 15 angles were extracted using anatomical points. The data set is analyzed by developing two NN classifiers, without (NN-1) and with (NN-2) the 5-fold cross-validation. The performance of the models was evaluated with percentage of agreement, Cohen's (c kappa) and Weighted (w kappa) Kappa coefficients, precision, recall, F1-score and accuracy (statistically significance: p < 0.05). Method error was found to be in the range of ck: 0.7-1. Overall classification performance of the models was changed between 82.14% and 89.29%. On average, performance of the NN-1 and NN-2 models were found to be 85.71% and 85.52%, respectively. The ck and wk of the NN-1 model were changed between 20.08 (p > 0.05) and 0.91 among regions. The average performance was found to be promising except the regions without adequate samples and the anatomical points are validated to be used in the future studies, initially.
dc.identifier.citationAmasya, H., Aydoğan, T., Cesur, E., Kemaloğlu Alagöz, N., Uğurlu, M., Bayrakdar, İ. Ş. ... Orhan, K. (2023). Using artificial intelligence models to evaluate envisaged points initially: A pilot study. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 237(6), 706-718. https://dx.doi.org/10.1177/09544119231173165
dc.identifier.doi10.1177/09544119231173165
dc.identifier.endpage718
dc.identifier.issn0954-4119
dc.identifier.issn2041-3033
dc.identifier.issue6
dc.identifier.pmid37211725
dc.identifier.scopus2-s2.0-85162971695
dc.identifier.scopusqualityQ3
dc.identifier.startpage706
dc.identifier.urihttps://dx.doi.org/10.1177/09544119231173165
dc.identifier.urihttps://hdl.handle.net/20.500.12511/11151
dc.identifier.volume237
dc.identifier.wos001001430600001en_US
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorCesur, Emre
dc.language.isoen
dc.publisherSAGE Publications Ltd
dc.relation.ispartofProceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial Intelligence
dc.subjectMachine Learning
dc.subjectAge Determination by Skeleton
dc.subjectRadiology
dc.subjectHand-Wrist
dc.titleUsing artificial intelligence models to evaluate envisaged points initially: A pilot study
dc.typeArticle

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