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

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dc.authoridOrhan, Kaan/0000-0001-6768-0176
dc.authoridBAYRAKDAR, Ibrahim Sevki/0000-0001-5036-9867
dc.authoridAmasya, Hakan/0000-0001-7400-9938
dc.contributor.authorAmasya, Hakan
dc.contributor.authorAydogan, Turgay
dc.contributor.authorCesur, Emre
dc.contributor.authorKemaloglu Alagoz, Nazan
dc.contributor.authorUgurlu, Mehmet
dc.contributor.authorBayrakdar, Ibrahim Sevki
dc.contributor.authorOrhan, Kaan
dc.date.accessioned2025-01-21T16:55:49Z
dc.date.available2025-01-21T16:55:49Z
dc.date.issued2023
dc.departmentKırıkkale Üniversitesi
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.doi10.1177/09544119231173165
dc.identifier.endpage718
dc.identifier.issn0954-4119
dc.identifier.issn2041-3033
dc.identifier.issue6
dc.identifier.pmid37211725
dc.identifier.startpage706
dc.identifier.urihttps://doi.org/10.1177/09544119231173165
dc.identifier.urihttps://hdl.handle.net/20.500.12587/25851
dc.identifier.volume237
dc.identifier.wosWOS:001001430600001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSage Publications Ltd
dc.relation.ispartofProceedings of The Institution of Mechanical Engineers Part H-Journal of Engineering In Medicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectArtificial intelligence; machine learning; age determination by skeleton; radiology; hand-wrist
dc.titleUsing artificial intelligence models to evaluate envisaged points initially: A pilot study
dc.typeArticle

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