Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic-Random Forest

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dc.contributor.authorUçan, Gülfem Özlü
dc.contributor.authorGwassi, Omar Abboosh Hussein
dc.contributor.authorApaydın, Burak Kerem
dc.contributor.authorUçan, Bahadır
dc.date.accessioned2025-03-23T11:40:17Z
dc.date.available2025-03-23T11:40:17Z
dc.date.issued2025
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
dc.description.abstractBackground/Objectives: Dental age estimation is a vital component of forensic science, helping to determine the identity and actual age of an individual. However, its effectiveness is challenged by methodological variability and biological differences between individuals. Therefore, to overcome the drawbacks such as the dependence on manual measurements, requiring a lot of time and effort, and the difficulty of routine clinical application due to large sample sizes, we aimed to automatically estimate tooth age from panoramic radiographs (OPGs) using artificial intelligence (AI) algorithms. Methods: Two-Dimensional Deep Convolutional Neural Network (2D-DCNN) and One-Dimensional Deep Convolutional Neural Network (1D-DCNN) techniques were used to extract features from panoramic radiographs and patient records. To perform age estimation using feature information, Genetic algorithm (GA) and Random Forest algorithm (RF) were modified, combined, and defined as Modified Genetic-Random Forest Algorithm (MG-RF). The performance of the system used in our study was analyzed based on the MSE, MAE, RMSE, and R2 values calculated during the implementation of the code. Results: As a result of the applied algorithms, the MSE value was 0.00027, MAE value was 0.0079, RMSE was 0.0888, and R2 score was 0.999. Conclusions: The findings of our study indicate that the AI-based system employed herein is an effective tool for age detection. Consequently, we propose that this technology could be utilized in forensic sciences in the future.
dc.identifier.citationOzlu Ucan, G., Gwassi, O. A. H., Apaydin, B. K., & Ucan, B. (2025). Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic–Random Forest. Diagnostics, 15(3), 314.
dc.identifier.doi10.3390/diagnostics15030314
dc.identifier.issn2075-4418
dc.identifier.issue3
dc.identifier.pmid39941244
dc.identifier.scopus2-s2.0-85217526556
dc.identifier.trdizinidWOS:001419503700001
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5733
dc.identifier.volume15
dc.identifier.wosqualityQ1
dc.indekslendigikaynakPubMed
dc.institutionauthorGwassi, Omar Abboosh Hussein
dc.language.isoen
dc.relation.ispartofDiagnostics (Basel)
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Öğrenci
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectage estimation
dc.subjectdeep learning
dc.subjectdental age estimation
dc.subjectforensic odontology
dc.subjectforensics
dc.subjectmachine learning
dc.subjectpanoramic radiograph
dc.titleAutomated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic-Random Forest
dc.typeArticle

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