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

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dc.authoridhttps://orcid.org/0000-0002-0720-3447
dc.authoridhttps://orcid.org/0000-0003-2621-4704
dc.authoridhttps://orcid.org/0000-0003-4062-0469
dc.contributor.authorÖzlü Uçan, Gülfem
dc.contributor.authorGwassi, Omar Abboosh Hussein
dc.contributor.authorApaydın, Burak Kerem
dc.contributor.authorUçan, Bahadır
dc.date.accessioned2025-06-24T09:04:27Z
dc.date.available2025-06-24T09:04:27Z
dc.date.issued2025
dc.departmentDiş Hekimliği Fakültesi
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. Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic–Random Forest. Diagnostics 2025, 15, 314. https://doi.org/ 10.3390/diagnostics15030314
dc.identifier.doi10.3390/diagnostics15030314
dc.identifier.issn2075-4418
dc.identifier.issue3
dc.identifier.urihttps://hdl.handle.net/11363/9984
dc.identifier.volume15
dc.identifier.wos001419503700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.institutionauthorÖzlü Uçan, Gülfem
dc.institutionauthoridhttps://orcid.org/0000-0002-0720-3447
dc.language.isoen
dc.publisherMDPI, MDPI AG, Grosspeteranlage 5, CH-4052 BASEL, SWITZERLAND
dc.relation.ispartofDIAGNOSTICS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectage estimation
dc.subjectdental age estimation
dc.subjectforensic odontology
dc.subjectdeep learning
dc.subjectmachine learning
dc.subjectforensics
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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