Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic–Random Forest
<b>Background/Objectives:</b> 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. Therefor...
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MDPI AG
2025-01-01
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| author | Gulfem Ozlu Ucan Omar Abboosh Hussein Gwassi Burak Kerem Apaydin Bahadir Ucan |
| author_facet | Gulfem Ozlu Ucan Omar Abboosh Hussein Gwassi Burak Kerem Apaydin Bahadir Ucan |
| author_sort | Gulfem Ozlu Ucan |
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| description | <b>Background/Objectives:</b> 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. <b>Methods:</b> 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 R<sup>2</sup> values calculated during the implementation of the code. <b>Results:</b> As a result of the applied algorithms, the MSE value was 0.00027, MAE value was 0.0079, RMSE was 0.0888, and R<sup>2</sup> score was 0.999. <b>Conclusions:</b> 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. |
| format | Article |
| id | doaj-art-e49b549d709f4ca6b748d9cbfee2fd3e |
| institution | DOAJ |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Diagnostics |
| spelling | doaj-art-e49b549d709f4ca6b748d9cbfee2fd3e2025-08-20T02:48:02ZengMDPI AGDiagnostics2075-44182025-01-0115331410.3390/diagnostics15030314Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic–Random ForestGulfem Ozlu Ucan0Omar Abboosh Hussein Gwassi1Burak Kerem Apaydin2Bahadir Ucan3Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Istanbul Gelisim University, Istanbul 34310, TurkeyElectrical and Computer Engineering, School of Engineering and Natural Sciences, Altinbas University, Istanbul 34217, TurkeyDepartment of Oral and Maxillofacial Radiology, Faculty of Dentistry, Pamukkale University, Denizli 20160, TurkeyDepartment of Communication and Design, Yildiz Technical University, Istanbul 34220, Turkey<b>Background/Objectives:</b> 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. <b>Methods:</b> 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 R<sup>2</sup> values calculated during the implementation of the code. <b>Results:</b> As a result of the applied algorithms, the MSE value was 0.00027, MAE value was 0.0079, RMSE was 0.0888, and R<sup>2</sup> score was 0.999. <b>Conclusions:</b> 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.https://www.mdpi.com/2075-4418/15/3/314age estimationdental age estimationforensic odontologydeep learningmachine learningforensics |
| spellingShingle | Gulfem Ozlu Ucan Omar Abboosh Hussein Gwassi Burak Kerem Apaydin Bahadir Ucan Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic–Random Forest Diagnostics age estimation dental age estimation forensic odontology deep learning machine learning forensics |
| title | Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic–Random Forest |
| title_full | Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic–Random Forest |
| title_fullStr | Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic–Random Forest |
| title_full_unstemmed | Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic–Random Forest |
| title_short | Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic–Random Forest |
| title_sort | automated age estimation from opg images and patient records using deep feature extraction and modified genetic random forest |
| topic | age estimation dental age estimation forensic odontology deep learning machine learning forensics |
| url | https://www.mdpi.com/2075-4418/15/3/314 |
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