دورية أكاديمية
Estimation of human age using machine learning on panoramic radiographs for Brazilian patients
| العنوان: | Estimation of human age using machine learning on panoramic radiographs for Brazilian patients |
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| المؤلفون: | Willian Oliveira, Mariana Albuquerque Santos, Caio Augusto Pereira Burgardt, Maria Luiza Anjos Pontual, Cleber Zanchettin |
| المصدر: | Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024) |
| بيانات النشر: | Nature Portfolio, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Forensic sciences, Age estimation, Deep neural network, Radiological methods, Medicine, Science |
| الوصف: | Abstract This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with advanced machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach to age estimation. Our study includes a new dataset of 12,827 dental panoramic X-ray images representing the Brazilian population, covering an age range from 2.25 to 96.50 years. To analyze these exams, we employed a model adapted from InceptionV4, enhanced with data augmentation techniques. The proposed approach achieved robust and reliable results, with a Test Mean Absolute Error of 3.1 years and an R-squared value of 95.5%. Professional radiologists have validated that our model focuses on critical features for age assessment used in odontology, such as pulp chamber dimensions and stages of permanent teeth calcification. Importantly, the model also relies on anatomical information from the mandible, maxillary sinus, and vertebrae, which enables it to perform well even in edentulous cases. This study demonstrates the significant potential of machine learning to revolutionize age estimation in Forensic Science, offering a more accurate, efficient, and universally applicable solution. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-024-70621-1 |
| URL الوصول: | https://doaj.org/article/b6b6cb8785934809bcb5bab6726a7425 |
| رقم الأكسشن: | edsdoj.b6b6cb8785934809bcb5bab6726a7425 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 20452322 |
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| DOI: | 10.1038/s41598-024-70621-1 |