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Reliability of artificial intelligence algorithms in automated age estimation using orthopantomograms: A scoping
Spoorti Kulkarni1, Sudeendra Prabhu2, Andrew Jeyabose3,4
1Department of Oral and Maxillofacial Pathology and Oral Microbiology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Background:
This study aims to evaluate the efficiency of AI (artificial intelligence) algorithms for automated age estimation using orthopantomograms (OPGs) and to determine whether these models can effectively replace conventional age estimation techniques.
Method:
Three independent literature searches were conducted in PubMed, Scopus, and Embase. Studies published in the English language were considered, focusing on age estimation using AI. A total of 1519 articles were screened, and 24 articles were included in the study. The data was extracted in a standardized, predefined manner. After finalizing the search, the data collected was tabulated, interpreted, and verified. The selected studies were analyzed for methodological rigor, algorithmic performance, and comparative effectiveness against traditional age estimation methods.
Results:
AI-based models, especially deep learning architectures like convolutional neural networks, EfficientNet, DenseNet, and hybrid models such as Age-Net, demonstrated superior accuracy, precision, and reliability compared to traditional age estimation methods. These AI-driven models show promising results in reducing human error, increasing efficiency, and enhancing forensic and clinical decision-making.
Conclusion:
AI-driven age estimation using OPGs represents a transformative advancement with considerable forensic and clinical potential. Although these AI models may not yet fully replace conventional techniques, they offer a substantial value as complementary tools, improving both accuracy and operational efficiency. To foster wider adoption and improve reliability, ongoing research and the development of standardized protocols are essential for integrating these methods into forensic odontology and related fields.
