AI-Based Dental Age Estimation through Application on a Mobile Phone age estimation

Abstract

Age estimation is a legally significant issue, particularly in underdeveloped and developing countries, due to factors such as inadequate civil registration systems and irregular migration. While various techniques are employed for age estimation using traditional methods, it is known that factors including age, gender, chronic illness, race, and geographical region can result in discrepancies between skeletal age and chronological age.

It complicates the process of achieving an accurate age estimation. This review aims to discuss recent research on artificial intelligence applications in light of current literature. Artificial intelligence and Machine Learning (ML) have enabled machines to acquire human-like capabilities in thinking, learning, problem solving, and decision making, leading to significant progress in achieving faster and accurate results. Artificial neural networks have been employed to classify data and conduct studies on age estimation. Artificial intelligence applications alongside traditional methods in age estimation will yield more meaningful outcomes.

Methods in forensic dentistry, archaeology, and forensic medicine: Various methods are being researched to determine the age of skeletal remains or unidentified bodies with minimal margin of error. Advances in forensic odontology have contributed to the increase in dental examinations and the acquisition of more accurate results. Teeth are often used for age estimation in identification. Due to their hard structure and low metabolic rate, it is suggested that the data obtained from dental development teeth provides more accurate results than other structures in the organism.

Important results: Use of artificial intelligence in forensic age estimation increases the accuracy of the methods used and enables rapid results. The time-consuming and costly nature of traditional methods makes the application of AI in this field more appealing. To further develop AI applications, it is essential to diversify datasets, continuously update algorithms, and collect diverse data that includes different ethnicities, genders, and age groups. It would help eliminate biases in AI systems and adopt a more universal approach. Additionally, attention to the privacy of health data and ethical considerations will enhance the reliability of AI applications.

Conclusion:

Digital applications have made age estimation faster, more accessible, and widely applicable in real-world scenarios.
It plays a significant role in fields like healthcare, forensic science, and security for identification and verification purposes.
Despite advancements, challenges such as accuracy limitations, data bias, and ethical concerns remain important considerations.
Continuous research and technological improvements are expected to make age estimation more precise, reliable, and fair in the future.