Application and performance of artificial intelligence technology in forensic odontology – A systematic review

• Published in: Legal medicine (Tokyo, Japan) Vol. 48; p. 101826
• Main Authors: Khanagar, Sanjeev B., Vishwanathaiah, Satish, Naik, Sachin, A. Al-Kheraif, Abdulaziz, Devang Divakar, Darshan, Sarode, Sachin C., Bhandi, Shilpa, Patil, Shankargouda
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01-02-2021
• Subjects: Artificial intelligence; Artificial neural networks; Convolutional neural networks; Deep learning; Forensic dentistry; Forensic odontology; Machine learning; Deep learning; Forensic dentistry; Forensic odontology; Artificial neural networks; Machine learning; Convolutional neural networks; Artificial intelligence
• ISSN: 1344-6223; 1873-4162
• DOI: 10.1016/j.legalmed.2020.101826

•Artificial intelligence(AI) is a major invention which provides reliable information regarding forensic science.•Studies reported that AI models predict with high accuracy and precision in forensic odontology.•These AI models helpful in predicting the victims in mass disaster or medico-legal cases.
Forensic odontology (FO) mainly deals with the identification of the individual through the remains, which mainly includes teeth and jawbones. Artificial intelligence (AI) technology has proven to be a breakthrough in providing reliable information in decision making in forensic sciences. This systematic review aimed to report on the application and performance of AI technology in FO. The data was gathered through searching for the articles in the renowned search engines, which have been published between January 2000 - June 2020. QUADAS-2 was adopted for the risk of bias analysis of the included studies. AI technology has been widely applied in FO for identifying bite-marks, predicting mandibular morphology, gender determination, and age estimation. Most of these AI models are based on either artificial neural networks (ANNs) or convolutional neural networks (CNNs). The results of the studies are promising. Studies have reported that these models display accuracy and precision equivalent to that of the trained examiners. These models can be promising tools when identifying victims of mass disasters and as an additive aid in medico-legal situations.