Age determination on panoramic radiographs using the Kvaal method with the aid of artificial intelligence

Abstract

Objectives: This study aimed to assess and compare age estimation on panoramic radiography using the Kvaal method and machine learning (ML). Methods and Materials: 554 panoramic radiographs were selected from a Brazilian practice. To estimate age using the Kvaal method, the following measurements were performed on the upper left central incisors and canines: tooth, pulp and root length; root and pulp width at three different levels: at the enamel–cementum junction (ECJ); midpoint between the enamel–cementum junction and; at the mid root level. For ML age estimation, radiomic, semantic and the radiomic-semantic attribute extractions were assessed. Nineteen semantic and 14 radiomic attributes and a single set of 33 semantic-radiomic attributes were extracted. Logistic Regression, Linear Regression, KNN, SVR, Decision Tree Reg, Random Forest Reg, Gradient Boost Reg e XG Boosting Reg were used for ML classification. For the Kvaal method, Mann–Whitney test, Spearman correlation coefficient, Student's t-test and linear regression with its respective coefficient of determination were used to estimate age and to assess data variability. Results: Mean absolute error (MAE) and standard error estimate (SEE) were assessed. For the Kvaal method, upper incisors presented higher precision than canines (R²: 0.335, SSE: 7.108). Males presented better MAE and SEE values (5.29,6.96) than females (5.69,7.37). The radiomic-semantic attributes presented superior precision (MAE: 4.77) than the radiomic and semantic (MAE: 5.23) attributes. The XG Boosting Reg classifier performed better than the other six assessed classifiers (MAE: 4.65). ML (MAE: 4.77 presented higher age estimation precision than the Kvaal method (MAE: 5.68). Conclusion: The use of ML on panoramic radiographs can improve age estimation.

Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs—A Systematic Review

“…AI has primarily been used for automated age estimation by analyzing tooth development stages [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ], tooth and bone parameters [ 48 , 49 , 50 ], bone age measurements [ 51 ], and pulp–tooth ratio [ 52 , 53 ]. We gathered data from the studies included, but due to the varied data samples used to assess AI model performance, a meta-analysis could not be conducted.…”

“…The findings suggested a positive outcome for the semantic–radiomic association attribute. Kvaal’s method and ML yielded better results for the male dataset, with ML outperforming the Kvaal method by around 1 year across all analyzed scenarios [ 52 ].…”

Latin American Forensic Odontology: A Scoping Review of its Current Research and the Objective/Subjective Nature of its Studies

“…Artificial intelligence (AI), a major advancement in forensic dental identification owing to the possibility of generating predictive studies and forensic automation (thereby avoiding human evaluations susceptible to subjectivity and error), has been reported to be promising in different areas of FO (Mohammad et al, 2022). In this review, four studies were identified by applying machine learning in different environments, developed by Brazilian and Colombian researchers, to simulate an identification process (Ortiz et al, 2021), distinguish between women and men using dentomaxillofacial features (Franco et al, 2022), andDAE (Becerra-Álvarez &Cortés-Osorio, 2020;Pereira de Sousa et al, 2023).…”