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

AI Article Synopsis

  • The study compared age estimation methods using panoramic radiography, specifically the Kvaal method and machine learning (ML).
  • It involved analyzing 554 radiographs to measure various dental measurements and extracting numerous attributes for ML analysis, employing different regression techniques.
  • Results showed that ML offered more precise age estimates (MAE: 4.77) compared to the Kvaal method (MAE: 5.68), indicating that ML is a more effective tool for age estimation in dental radiography.

Article 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 -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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10170175PMC
http://dx.doi.org/10.1259/dmfr.20220363DOI Listing

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