Abstract
Chronological age estimation is crucial labour in many clinical procedures, where the teeth have proven to be one of the best estimators. Although some methods to estimate the age from tooth measurements in orthopantomogram (OPG) images have been developed, they rely on time-consuming manual processes whose results are affected by the observer subjectivity. Furthermore, all those approaches have been tested only on OPG image sets of good radiological quality without any conditioning dental characteristic. In this work, two fully automatic methods to estimate the chronological age of a subject from the OPG image are proposed. The first (DANet) consists of a sequential Convolutional Neural Network (CNN) path to predict the age, while the second (DASNet) adds a second CNN path to predict the sex and uses sex-specific features with the aim of improving the age prediction performance. Both methods were tested on a set of 2289 OPG images of subjects from 4.5 to 89.2 years old, where both bad radiological quality images and images showing conditioning dental characteristics were not discarded. The results showed that the DASNet outperforms the DANet in every aspect, reducing the median Error (E) and the median Absolute Error (AE) by about 4 months in the entire database. When evaluating the DASNet in the reduced datasets, the AE values decrease as the real age of the subjects decreases, until reaching a median of about 8 months in the subjects younger than 15. The DASNet method was also compared to the state-of-the-art manual age estimation methods, showing significantly less over- or under-estimation problems. Consequently, we conclude that the DASNet can be used to automatically predict the chronological age of a subject accurately, especially in young subjects with developing dentitions.
Highlights
C HRONOLOGICAL age estimation is a key task in a variety of scenarios: in forensics to estimate the age of a corpse; in trials where the age of the defendant is unclear because of a suspicion that he/she is lying or there is an absence of clarifying documentation; in migration management to assess if a migrant is, or is not, an adult; and in adoption processes to determine the estimated age of undocumented children
We considered the exclusion criteria applied to OPG images in earlier studies that used traditional chronological age-calculation methods [48]
Establishing someone’s chronological age using their dental age is a topic of great importance in the clinical dental field, and is reflected in the high number of studies published on the topic in the literature [34], [35]
Summary
C HRONOLOGICAL age estimation is a key task in a variety of scenarios: in forensics to estimate the age of a corpse; in trials where the age of the defendant is unclear because of a suspicion that he/she is lying or there is an absence of clarifying documentation; in migration management to assess if a migrant is, or is not, an adult; and in adoption processes to determine the estimated age of undocumented children. They relied on the left-hand side mandibular teeth to measure the distance between the inner side of the open apex in single-rooted teeth and the sum of the distances between the inner side of the open apices in multirooted teeth, with a regression formula proposed to translate all the measurements into a chronological age value All these methods have been compared in a variety of populations [15]–[30], with helpful conclusions reached about the most suitable teeth for age estimation and dental development differences depending on sex and race. Our approach is completely automatic, uses raw OPG images without any preprocessing, and does not require dental measurements or other manual annotations to obtain accurate results
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