An Uncertainty-Aware and Sex-Prior Guided Biological Age Estimation From Orthopantomogram Images

Bone age, as a measure of biological age (BA), plays an important role in a variety of fields, including forensics, orthodontics, sports, and immigration. Despite its significance, accurate estimation of BA remains a challenge due to the uncertainty error between BA and chronological age (CA) caused by individual diversity and the difficult integration of multiple factors, such as sex, and identified or measured anatomical structures, into the estimation process. To address problems, we propose an uncertainty-aware and sex-prior guided biological age estimation from orthopantomogram images (OPGs), named UASP-BAE, which models uncertainty errors while setting sex dimorphism as tractive features to enhance age-related specific features, aiming to improve the accuracy of BA estimation. Furthermore, considering the global relevance of the anatomic structure, such as the mandible, teeth, maxillary sinus, etc., a cross-attention module based on CNN and self-attention is proposed to mine the local texture and global semantic features of OPGs. Moreover, we design a novel age composition loss by cross-entropy, probability bias, and regression functions, aiming at evaluating BA's uncertainty errors and results to obtain an accurate and robust model. On 10703 OPGs from 5.00 to 25.00 years of age, our model had a best MAE value of 0.8005 years and higher than the comparison popular algorithms, which also demonstrates the method's potential for improved accuracy in BA estimation.