Exploring Effective DNN Models for Forensic Age Estimation based on Panoramic Radiograph Images

Dental age estimation is widely used in forensic identification, but the accuracy of traditional methods cannot satisfy the demand for accuracy, especially for age estimation of adults. We introduce a deep learning-based methodology to estimate the age based on collected X-ray images of the teeth. We present a new dental dataset, which contains labeled orthopan-tomograms (OPGs) of 27,957 people, including 16,383 OPGs for females as well as 11,574 OPGs for males. All ages range from 0 to 93-year-old with a median of 27. The accuracy of the age labels is guaranteed by the ID card information. Aiming at the characteristics of the dental data itself, we explore various neural network elements that are effective for age estimation, including proper network depth, convolution kernel size, multi-branch structure, and the feature reusing of early layers. Based on the characteristic exploration, we further search models for dental age estimation by using the popular Neural Architecture Search (NAS) method. Experiment results show that our model achieves a mean absolute error (MAE) of 1.64 years, surpass all existing CNN models. Compared with Inception-v4 with an MAE of 1.70 and 20.46B FLOPs (inputs size 384×384), the FLOPs of our model can be reduced by 2.7 times (7.49B FLOPs). To our best knowledge, this is the first study for age estimation by exploring and searching the DNN model. Our results have surpassed legal medical expert-level performance (with an MAE of more than 2) for age estimation. Our methodology and results in this paper are very meaningful to forensic medicine for aging estimation with panoramic radiograph images.