  Dental age is one of the most reliable methods to identify an individual's
age. By using dental panoramic radiography (DPR) images, physicians and
pathologists in forensic sciences try to establish the chronological age of
individuals with no valid legal records or registered patients. The current
methods in practice demand intensive labor, time, and qualified experts. The
development of deep learning algorithms in the field of medical image
processing has improved the sensitivity of predicting truth values while
reducing the processing speed of imaging time. This study proposed an automated
approach to estimate the forensic ages of individuals ranging in age from 8 to
68 using 1,332 DPR images. Initially, experimental analyses were performed with
the transfer learning-based models, including InceptionV3, DenseNet201,
EfficientNetB4, MobileNetV2, VGG16, and ResNet50V2; and accordingly, the
best-performing model, InceptionV3, was modified, and a new neural network
model was developed. Reducing the number of the parameters already available in
the developed model architecture resulted in a faster and more accurate dental
age estimation. The performance metrics of the results attained were as
follows: mean absolute error (MAE) was 3.13, root mean square error (RMSE) was
4.77, and correlation coefficient R$^2$ was 87%. It is conceivable to propose
the new model as potentially dependable and practical ancillary equipment in
forensic sciences and dental medicine.
