## 1 Introduction  
When estimating age, criteria such as weight, height, signs of puberty, mental development, and teeth and bone development are considered [1]. Estimated age is essential in some dental treatment approaches. It can also be used in anthropology to determine the age of death of hominid fossils. In forensic cases, it can be used when chronological age information is unavailable or the existing information is questionable [2]. Hard tissues in the body can be examined to determine age. For bone-based estimation, the hand-wrist area, public symphysis, and medial clavicular epiphysis cartilage can be examined, but teeth-based methods are generally less susceptible to error [3]. Tenth are the most durable part of the human body and can provide valuable information about a person's ethnic origin, occupation, sex, and age [4]. [PERSON]'s, [PERSON]'s, and [PERSON]'s methods and other methods, such as measuring pulp volume or root transparency, have been developed to estimate age from teeth [5, 6]. Each method has different levels of accuracy and is generally limited to specific populations [7]. Performing age estimation based on manual measurement methods such as [PERSON]'s open apices method or using dental development tables like [PERSON]'s method can increase the likelihood of errors and result in subjective outcomes that may vary between operators. Moreover, these conventional methods are time-consuming and require the teeth used for age estimation to be free of cavities or to have not undergone root canal treatment [8]. Indeed, it is essential to note that sex can influence dental development patterns. It has been demonstrated that girls reach certain stages of dental development earlier than boys, particularly in permanent teeth [9]. Therefore, age estimation methods such as those of [PERSON] and [PERSON] have separate dental development tables for girls and boys to account for these sex-related differences [10]. CBCT and cephalometric and panoramic radiography can be used to estimate radiographic sex based on teeth and jawbones [11; 12; 13]. Although panoramic radiographs have disadvantages, such as ghost images, geometric distortion, and superimpositions, they are frequently preferred for diagnosis and follow-up because they are cheaper and more accessible, can be taken quickly, and have a lower radiation dose [14]. Parallel to technological advancements and the integration of deep learning (DL) into various industries, traditional techniques are being replaced by artificial intelligence (AI) methods. DL is a subset of machine learning that uses networks with multiple layers of computation to analyze input data, thereby significantly enhancing the performance of AI [15]. Convolutional neural networks (CNNs) are a type of DL algorithm particularly well suited for image processing [16]. AlexNet is a classic CNN architecture with an eight-layer depth that won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012 by significantly surpassing previous methods. It became popular and inspired the development of other similar methods. VGG-16 is another popular method with a straightforward design, but it has many parameters that take a long time to learn. The numbers at the end of the network names usually indicate the number of layers, in other words, their depth. VGG-19 is very similar to VGG-16 but contains more layers. Google implemented inception building blocks into CNNs and called the network GoogLeNet (Inceptionv1). These blocks work well together, resulting in a model that is easy to generalize. This model is regularly updated with new versions, including Xception, which uses depthwise separable convolutions. ResNet, short for Residual Network, is a specific type of neural network. It comes in various sizes and numbers of layers, such as ResNet-18, ResNet-50, and ResNet-101. DenseNet is very similar to ResNet but concatenates the previous layer's output with the next layer's output. DarkNet-19 and DarkNet-53 are the networks used in YOLO, a popular object recognition method.