# Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models
## 1 Introduction  
Tooth decay, also known as dental caries, is a widespread and long-lasting health issue across the globe [1, 2]. According to the International Caries Detection and Assessment System (ICDAS) [3], dental caries can be described as a loss of minerals in the hard tissues of the teeth due to a combination of factors, including biofilm formation, dietary choices, and various other factors. These factors encompass biological, behavioral, psychological, and environmental aspects. The end result of this process is the development of a caries lesion, which can affect both primary and permanent teeth, leading to damage in the tooth's crown and exposure of the root surfaces [4]. These lesions begin with the demineralization of tooth surfaces, ultimately progressing into larger cavities and potentially causing pulpitis [5]. As such, timely detection can prevent tooth damage and save expensive healthcare costs. Thus, an effective method for early dental caries detection is a crucial subject in dental research. Over the past years, visual inspection, tactile examinations, radiograph methods, and the use of a dental probe were the most common methods for determining caries lesions with the help of radiography and dental explorers [5, 6]. These traditional caries detection methods are effective for large, clearly visible caries and partially obscured caries that are detectable using a handheld mirror [6]. Inspection using radiographic imaging indicates the locations of suspicious lesions, and tactile examination helps dentists accessvarious lesions directly. This methodology has some drawbacks, however. It is impossible to trace subtle lesions because X-ray images show a two-dimensional (2D) representation of three-dimensional (3D) tooth structures. Moreover, the tactile examination is experimental and depends on intuition, and not on quantifiable standards [5]. Radiographic detection requires ionizing radiation, which can deposit energy in human cells and cause tissue change [7]. In addition, for different practitioners, even when using the same radiograph, discrepancies in caries detection can be caused by factors such as the radiograph quality, viewing conditions, and the dentist's expectations and experience [8]. In recent years, artificial intelligence (AI) has become more popular and widely applied in different medicine fields, such as the liver [9], breast [10] and dentistry [11; 12]. Among the wide scope of applications in dentistry, AI has shown good performance in detecting carious lesions [13]. The convolutional neural network (CNN)-based models have shown to detect caries lesions with superior performance. The augmentation and integration of AI in dental care is a relatively new paradigm and needs to be expedited and further explored [14]. CNNs have recently been integrated to solve a number of medical problems stages [15] due to the ability to handle complex tasks, and they have been shown to provide accurate detection with improved clinical outcomes [16; 17; 18; 19]. U-Net is a popular convolutional neural network architecture known for its effectiveness in medical image segmentation tasks. It is a commonly used CNN architecture for medical image segmentation, known for its effectiveness in pixel-level classification and its flexibility in handling both 2D and 3D medical images [20]. Several medical image segmentation algorithms use the U-Net encoder-decoder architecture [21; 22]. To capture fine-grained semantics and coarse-grained details, U-Net3+ [23] improves the U-Net. In dentistry, the use of CNN has been investigated since 2015. [PERSON] et al. [21] proposed the U-Net to analyze dental structure segmentation on bitewing radiographs. There have, however, been limited studies evaluating U-Net or CNN-based U-Net architectures for the detection of dental caries. In addition, there are very few datasets available for dental panoramic X-ray images. Therefore, we focus in a similar direction to contribute to the state of the art by utilizing the U-Net shape-based approach for dental cavity detection.