# Root Dilaceration Using Deep Learning:
## 1 Introduction  
Root dilaceration refers to an abnormality in tooth development, characterized by a deviation from the longitudinal axis, a sharp bend, or curvature in the tooth's root [1]. Initially identified by [PERSON] in 1848, this anomaly is recognized as an irregular deviation of both the crown and roots [2]. The etiology of root dilacerations remains partially unknown, with no broadly acknowledged theory or supporting scientific evidence to explain their formation. However, several potential causes have been discussed in earlier studies, including trauma, genetics, spatial constraints, proximity to cysts, tumors, or unanatomical structures [1, 3, 4]. The most effective method for diagnosing root dilacerations is through radiographic examination. A high degree of root curvature is significantly risky as it can lead to increased force and stress when occlusal forces are applied to the tooth, causing instability. Maintaining control over dental instruments during endodontic treatment becomes vitally important, particularly in the case of canal curvature. The occurrence of dilaceration is regarded asthe key dental factor influencing the success of endodontic treatment. The extraordinary angulation of the root can lead to complications such as ledge, transport, zipping, and broken file [3; 5; 6]. Additionally, root resorption can occur during orthodontic treatment in cases involving dilacerated roots [7]. It is also noteworthy that dilaceration is the most common cause of eruption failure in permanent central incisors [8]. Consequently, it is essential to monitor root development in terms of angulation, position, and shape, and crucially, to diagnose any dilacerations prior to orthodontic, endodontic, and surgical treatments. The reported prevalence of root dilacerations fluctuates greatly across studies, with a range from 2.12% to 69.4% [9; 10; 11; 12; 13; 14]. This variation arises due to differences in the criteria used to define root dilaceration, methodologies employed, and other influential factors such as trauma history, ethnicity, and gender [15; 16; 17]. Despite many studies, there remains no consensus on the definition of dilaceration for determining the prevalence or the specific location of the dilaceration. [PERSON] et al. defined dilaceration as apical deviations greater than 20 degrees from the normal axis of the tooth in the roots; whereas, [PERSON] et al. and [PERSON] et al. and others considered the angle to be 90 degrees in the anterior or posterior plane [1; 5; 6; 9]. [PERSON] et. al. classified dilacerations into mild (20-40 degrees), moderate (40-60 deg) and extreme (beyond 60 deg) according to the angle of the root [18]. Deep learning, an integral branch of artificial intelligence (AI), is characterized by its use of algorithms that can learn and improve from a vast array of input data. This capacity for learning enables computer systems to resolve complex problems more efficiently, making it a powerful tool in data-heavy fields. Transitioning to the realm of dentistry, the potential and practicality of deep learning becomes increasingly clear. In this context, deep learning has been successfully utilized to automate and refine a multitude of tasks [19; 20; 21; 22; 23; 24; 25]. Considering the time-consuming and complicated nature of recognizing all related signs of dental conditions, deep-learning-based detection approaches are needed to save clinicians' time and improve their work quality performance. In this work, we aim to develop a computer-aided decision support system to automatically detect root dilaceration in panoramic radiographs. A tooth was determined as having root dilaceration if there was an angulation or curvature of 20 degrees or more from the normal axis in the anterior or posterior plane. Anterior and posterior deviations were examined in PRs. A total of 19 different state-of-the-art deep-learning-based detection models were applied including Faster R-CNN, SSD, YOLO, and RetinaNet.