# Root Dilaceration Using Deep Learning:
## 4 Discussion  
As one of the most frequently used imaging modalities in routine dentistry, panoramic radiographs scan a wider range of dental structures with relatively low radiation, which makes them an essential tool for diagnosis in clinics [44; 45]. Considering complex oral structures, busy working conditions, and lack of time, deep-learning-based computer-aided detection systems can advance the quality of daily routines and treatment planning by providing instant image analysis for clinicians. In this context, we applied state-of-the-art deep learning models to detect root dilaceration in panoramic radiographs. To the best of our knowledge, this is the first and most comprehensive study on detecting root dilaceration directly using deep learning. There is no other study that can  
Figure 4: Precision-recall curve and loss figures for two chosen models with overall high performance. (**(a)** for Cascade RCNN and **(b)** for RetinaNet). The Precision-recall curve visualizes classification performance and loss figures show how the models optimize loss. directly be compared with this work. Previous studies are mostly based on evaluating the prevalence of root dilaceration using different types of radiographs, including panoramic radiographs, periapical radiographs and cone-beam computed tomography images, and case reports on treatment management. Very limited previous studies that were indirectly related to root dilaceration using artificial intelligence were also discussed as shown in Table 4.  
[PERSON] et al. used artificial intelligence to detect 17 fine-grained dental anomalies using 23,000 panoramic radiographs [46]. R-CNN and Detectron-2 were used for detection. Root dilaceration was mentioned in the supernumerary tooth section of dental anomalies category but was not specifically examined. Supernumerary teeth were shown to be one of the causes of root dilaceration. Precision, sensitivity (recall) and specificity for supernumerary tooth detection values were 0.32, 0.62 and 0.97, respectively. Compared to this study, the proposed work provides superior results: detected root dilacerations with a varying mAP of between 0.68 and 0.92. Precision and recall values were between 0.66 and 0.91 and 0.59 and 0.87, respectively. [PERSON] used machine learning to predict canine eruption and some other anomalies in panoramic radiographs [47]. When mentioning the high incidence of maxillary canine impaction and common etiologic aspects, root dilaceration was shown to be a localized factor. The final results reported that the sensitivity (recall), specificity, F1 score, and accuracy values were 0.479, 0.592, 0.28 and 0.572, respectively. Compared to the presented findings, the proposed deep learning models showed better performance. Software for accurate detection and assessment of root dilaceration holds great relevance to the clinic due to its potential impact on improving patient outcomes and treatment planning in dentistry. By applying deep learning techniques to panoramic radiographs taken during routine imaging examinations, clinicians can benefit in several ways:  
* Early Detection: Deep learning models can identify root dilacerations at an early stage even if the patient is not there for a root dilaceration-related case, which allows clinicians to promptly intervene and implement appropriate treatment strategies. Early detection may prevent further complications, such as tooth impaction, misalignment, or delayed eruption;
* Accurate Diagnosis: Deep learning algorithms can aid in accurately diagnosing root dilacerations with high performance. This can reduce the risk of misinterpretation or missed diagnoses, ensuring that patients receive timely and accurate treatment;