The mandibular third molar extraction, considered one of the most common surgeries in oral and maxillofacial field, it can be associated with several postoperative complications, like pain, bleeding, swelling, and inferior alveolar nerve (IAN) injury or complete damage, impairing the quality of life of the affected patients. The incidence of temporary IAN injury caused by mandibular third molar extraction was 0.4-8.4%, while the incidence of permanent injury is less than 1% \[1, 2\]. However, due to the high occurrence of impacted mandibular third molar, a large number of patients suffer from IAN injury caused by impacted mandibular third molar extraction \[3\]. The most significant risk factor of IAN injury caused by mandibular third molar extraction is the proximity of the root of the mandibular third molar to the mandibular canal \[1, 2, 4, 5\]. So, comprehensive preoperative analysis and evaluation of the anatomical structures are essential before impacted mandibular third molar extraction to decrease the IAN injury risk.

The panoramic radiography is not that much accurate in displaying the relation between impacted mandibular third molar extraction and IAN due to the superimposition and inherent limitations. The accuracy of predicting the probability the (IAN) injury during the impacted mandibular third molar extraction using panoramic radiographs were controversial \[6\].

Cone beam computed tomography (CBCT), A (3D) imaging modality, provides accurate 3D information with decreased radiation dose than medical CT \[7\]. It was demonstrated that CBCT was a better and accurate radiographic method than panoramic radiography for evaluating the relationship between mandibular third molar and (IAN) \[6, 8\]. So that, CBCT has been considered as the modality of choice for preoperative assessment of complicated mandibular third molar extraction \[9\].

Deep learning, one of artificial intelligence subsets, had a rapid progression and has a significant role in medical fields. One of the deep learning models, guided learning of the convolutional neural network (CNN) is recently investigated, which has been proven to surpass human judgmental level in many medical imaging fields \[12, 13\]. After CNN was introduced to the maxillofacial field, it was used for the assessment, detection, categorization, and segmentation of the surrounding anatomical structures \[14-18 Recently, deep learning based on CNN models has been used for the impacted mandibular third molar and mandibular canal detection and segmentation on panoramic radiographs and CBCT \[15, 18, 30\], the classification and staging of development \[31, 32\], and the approximation measurements of the impacted mandibular third molar on panoramic radiographs \[33\]. Fukuda et al. compared 3 CNNs for classification of the impacted mandibular third molar and mandibular canal relation with panoramic radiographs \[34\]. Yoo et al. proposed a CNN-based approach to assess the stalemate of the impacted mandibular third molar extraction using panoramic radiographs \[35\]. So, as mentioned before, panoramic radiography can't accurately describe the anatomical structures due to the superimposition that happens in the (2D) imaging modalities. Orhan et al. reported an AI application (Diagnocat, Inc.) based on CNN with high precision in detecting the M3 and assessment of the number of roots related to adjacent anatomical structures