Background: Assessing the time required for tooth extraction is the most important factor to consider before surgeries. The purpose of this study was to create a practical predictive model for assessing the time to extract the mandibular third molar tooth using deep learning. The accuracy of the model was evaluated by comparing the extraction time predicted by deep learning with the actual time required for extraction. Methods: A total of 724 panoramic X-ray images and clinical data were used for artificial intelligence (AI) prediction of extraction time. Clinical data such as age, sex, maximum mouth opening, body weight, height, the time from the start of incision to the start of suture, and surgeon’s experience were recorded. Data augmentation and weight balancing were used to improve learning abilities of AI models. Extraction time predicted by the concatenated AI model was compared with the actual extraction time. Results: The final combined model (CNN + MLP) model achieved an R value of 0.8315, an R-squared value of 0.6839, a p-value of less than 0.0001, and a mean absolute error (MAE) of 2.95 min with the test dataset. Conclusions: Our proposed model for predicting time to extract the mandibular third molar tooth performs well with a high accuracy in clinical practice.

Park SY, et al. J Korean Assoc Oral Maxillofac Surg. 2017 Dec;43(6):415-422. https://doi.org/10.5125/jkaoms.2017.43.6.415

Kwon D, et al. Imaging Sci Dent. 2022 May;52:e24. https://doi.org/10.5624/isd.20220035

Background: The posterior maxilla and skull base is a region with a complex anatomy. Accurate resection of the pterygoid plate is critical during a maxillectomy procedure. However, there is a paucity of functional and anatomical studies on the pterygoid plate and skull base. This study aimed to investigate functional anatomy of the pterygoid plate and its surrounding structures in the posterior maxilla to provide a better understanding of surgical procedures in this region. Methods: 3D software was used to measure 3D distances, angles, and areas of key anatomical landmarks on CT images of 100 hemifaces. Morphological classification of pterygoid plates was then performed. Results: Results of comparing right and left pterygoid plates revealed no significant differences in dimensions or angles. Comparisons between sexes revealed that a few parameters were significantly different (P < 0.01), including pterygoid height on the left side, distance from the zygomatico-maxillary buttress to the infraorbital fissure (Zy-IOF), and area of the left lateral pterygoid plate. The morphology of the lateral pterygoid plate was classified into four types based on the shape of the middle region: middle convex (42%), double concave (36%), flattened (10%), and middle concave (12%). The morphology of pterygoid plates was classified based on the divergence of medial and lateral pterygoid plates, with the narrow type (56%) being more common than the wide type in this study cohort. Conclusions: This 3D digital anatomical study measured key landmarks for maxillary resection. Such measurement has never been reported. This anatomical study provides surgeons with information on the anatomy of the posterior maxilla and allows for safer and more accurate resection of the difficult-to-resect posterior maxilla.

Ryoo SH, et al. J Dent Anesth Pain Med. 2021 Jun;21(3):261-268. https://doi.org/10.17245/jdapm.2021.21.3.261