Background/Objectives: The growing demand for artificial intelligence (AI) in healthcare is driven by the need for more robust and automated diagnostic systems. These methods not only provide accurate diagnoses but also promise to enhance operational efficiency and optimize resource utilization in clinical workflows. In the field of dental lesion detection, the application of deep learning models to various imaging techniques has gained significant prominence. This study presents a comprehensive systematic review of the utilization of deep learning methods for detecting dental lesions across different imaging modalities, including panoramic imaging, periapical radiographs, and cone-beam computed tomography (CBCT). A systematic search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a structured and transparent review process. Methods: This study addresses four key research questions related to the types of objects used for AI in dental images, state-of-the-art approaches for detecting lesions in dental images, data augmentation methods, and challenges and possible solutions to the existing AI-based dental lesion detection. Furthermore, this systematic review was performed on 29 primary studies identified from multiple electronic databases. This review focused on studies published between 2019 and 2024, sourced from IEEE, Web of Knowledge, Springer, ScienceDirect, PubMed, and Google Scholar. Results: We identified five types of lesions in dental images as periapical lesions, cyst lesions, jawbone lesions, dental caries, and apical lesions. Among the fourteen state-of-the-art deep learning approaches, the results demonstrate that deep learning models, such as U-Net, AlexNet, and You Only Look Once (YOLO) version 8 (YOLOv8) are commonly employed for dental lesion detection. These deep learning models have the potential to serve as integral components of decision-making processes by improving detection accuracy and supporting clinical workflows. Furthermore, we found that among twelve types of data augmentation techniques, flipping, rotation, and reflection methods played an important role in increasing the diversity of the datasets. We also identified six challenges for dental lesion detection, and the main issues were identified as data integration, poor data quality, limited model generalization, and overfitting. Proposed solutions against the aforementioned challenges include the integration of larger datasets, model optimization, and diversification of data sources. Conclusions: This study provides a comprehensive overview of current methodologies and potential advancements in dental lesion detection using deep learning. The findings indicate that possible solutions against the challenges of AI-based diagnostic methods in dental lesion detection need to be more generalizable regardless of image type, the number of data, and data quality.

Impaction of a mandibular premolar is relatively uncommon. Ectopic placement is more unusual and there has been no discussion in the literature of an ectopic mandibular premolar in the coronoid process. In this case report, we present an impacted ectopic mandibular permanent premolar in the sigmoid notch (incisura mandibulae) region. Etiology of the tooth and treatment options are discussed and illustrated by Cone Beam Computed Tomography (CBCT) images.

Pleomorphic adenoma (PA) is the most prevalent benign salivary gland tumor, manifesting predominantly in the parotid gland. The imaging techniques most fre

Pleomorfik adenom, tükürük bezi neoplazmlarının büyük bir kısmını oluşturan ve parotis bezi en sık görülen neoplazmı olup minör tükürük bezlerinde nadiren görülür. Minör tükürük bezi kökenli lezyonlar sıklıkla sert damakta lokalizedir. Yavaş büyüyen ağrısız şişlikle karakterize olup sıklıkla 4.-5. dekatta ve kadınlarda görülür. Etyoloji tam olarak bilinmemekle beraber genetik, çevresel ve mikrobiyal etkenler rol alabilir. Lezyonun boyutu, sınırları ve çevre doku ile ilişkisinde ileri görüntüleme tekniklerinden yararlanılır. Manyetik rezonans görüntüleme (MRG) lezyonun görüntülenmesinde yüksek etkinliğe sahip olsa da kesin teşhis histopatoloji ile konulur. Pleomorfik adenom sağlıklı doku sınırları bırakılarak eksize edildiğinde tedaviye iyi yanıt verir ve nüks nadiren görülür. Bu olgu sunumunda damakta nadir görülen bir pleomorfik adenom vakasının sunumu amaçlanmıştır.