Predicting Alveolar Nerve Injury and the Difficulty Level of Extraction Impacted Third Molars: A Systematic Review of Deep Learning Approaches

Hamza Al Salieti¹Hanan M. Qasem²Sakhr Alshwayyat^3,4,5Noor Almasri⁶Mustafa Alshwayyat⁷Amira A. Aboali⁸Farah Alsarayrah²Lina Khasawneh⁹Mohammed Al-mahdi Al-kurdi^10*

• ¹Faculty of Dentistry, Applied Science Private University, Jordan
• ²Faculty of Dentistry, Jordan University of Science and Technology, Jordan
• ³Research Associate, King Hussein Cancer Center, Jordan
• ⁴Internship, Princess Basma Teaching Hospital, Jordan
• ⁵Applied Science Research Center, Applied Science Private University, Jordan
• ⁶Faculty of Medicine, University of Jordan, Jordan
• ⁷Faculty of Medicine, Jordan University of Science and Technology, Jordan
• ⁸Faculty of Medicine, Alexandria University, Egypt
• ⁹Department of Prosthodontics, Faculty of Dentistry, Jordan University of Science and Technology, Jordan
• ¹⁰Faculty of Medicine, University of Aleppo, Syrian Arab Republic

Background: Third molar extraction, a common dental procedure, often involves complications, such as alveolar nerve injury. Accurate preoperative assessment of the extraction difficulty and nerve injury risk is crucial for better surgical planning and patient outcomes. Recent advancements in deep learning (DL) have shown the potential to enhance the predictive accuracy using panoramic radiographic (PR) images. This systematic review evaluated the accuracy and reliability of DL models for predicting third molar extraction difficulty and inferior alveolar nerve (IAN) injury risk. Methods: A systematic search was conducted across PubMed, Scopus, Web of Science, and Embase until September 2024, focusing on studies assessing DL models for predicting extraction complexity and IAN injury using PR images. The inclusion criteria required studies to report predictive performance metrics. Study selection, data extraction, and quality assessment were independently performed by two authors using the PRISMA and QUADAS-2 guidelines. Results: Six studies involving 12,419 PR images met the inclusion criteria. DL models demonstrated high accuracy in predicting extraction difficulty (up to 96%) and IAN injury (up to 92.9%), with notable sensitivity (up to 97.5%) for specific classifications, such as horizontal impactions. Geographically, three studies originated in South Korea and one each from Turkey and Thailand, limiting generalizability. Despite high accuracy, demographic data were sparsely reported, with only two studies providing patient sex distribution. Conclusion: DL models show promise in improving the preoperative assessment of third molar extraction. However, further validation in diverse populations and integration with clinical workflows are necessary to establish its real-world utility, as limitations such as limited generalizability, potential selection bias and lack of long-term follow up remain challenges. Keywords: Alveolar Nerve, Deep Learning, Mandibular Nerve, Panoramic Radiographic, Third Molar.