AUTHOR=Al Salieti Hamza , Qasem Hanan M. , Alshwayyat Sakhr , Almasri Noor , Alshwayyat Mustafa , Aboali Amira A. , Alsarayrah Farah , Khasawneh Lina , Al-kurdi Mohammed Al-mahdi 

TITLE=Predicting alveolar nerve injury and the difficulty level of extraction impacted third molars: a systematic review of deep learning approaches

JOURNAL=Frontiers in Dental Medicine

VOLUME=Volume 6 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/dental-medicine/articles/10.3389/fdmed.2025.1534406

DOI=10.3389/fdmed.2025.1534406

ISSN=2673-4915

ABSTRACT=BackgroundThird 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.MethodsA 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.ResultsSix 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.ConclusionDL 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.