Application of Deep Learning and Feature Selection Technique on External Root Resorption Identification on CBCT Images

Background: Artificial intelligence have been proven to improve the identification of various maxillofacial lesions. The aim of the current study is two-fold, to assess the performance of four deep learning models (DLM) in external root resorption (ERR) identification, and to assess the effect of combining feature selection technique (FST) with DLM on their ability in ERR identification. Methods: External root resorption was simulated on 88 extracted premolar teeth using tungsten bur according to different depths (0.5mm, 1mm and 2mm). All teeth were scanned using a Cone beam CT (Carestream Dental-CHECK). A training (70%), validation (10%), and test (20%) dataset were established. The performance of four DLMs (i. Random Forest (RF)+Visual Geometry Group 16 (VGG), ii. RF+EfficienNetB4 (EFNET), iii. Support Vector Machine (SVM)+VGG and iv. SVM+EFNET) and four hybrid models (DLM+FST: i. FS+RF+VGG, ii. FS+RF+EFNET, iii. FS+SVM+VGG and iv. FS+SVM+EFNET) was compared. Five performance parameters were assessed namely classification accuracy, F1-score, precision, specificity, error rate. FST algorithms (Boruta and Recursive Feature Selection) were combined with the DLMs to assess their performance. Result: RF+VGG exhibited the highest performance in identifying ERR followed by the other tested models. Similarly, FST combined with RF+VGG outperformed other models with classification accuracy, F1-score, precision, and specificity of 81.9%, weighted accuracy of 83%, and AUC of 96%. Conclusion: In general, all DLMs have similar performance on ERR identification. However, the performance can be improved by combining FST with DLMs.