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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>External root resorption was simulated on 88 extracted premolar teeth using tungsten bur in different depths (0.5\u00a0mm, 1\u00a0mm, and 2\u00a0mm). All teeth were scanned using a Cone beam CT (Carestream Dental, Atlanta, GA). Afterward, a training (70%), validation (10%), and test (20%) dataset were established. The performance of four DLMs including Random Forest (RF)\u2009+\u2009Visual Geometry Group 16 (VGG), RF\u2009+\u2009EfficienNetB4 (EFNET), Support Vector Machine (SVM)\u2009+\u2009VGG, and SVM\u2009+\u2009EFNET) and four hybrid models (DLM\u2009+\u2009FST: (i) FS\u2009+\u2009RF\u2009+\u2009VGG, (ii) FS\u2009+\u2009RF\u2009+\u2009EFNET, (iii) FS\u2009+\u2009SVM\u2009+\u2009VGG and (iv) FS\u2009+\u2009SVM\u2009+\u2009EFNET) was compared. Five performance parameters were assessed: classification accuracy, F1-score, precision, specificity, and error rate. FST algorithms (Boruta and Recursive Feature Selection) were combined with the DLMs to assess their performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      RF\u2009+\u2009VGG exhibited the highest performance in identifying ERR, followed by the other tested models. Similarly, FST combined with RF\u2009+\u2009VGG outperformed other models with classification accuracy, F1-score, precision, and specificity of 81.9%, weighted accuracy of 83%, and area under the curve (AUC) of 96%. Kruskal Wallis test revealed a significant difference (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.008) in the prediction accuracy among the eight DLMs.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In general, all DLMs have similar performance on ERR identification. 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