Transfer learning-based super-resolution in panoramic models for predicting mandibular third molar extraction difficulty: a multi-center study

Background:This study aims to predict the extraction difficulty of mandibular third molars based on panoramic images using transfer learning while employing super-resolution(SR)technology to enhance the feasibility and validity of the prediction.Methods:We reviewed a total of 608 preoperative mandibular third molar panoramic radiographs from two medical facilities:the First Affiliated Hospital of Zhengzhou University(n=509;456 in the training set and 53 in the test set)and the Henan Provincial Dental Hospital(n=99 in the validation set).We conducted a deep-transfer learning network on high-resolution(HR)panoramic radiographs to improve the longitudinal resolution of the images and obtained the SR images.Subsequently,we constructed models named Model-HR and Model-SR using high-dimensional quantitative features extracted through the Least Absolute Shrinkage and Selection Operator method.The models’performances were evaluated using the receiver operating characteristic curve(ROC).To assess the reliability of the model,we compared the results from the test set with those of three dentists.Results:Model-SR outperformed Model-HR(area under the curve(AUC):0.779,sensitivity:85.5%,specificity:60.9%,and accuracy:79.8%vs.AUC:0.753,sensitivity:73.7%,specificity:73.9%,and accuracy:73.7%)in predicting the difficulty of extracting mandibular third molars.Both Model-HR(AUC=0.821,95%CI 0.687–0.956)and Model-SR(AUC=0.963,95%CI 0.921–0.999)demonstrated superior performance compared to expert dentists(highest AUC=0.799,95%CI 0.671–0.927).Conclusions:Model-SR yielded superior predictive performance in determining the difficulty of extracting mandibular third molars when compared with Model-HR and expert dentists’visual assessments.