Objective To evaluate the accuracy of different convolutional neural networks (CNN),representative deep learning models,in the differential diagnosis of ameloblastoma and odontogenic keratocyst,and subsequently compare the diagnosis results between models and oral radiologists. Methods A total of 1000 digital panoramic radiographs were retrospectively collected from the patients with ameloblastoma (500 radiographs) or odontogenic keratocyst (500 radiographs) in the Department of Oral and Maxillofacial Radiology,Peking University School of Stomatology.Eight CNN including ResNet (18,50,101),VGG (16,19),and EfficientNet (b1,b3,b5) were selected to distinguish ameloblastoma from odontogenic keratocyst.Transfer learning was employed to train 800 panoramic radiographs in the training set through 5-fold cross validation,and 200 panoramic radiographs in the test set were used for differential diagnosis.Chi square test was performed for comparing the performance among different CNN.Furthermore,7 oral radiologists (including 2 seniors and 5 juniors) made a diagnosis on the 200 panoramic radiographs in the test set,and the diagnosis results were compared between CNN and oral radiologists. Results The eight neural network models showed the diagnostic accuracy ranging from 82.50% to 87.50%,of which EfficientNet b1 had the highest accuracy of 87.50%.There was no significant difference in the diagnostic accuracy among the CNN models (P=0.998,P=0.905).The average diagnostic accuracy of oral radiologists was (70.30±5.48)%,and there was no statistical difference in the accuracy between senior and junior oral radiologists (P=0.883).The diagnostic accuracy of CNN models was higher than that of oral radiologists (P<0.001). Conclusion Deep learning CNN can realize accurate differential diagnosis between ameloblastoma and odontogenic keratocyst with panoramic radiographs,with higher diagnostic accuracy than oral radiologists.
Humans ; Ameloblastoma/diagnostic imaging* ; Deep Learning ; Diagnosis, Differential ; Radiography, Panoramic ; Retrospective Studies ; Odontogenic Cysts/diagnostic imaging* ; Odontogenic Tumors

Fibroma/diagnostic imaging* ; Humans ; Odontogenic Tumors/diagnostic imaging* ; Tuberous Sclerosis/complications*

Primary sinonasal ameloblastoma is an extremely rare odontogenic epithelial tumor histomorphologically identical to its gnathic counterparts but with distinct epidemiologic and clinicopathologic characteristics. We present a case of a 46-year-old female with a one year history of recurrent epistaxis, nasal obstruction and frontonasal headache. Clinical examination, CT scan and subsequent surgical excsion revealed an intranasal mass attached to the lateral nasal cavity with histomorphologic features of ameloblastoma and was signed out as extragnathic soft tissue ameloblastoma of the sinonasal area. Extraosseous extragnathic primary sinonasal ameloblastoma are rare but do occur and should be distinguished from infrasellar craniopharyngiomas.

Human ; Female ; Middle Aged ; AMELOBLASTOMA ; ODONTOGENIC TUMORS ; CALCIFYING EPITHELIAL ODONTOGENIC TUMOR ; NEOPLASMS BY HISTOLOGIC TYPE ; NEOPLASMS ; EPISTAXIS ; RECURRENCE ; DIAGNOSTIC IMAGING ; MEDICAL IMAGING ; DIAGNOSTIC TECHNIQUES AND PROCEDURES ; TOMOGRAPHY SCANNERS, X-RAY COMPUTED