Improved Diagnostic Accuracy of Ameloblastoma and Odontogenic Keratocyst on Cone-Beam CT by Artificial Intelligence

Frontiers in Oncology

Hua Chen¹  Ting-Guan Sun²  Xue-Meng Shen³  Zi-Kang Chai⁴  Zhi-Jun Sun⁴  Juan Liu⁴  Liang Mao⁴ 

[1] Key Laboratory of Oral Biomedicine, Ministry of Education, School and Hospital of Stomatology, Wuhan University, Wuhan, China;Department of Oral Maxillofacial-Head Neck Oncology, School and Hospital of Stomatology, Wuhan University, Wuhan, China;Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China;;The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) &

关键词: deep learning;    convolutional neural network;    Inception v3;    ameloblastoma;    odontogenic keratocyst;    cone-beam CT;   

DOI  :  10.3389/fonc.2021.793417

来源: DOAJ

【 摘 要 】

ObjectiveThe purpose of this study was to utilize a convolutional neural network (CNN) to make preoperative differential diagnoses between ameloblastoma (AME) and odontogenic keratocyst (OKC) on cone-beam CT (CBCT).MethodsThe CBCT images of 178 AMEs and 172 OKCs were retrospectively retrieved from the Hospital of Stomatology, Wuhan University. The datasets were randomly split into a training dataset of 272 cases and a testing dataset of 78 cases. Slices comprising lesions were retained and then cropped to suitable patches for training. The Inception v3 deep learning algorithm was utilized, and its diagnostic performance was compared with that of oral and maxillofacial surgeons.ResultsThe sensitivity, specificity, accuracy, and F1 score were 87.2%, 82.1%, 84.6%, and 85.0%, respectively. Furthermore, the average scores of the same indexes for 7 senior oral and maxillofacial surgeons were 60.0%, 71.4%, 65.7%, and 63.6%, respectively, and those of 30 junior oral and maxillofacial surgeons were 63.9%, 53.2%, 58.5%, and 60.7%, respectively.ConclusionThe deep learning model was able to differentiate these two lesions with better diagnostic accuracy than clinical surgeons. The results indicate that the CNN may provide assistance for clinical diagnosis, especially for inexperienced surgeons.

【 授权许可】

Unknown