Application of Convolutional Neural Network in the Diagnosis of Jaw Tumors

Healthcare Informatics Research

Wiwiek Poedjiastoeti¹  Siriwan Suebnukarn² 

[1] Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Trisakti University, Jakarta, .Indonesia;Faculty of Dentistry, Thammasat University, Pathumthani, .Thailand;

关键词: artificial intelligence;    ameloblastoma;    odontogenic tumors;    panoramic radiography;    oral and maxillofacial surgeons;   

DOI  :  10.4258/hir.2018.24.3.236

来源: DOAJ

【 摘 要 】

ObjectivesAmeloblastomas and keratocystic odontogenic tumors (KCOTs) are important odontogenic tumors of the jaw. While their radiological findings are similar, the behaviors of these two types of tumors are different. Precise preoperative diagnosis of these tumors can help oral and maxillofacial surgeons plan appropriate treatment. In this study, we created a convolutional neural network (CNN) for the detection of ameloblastomas and KCOTs.MethodsFive hundred digital panoramic images of ameloblastomas and KCOTs were retrospectively collected from a hospital information system, whose patient information could not be identified, and preprocessed by inverse logarithm and histogram equalization. To overcome the imbalance of data entry, we focused our study on 2 tumors with equal distributions of input data. We implemented a transfer learning strategy to overcome the problem of limited patient data. Transfer learning used a 16-layer CNN (VGG-16) of the large sample dataset and was refined with our secondary training dataset comprising 400 images. A separate test dataset comprising 100 images was evaluated to compare the performance of CNN with diagnosis results produced by oral and maxillofacial specialists.ResultsThe sensitivity, specificity, accuracy, and diagnostic time were 81.8%, 83.3%, 83.0%, and 38 seconds, respectively, for the CNN. These values for the oral and maxillofacial specialist were 81.1%, 83.2%, 82.9%, and 23.1 minutes, respectively.ConclusionsAmeloblastomas and KCOTs could be detected based on digital panoramic radiographic images using CNN with accuracy comparable to that of manual diagnosis by oral maxillofacial specialists. These results demonstrate that CNN may aid in screening for ameloblastomas and KCOTs in a substantially shorter time.

【 授权许可】

Unknown