Application of Convolutional Neural Network in the Diagnosis of Jaw Tumors

Abstract

Objectives: Ameloblastomas 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.

Methods: Five 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.

Results: The 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.

Conclusions: Ameloblastomas 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.

Keywords: Ameloblastoma; Artificial Intelligence; Odontogenic Tumors; Oral and Maxillofacial Surgeons; Panoramic Radiography.

Figure 1. Original input image (upper) was preprocessed using inverse logarithm transformation and histogram equalization (lower).

Figure 2. Patient with multilocular cystic radiolucency at the right angle of the mandible. The model correctly classifies the ameloblastoma (probability = 0.57) and labels the correct location.

Figure 3. Patient with a unilocular cystic radiolucency at the anterior part of the mandible. The model correctly classifies the ameloblastoma (probability = 0.62) and labels the correct location.

Figure 4. Patient with a unilocular cystic radiolucency at the left angle of the mandible. The model correctly classifies the keratocystic odontogenic tumors (probability = 0.73) and labels the correct location.

Figure 5. Patient with a unilocular cystic radiolucency at the left angle of the mandible. The model correctly classifies the keratocystic odontogenic tumors (probability = 0.67) and labels the correct location.