Performance evaluation of deep learning models for the classification and identification of dental implants

Abstract

Statement of problem: Dental implant systems can be identified using image classification deep learning. However, investigations on the accuracy of classifying and identifying implant design through an object detection model are lacking.

Purpose: The purpose of this study was to evaluate the performance of an object detection deep learning model for classifying the implant designs of 103 types of implants.

Material and methods: From panoramic radiographs, 14 037 implant images were extracted. Implant designs were subdivided into 10 classes in the coronal, 13 in the middle, and 10 in the apical third. Classes with fewer than 50 images were excluded from the training dataset. Among the images, 80% were used as training data, and the remaining 20% as test data; the data were generated 3 times for 3-fold cross-validation (implant datasets 1, 2, and 3). Versions 5 and 7 of you only look once (YOLO) algorithm were used to train the model, and the mean average precision (mAP) was evaluated. Subsequently, data augmentation was performed using image processing and a real-enhanced super-resolution generative adversarial network, and the accuracy was re-evaluated using YOLOv7.

Results: The mAP of YOLOv7 in the 3 datasets was 0.931, 0.984, and 0.884, respectively, which were higher than the mAP of YOLOv5. After image processing in implant dataset-1, the mAP improved to 0.986 and, with the real-enhanced super-resolution generative adversarial network, to 0.988 and 0.986 at magnification ×2 and ×4, respectively.

Conclusions: The object detection model for classifying implant designs found a high accuracy for 26 classes. The mAP of the model differed depending on the type of algorithm, image processing process, and detailed implant design.