Imaging Science in Dentistry

  • 제52권2호
  • /
  • Pages.219-224
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  • 2022
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  • 2233-7822(pISSN)
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  • 2233-7830(eISSN)
대한영상치의학회 (Korean Academy of Oral and Maxillofacial Radiology)
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Transfer learning in a deep convolutional neural network for implant fixture classification: A pilot study

  • Kim, Hak-Sun (Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry) ;
  • Ha, Eun-Gyu (Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry) ;
  • Kim, Young Hyun (Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry) ;
  • Jeon, Kug Jin (Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry) ;
  • Lee, Chena (Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry) ;
  • Han, Sang-Sun (Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry)
  • 투고 : 2021.12.01
  • 심사 : 2022.01.25
  • 발행 : 2022.06.30
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초록

Purpose: This study aimed to evaluate the performance of transfer learning in a deep convolutional neural network for classifying implant fixtures. Materials and Methods: Periapical radiographs of implant fixtures obtained using the Superline (Dentium Co. Ltd., Seoul, Korea), TS III(Osstem Implant Co. Ltd., Seoul, Korea), and Bone Level Implant(Institut Straumann AG, Basel, Switzerland) systems were selected from patients who underwent dental implant treatment. All 355 implant fixtures comprised the total dataset and were annotated with the name of the system. The total dataset was split into a training dataset and a test dataset at a ratio of 8 to 2, respectively. YOLOv3 (You Only Look Once version 3, available at https://pjreddie.com/darknet/yolo/), a deep convolutional neural network that has been pretrained with a large image dataset of objects, was used to train the model to classify fixtures in periapical images, in a process called transfer learning. This network was trained with the training dataset for 100, 200, and 300 epochs. Using the test dataset, the performance of the network was evaluated in terms of sensitivity, specificity, and accuracy. Results: When YOLOv3 was trained for 200 epochs, the sensitivity, specificity, accuracy, and confidence score were the highest for all systems, with overall results of 94.4%, 97.9%, 96.7%, and 0.75, respectively. The network showed the best performance in classifying Bone Level Implant fixtures, with 100.0% sensitivity, specificity, and accuracy. Conclusion: Through transfer learning, high performance could be achieved with YOLOv3, even using a small amount of data.

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참고문헌

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