Deep learning-based prediction of possibility for immediate implant placement using panoramic radiography.

Journal: Scientific reports
PMID: 39939654

Abstract

In this study, we investigated whether deep learning-based prediction of immediate implant placement is possible. Panoramic radiographs of 201 patients with 874 teeth (Group 1: 440 teeth difficult to place implant immediately after extraction, Group 2: 434 teeth possible of immediate implant placement after extraction) for extraction were evaluated for the training and testing of a deep learning model. DenseNet121, ResNet18, ResNet101, ResNeXt101, InceptionNetV3, and InceptionResNetV2 were used. Each model was trained using preprocessed dental data, and the dataset was divided into training, validation, and test sets to evaluate model performance. For each model, the sensitivity, precision, accuracy, balanced accuracy, and F1-score were all greater than 0.90. The results of this study confirm that deep-learning-based prediction of the possibility of immediate implant placement is possible at a fairly accurate level.

Authors

  • Sae Byeol Mun
    Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon 21999, Republic of Korea.
  • Hun Jun Lim
    Department of Oral and Maxillofacial Surgery, Daejeon Dental Hospital, Wonkwang University College of Dentistry, Daejeon, Korea.
  • Young Jae Kim
    Department of Biomedical Engineering, College of Medicine, Gachon University, Gyeonggi-do, Republic of Korea.
  • Bong Chul Kim
    Department of Oral and Maxillofacial Surgery, Daejeon Dental Hospital, Wonkwang University College of Dentistry, Daejeon, Korea. bck@wku.ac.kr.
  • Kwang Gi Kim
    Department of Biomedical Engineering Branch, National Cancer Center, Gyeonggi-do, South Korea.

Keywords

External Resources

Popular Topics
Recent Journals