Automatic Feature Segmentation in Dental Periapical Radiographs

ARI T., SAĞLAM H., ÖKSÜZOĞLU H., KAZAN O., BAYRAKDAR İ. Ş., DUMAN Ş. B., ...Daha Fazla

Diagnostics, cilt.12, sa.12, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 12 Sayı: 12
  • Basım Tarihi: 2022
  • Doi Numarası: 10.3390/diagnostics12123081
  • Dergi Adı: Diagnostics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, EMBASE, INSPEC, Directory of Open Access Journals
  • Anahtar Kelimeler: artificial intelligence, periapical radiographs, deep learning, oral diseases, oral findings, CONVOLUTIONAL NEURAL-NETWORK, ARTIFICIAL-INTELLIGENCE, DIAGNOSIS, RADIOLOGY
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

© 2022 by the authors.While a large number of archived digital images make it easy for radiology to provide data for Artificial Intelligence (AI) evaluation; AI algorithms are more and more applied in detecting diseases. The aim of the study is to perform a diagnostic evaluation on periapical radiographs with an AI model based on Convoluted Neural Networks (CNNs). The dataset includes 1169 adult periapical radiographs, which were labelled in CranioCatch annotation software. Deep learning was performed using the U-Net model implemented with the PyTorch library. The AI models based on deep learning models improved the success rate of carious lesion, crown, dental pulp, dental filling, periapical lesion, and root canal filling segmentation in periapical images. Sensitivity, precision and F1 scores for carious lesion were 0.82, 0.82, and 0.82, respectively; sensitivity, precision and F1 score for crown were 1, 1, and 1, respectively; sensitivity, precision and F1 score for dental pulp, were 0.97, 0.87 and 0.92, respectively; sensitivity, precision and F1 score for filling were 0.95, 0.95, and 0.95, respectively; sensitivity, precision and F1 score for the periapical lesion were 0.92, 0.85, and 0.88, respectively; sensitivity, precision and F1 score for root canal filling, were found to be 1, 0.96, and 0.98, respectively. The success of AI algorithms in evaluating periapical radiographs is encouraging and promising for their use in routine clinical processes as a clinical decision support system.