SUCCESS OF ARTIFICIAL INTELLIGENCE SYSTEM IN DETERMINING ALVEOLAR BONE LOSS FROM DENTAL PANORAMIC RADIOGRAPHY IMAGES

Sevda Kurt; Özer Çelik; İbrahim Şevki Bayrakdar; Kaan Orhan; Elif Bilgir; Alper Odabas; Ahmet Faruk Aslan

doi:10.7126/cumudj.777057

TR EN

Dental Panoramik Radyografide Yapay Zeka Sistemi Kullanılarak Alveoler Kemik Kaybının Belirlenmesi

Öz

Amaç: Bu çalışmanın amacı yapay zeka (Artificial Intelligence) (AI) sistemleri kullanılarak dental panoramik radyografik görüntülerden alveoler kemik kaybını tespit etmektir. Gereç ve Yöntem: Bu çalışmada toplam 2276 panoramik radyografik görüntü kullanıldı. Bunların 1137'si kemik yıkımı olan vakalara aitken, 1139'u periodontal olarak sağlıklıydı. Veri kümesi eğitim (n = 1856), doğrulama (n = 210) ve test seti (n = 210) olarak üç bölüme ayrıldı. Veri setindeki tüm görüntüler eğitimden önce 1472x718 piksel olarak yeniden boyutlandırıldı. Açık kaynaklı python programlama dili ve OpenCV, NumPy, Pandas ve Matplotlib kütüphaneleri etkili bir şekilde kullanılarak rastgele bir dizi oluşturuldu. Ön işleme için önceden eğitilmiş bir Google Net Inception v3 CNN ağı kullanılmış ve veri setleri aktarım öğrenimi kullanılarak eğitildi. Tanısal performans, duyarlılık, özgüllük, kesinlik, doğruluk ve F1 skoru kullanılarak konfüzyon matrisi ile değerlendirildi. Bulgular: Kemik kaybı olan 105 olgunun 99'u AI sistemi ile tespit edildi. Duyarlılık 0.94, özgüllük 0.88, hassasiyet 0.89, doğruluk 0.91 ve F1 skoru 0.91 idi. Sonuç: Konvolüsyon nöral ağ modeli periodontal kemik kayıplarını belirlemede başarılıdır. Gelecekte tanı ve tedavi planlamasında hekimlerin çalışmasını kolaylaştıran bir sistem olarak kullanılabilir.

Anahtar Kelimeler

SUCCESS OF ARTIFICIAL INTELLIGENCE SYSTEM IN DETERMINING ALVEOLAR BONE LOSS FROM DENTAL PANORAMIC RADIOGRAPHY IMAGES

Abstract

Objectives: The aim of this study was to detect alveolar bone loss from dental panoramic radiographic images using artificial intelligence systems. Material and Methods: A total of 2276 panoramic radiographic images were used in this study. While 1137 of them belong to cases with bone destruction, 1139 were periodontally healthy. The dataset is divided into three parts as training (n=1856) , validation (n=210) and testing set (n= 210). All images in the data set were resized to 1472x718 pixels before training. A random sequence was created using the open-source python programming language and OpenCV, NumPy, Pandas, and Matplotlib libraries effectively. A pre-trained Google Net Inception v3 CNN network was used for preprocessing and data sets were trained using transfer learning. Diagnostic performance was evaluated with the confusion matrix using sensivitiy, specificity, precision, accuracy and F1 score. Results: Of the 105 cases with bone loss, 99 were detected by the AI system. Sensitivity was 0.94, specificity 0.88, precision 0.89, accuracy 0.91 and F1 score 0.91. Conclusion: The convolutional neural network model is successful in determining periodontal bone losses. It can be used as a system to facilitate the work of physicians in diagnosis and treatment planning in the future.

Keywords

References

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Details

Primary Language

English

Subjects

Health Care Administration

Journal Section

Research Article

Authors

Sevda Kurt
0000-0002-3711-6520
Türkiye

Özer Çelik
Türkiye

İbrahim Şevki Bayrakdar ^*
0000-0001-5036-9867
Türkiye

Kaan Orhan
0000-0001-6768-0176
Türkiye

Elif Bilgir
0000-0001-9521-4682
Türkiye

Alper Odabas
0000-0002-4361-3056
Türkiye

Ahmet Faruk Aslan
0000-0003-1583-6508
Türkiye

Publication Date

December 31, 2020

Submission Date

August 4, 2020

Acceptance Date

October 6, 2020

Published in Issue

Year 2020 Volume: 23 Number: 4

DOI

https://doi.org/10.7126/cumudj.777057

IZ

https://izlik.org/JA66HJ72TY

Cite

RIS / Bibtex

EndNote

Kurt S, Çelik Ö, Bayrakdar İŞ, Orhan K, Bilgir E, Odabas A, Aslan AF (December 1, 2020) SUCCESS OF ARTIFICIAL INTELLIGENCE SYSTEM IN DETERMINING ALVEOLAR BONE LOSS FROM DENTAL PANORAMIC RADIOGRAPHY IMAGES. Cumhuriyet Dental Journal 23 4 318–324.