The purpose of the paper was the assessment of the success of an artificial intelligence (AI) algorithm formed on a deep‐convolutional neural network (D‐CNN) model for the segmentation of apical lesions on dental panoramic radiographs. A total of 470 anonymized panoramic radiographs were used to progress the D‐CNN AI model based on the U‐Net algorithm (CranioCatch, Eskisehir, Turkey) for the segmentation of apical lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Eskisehir Osmangazi University. A U‐Net implemented with PyTorch model (version 1.4.0) was used for the segmentation of apical lesions. In the test data set, the AI model segmented 63 periapical lesions on 47 panoramic radiographs. The sensitivity, precision, and F1‐score for segmentation of periapical lesions at 70% IoU values were 0.92, 0.84, and 0.88, respectively. AI systems have the potential to overcome clinical problems. AI may facilitate the assessment of periapical pathology based on panoramic radiographs.

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.

Bite-wing radiographs are one of the most used intraoral radiography techniques in dentistry. AI is extremely important in terms of more efficient patient care in the field of dentistry. The aim of this study was to perform a diagnostic evaluation on bite-wing radiographs with an AI model based on CNNs. In this study, 500 bite-wing radiographs in the radiography archive of Eskişehir Osmangazi University, Faculty of Dentistry, Department of Oral and Maxillofacial Radiology were used. The CranioCatch labeling program (CranioCatch, Eskisehir, Turkey) with tooth decays, crowns, pulp, restoration material, and root-filling material for five different diagnoses were made by labeling the segmentation technique. The U-Net architecture was used to develop the AI model. F1 score, sensitivity, and precision results of the study, respectively, caries 0.8818–0.8235–0.9491, crown; 0.9629–0.9285–1, pulp; 0.9631–0.9843–0.9429, with restoration material; and 0.9714–0.9622–0.9807 was obtained as 0.9722–0.9459–1 for the root filling material. This study has shown that an AI model can be used to automatically evaluate bite-wing radiographs and the results are promising. Owing to these automatically prepared charts, physicians in a clinical intense tempo will be able to work more efficiently and quickly.

Amaç: Bu çalışmanın amacı Eskişehir Osmangazi Üniversitesi Diş Hekimliği Fakültesi Ağız, Diş ve Çene Radyolojisi Anabilim Dalı Radyoloji kliniğinde çekilmiş Konik Işınlı Bilgisayarlı Tomografi (KIBT)’lerin hangi nedenlerle istendiğini belirlemektir. Gereç-Yöntemler: Ağız, Diş ve Çene Radyolojisi arşivinden rastgele seçilen 843 KIBT’ın istem nedenleri retrospektif olarak değerlendirildi. Hasta bilgi ve yönetim sistemi üzerinde KIBT istemleri için hekimler tarafından kaydedilen Ön tanı/İstem gerekçelerine göre nedenler sınıflandırıldı ve frekansları hesaplandı. Bulgular: Hastaların 403’ü erkek ve 440’ı kadındı. Yaş aralıkları 6-83 (ort 29.41±17.212) idi. En sık istem nedeni olan patolojiler 228 vaka ile % 27 oranındaydı. Ortodontik sebeplerle istenen 185 (% 21.9) ve gömülü dişler için istenen 169 (% 20) vaka ikinci ve üçüncü grupları oluşturuyordu. Maksillofasiyal travma nedeniyle KIBT istenen dört vaka % 0.5 oranla en alt sıradaydı. Sonuç: KIBT’ın uygun maliyeti, düşük radyasyon dozuyla üç boyutlu olarak görüntü oluşturması gibi özellikleri sayesinde diş hekimliği pratiğinde kullanımı yaygınlaşmış olup farklı amaçlar için tercih edilmektedir. Bu avantajlarına rağmen KIBT kullanımında ALARA (As Low As Reasonably Achievable) prensipleri göz önünde bulundurulmalı ve gerekli olmayan durumlarda KIBT isteminden kaçınılmalıdır.