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.
Teknolojik anlamdaki değişiklikler tıp ve diş hekimliği alanında büyük değişimler yaratmıştır. Bu değişime sebep olan en önemli yeniliklerden biri de yapay zekâ teknolojisidir. Tıp ve diş hekimliği alanında hasta sağlık hizmetlerine önemli katkıları ve hekimlere sağladığı kolaylıklar sayesinde gittikçe daha çok tercih edileceği düşünülmektedir. İşlem hızındaki artış, hesaplama gücü, depolama kapasitesi, farklı görevleri yerine getirme yeteneği ve gelişmiş grafik işlem birimleri ve bilgisayarların satın alınabilirliği ile tıpta ve özellikle radyolojide yeni bir dönemin başlangıcı kabul edilmektedir Diş hekimliği alanında da başlayan bu yeni dönem, hastalıkların erken teşhisinin yapılması ve önlenmesinde büyük katkı ortaya koyacaktır. Bu derlemenin amacı yaşadığımız dönem ve gelecek için son derece önemli bir noktada olan yapay zekâ teknolojisinin diş hekimliği alanındaki uygulamalarını anlatmaktır.
Orobanche cumana Wallr. known as sunflower broomrape is a holoparasitic plant that causes huge yield losses in sunflower (Helianthus annuus L.) fields. Genetic characterization, genetic diversity, and race determination studies in O. cumana are very significant for preventing threats in sunflower fields. In this study, the broomrape populations sampled from Edirne, Kırklareli, Tekirdağ, and Adana provinces were used for genetic characterization. The sensitive Özdemirbey sunflower variety was used for growing O. cumana individuals. Eight simple sequence repeat (SSR) loci (Ocum52, Ocum70, Ocum81, Ocum87, Ocum108, Ocum141, Ocum160, and Ocum196) were used for the evaluation of genetic characterization and diversity of broomrape populations. All studied SSR loci were found to be polymorphic and yielded a total of 22 alleles in 143 samples analyzed. Na = 2.089 (mean number of alleles per locus), Ne = 1.390 (mean effective alleles), I = 0.392 (mean Shannon's information index), Ho = 0.156 (mean observed heterozygosity), He = 0.239 (mean expected heterozygosity), and PIC = 0.228 (mean polymorphic information content) were calculated to assess genetic diversity of O. cumana populations.. As a result of molecular variance analysis, it was concluded that found that the genetic diversity of the populations was 38% among the population. The remaining 23% and 39% were due to among individuals and within individuals, respectively. The UPGMA method and STRUCTURE analysis divided the studied populations into 2 groups. Cluster I included LK2013, HT2016, T2018, and LE2013 populations, while group 2 included AE2003, AD2018, and MT2013 populations. The results we obtained have enabled us to reach important genetic diversity information about O. cumana, and the information obtained will provide important contributions for planned studies in the future.
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