ABSTRACT
OBJECTIVE: To investigate the effectiveness of a convolutional neural network (CNN) in detecting healthy teeth and early carious lesions on occlusal surfaces and to assess the applicability of this deep learning algorithm as an auxiliary aid. MATERIALS AND METHODS: A total of 2,481 posterior teeth (2,459 permanent and 22 deciduous teeth) with varying stages of carious lesions were classified according to the International Caries Detection and Assessment System (ICDAS). After clinical evaluation, ICDAS 0 and 2 occlusal surfaces were photographed with a professional digital camera. VGG-19 was chosen as the CNN and the findings were compared with those of a reference examiner to evaluate its detection efficiency. To verify the effectiveness of the CNN as an auxiliary detection aid, three examiners (an undergraduate student (US), a newly graduated dental surgeon (ND), and a specialist in pediatric dentistry (SP) assessed the acquired images (Phase I). In Phase II, the examiners reassessed the same images using the CNN-generated algorithms. RESULTS: The training dataset consisted of 8,749 images, whereas the test dataset included 140 images. VGG-19 achieved an accuracy of 0.879, positive agreement of 0.827, precision of 0.949, negative agreement 0.800, and an F1-score of 0.887. In Phase I, the accuracy rates for examiners US, ND, and SP were 0.543, 0.771, and 0.807, respectively. In Phase II, the accuracy rates improved to 0.679, 0.886, and 0.857 for the respective examiners. The number of correct answers was significantly higher in Phase II than in Phase I for all examiners (McNemar test;P<0.05). CONCLUSIONS: VGG-19 demonstrated satisfactory performance in the detection of early carious lesions, as well as an auxiliary detection aid. CLINICAL RELEVANCE: Automated detection using deep learning algorithms is an important aid in detecting early caries lesions and improves the accuracy of the disease detection, enabling quicker and more reliable clinical decision-making.
Subject(s)
Deep Learning , Dental Caries , Child , Humans , Neural Networks, Computer , Algorithms , Dental Caries/diagnostic imaging , Dental Caries/pathology , StudentsABSTRACT
The draft sequence of the genome of Bradyrhizobium elkanii 587 is presented. This was obtained using Illumina Next-Gen DNA sequencing combined with Sanger sequencing. Genes for the pathways involved in biological nitrogen fixation (the nif gene cluster), nod genes including nodABC, and genes for the type III protein secretion system (T3SS) are present.
Subject(s)
Bradyrhizobium/genetics , Genome, Bacterial , Nitrogen Fixation , Sequence Analysis, DNA , Symbiosis , Bacterial Proteins/genetics , Bradyrhizobium/classification , Bradyrhizobium/metabolism , Brazil , Molecular Sequence Data , Nitrogen Fixation/genetics , Sequence Analysis, DNA/methods , Glycine max/microbiologyABSTRACT
We report the case of an adult male patient with Tourette syndrome, self-injurious behavior and depression, refractory to conventional treatment, and whose symptoms remitted after electroconvulsive therapy. Serial Technetium 99m-Ethyl-Cysteinate-Dimer single photon emission tomographies were applied, before, during, and after electroconvulsive therapy. The neural substrates of this treatment process were further analyzed by woxel-wise subtracted single photon emission tomography images.
