Age- and sex-specific reference intervals for superoxide dismutase enzyme and several minerals in a healthy adult cohort

Introduction The aim of this study was to establish RIs for clinically important markers including superoxide dismutase (SOD), serum copper, zinc, calcium, magnesium, and phosphate in a cohort of healthy Iranian adults. Materials A subsample from MASHAD cohort study was used to assess serum SOD, copper, zinc, calcium, magnesium and phosphate. Serum SOD was measured according to its inhibitory potential of pyrogallol oxidation. Micro- and macro-minerals were measured using flame atomic absorption spectrometry and a BT3000 autoanalyzer, respectively. Sex- and age-specific RIs were then calculated based on CLSI Ep28-A3 guidelines. Results Reference value distributions for studied parameters did not demonstrate any age-specific differences that were statistically significant. In addition, sex partitioning was not required for all parameters, apart from serum magnesium, which showed a wider range in females (0.81–1.26 mg/dl) compared with males (0.82–1.23 mg/dl). Conclusion The RIs established in this study can be expected to improve mineral assessment and clinical decision-making in the Iranian adult population

Deep Learning for Caries Detection : A Systematic Review

Publisher Copyright: © 2022 Elsevier LtdObjectives Detecting caries lesions is challenging for dentists, and deep learning models may help practitioners to increase accuracy and reliability. We aimed to systematically review deep learning studies on caries detection. Data We selected diagnostic accuracy studies that used deep learning models on dental imagery (including radiographs, photographs, optical coherence tomography images, near-infrared light transillumination images). The latest version of the quality assessment tool for diagnostic accuracy studies (QUADAS-2) tool was used for risk of bias assessment. Meta-analysis was not performed due to heterogeneity in the studies methods and their performance measurements. Sources Databases (Medline via PubMed, Google Scholar, Scopus, Embase) and a repository (ArXiv) were screened for publications published after 2010, without any limitation on language. Study selection From 252 potentially eligible references, 48 studies were assessed full-text and 42 included, using classification (n=26), object detection (n=6), or segmentation models (n=10). A wide range of performance metrics was used; image, object or pixel accuracy ranged between 68%-99%. The minority of studies (n=11) showed a low risk of biases in all domains, and 13 studies (31.0%) low risk for concerns regarding applicability. The accuracy of caries classification models varied, i.e. 71% to 96% on intra-oral photographs, 82% to 99.2% on peri-apical radiographs, 87.6% to 95.4% on bitewing radiographs, 68.0% to 78.0% on near-infrared transillumination images, 88.7% to 95.2% on optical coherence tomography images, and 86.1% to 96.1% on panoramic radiographs. Pooled diagnostic odds ratios varied from 2.27 to 32767. For detection and segmentation models, heterogeneity in reporting did not allow useful pooling. Conclusion An increasing number of studies investigated caries detection using deep learning, with a diverse types of architectures being employed. Reported accuracy seems promising, while study and reporting quality are currently low. Clinical significance Deep learning models can be considered as an assistant for decisions regarding the presence or absence of carious lesions.publishersversionPeer reviewe