In Vivo Biocompatibility of an Ionic Liquid-protected Silver Nanoparticle Solution as Root Canal Irrigant

Introduction: The aim of this study was to assess the biocompatibility of positively charged imidazolium-based ionic liquid-protected nanosilver solution (AgNPs) root canal irrigant. Methods and Materials: Eighteen male 4- to 5-month old Sprague-Dawley rats, weighing 200-300 gr were selected and randomly divided into 5 groups: Normal saline 0.9% (group 1), 5.25% NaOCl (group 2), 2.5% NaOCl (group 3), 2.0% chlorhexidine solution (group 4) and AgNPs at 5.7×10-8 M/L (group 5) were randomly injected in 5 sites of dorsal skin of each rat. Tissue inflammatory reaction were evaluated histopathologically after 2 h, 48 h and 14 days. Statistical analysis was done with SPSS version 21 and the Kruskal-Wallis H and Dunn tests were used to find statistically significant differences. The level of significance was set at 0.05. Result: All solutions irritated the highest tissue response after 48 h. Group 1 showed lower inflammatory response compared to groups 2 and 4 (P<0.05). Group 2 displayed higher inflammatory response in comparison with group 5 (P<0.05). Tissue reaction to group 5 was not more severe than the reaction to group 3 or 4. It also would irritate less inflammatory response compared to group 2 (P<0.05). Conclusion: Comparing with NaOCl and CHX, it is possible to label AgNPs as a tissue compatible agent. Keywords: Biocompatibility; Root Canal Irrigant; Silver Nanoparticl

Differential privacy preserved federated transfer learning for multi-institutional 68Ga-PET image artefact detection and disentanglement.

PURPOSE Image artefacts continue to pose challenges in clinical molecular imaging, resulting in misdiagnoses, additional radiation doses to patients and financial costs. Mismatch and halo artefacts occur frequently in gallium-68 (68Ga)-labelled compounds whole-body PET/CT imaging. Correcting for these artefacts is not straightforward and requires algorithmic developments, given that conventional techniques have failed to address them adequately. In the current study, we employed differential privacy-preserving federated transfer learning (FTL) to manage clinical data sharing and tackle privacy issues for building centre-specific models that detect and correct artefacts present in PET images. METHODS Altogether, 1413 patients with 68Ga prostate-specific membrane antigen (PSMA)/DOTA-TATE (TOC) PET/CT scans from 3 countries, including 8 different centres, were enrolled in this study. CT-based attenuation and scatter correction (CT-ASC) was used in all centres for quantitative PET reconstruction. Prior to model training, an experienced nuclear medicine physician reviewed all images to ensure the use of high-quality, artefact-free PET images (421 patients' images). A deep neural network (modified U2Net) was trained on 80% of the artefact-free PET images to utilize centre-based (CeBa), centralized (CeZe) and the proposed differential privacy FTL frameworks. Quantitative analysis was performed in 20% of the clean data (with no artefacts) in each centre. A panel of two nuclear medicine physicians conducted qualitative assessment of image quality, diagnostic confidence and image artefacts in 128 patients with artefacts (256 images for CT-ASC and FTL-ASC). RESULTS The three approaches investigated in this study for 68Ga-PET imaging (CeBa, CeZe and FTL) resulted in a mean absolute error (MAE) of 0.42 ± 0.21 (CI 95%: 0.38 to 0.47), 0.32 ± 0.23 (CI 95%: 0.27 to 0.37) and 0.28 ± 0.15 (CI 95%: 0.25 to 0.31), respectively. Statistical analysis using the Wilcoxon test revealed significant differences between the three approaches, with FTL outperforming CeBa and CeZe (p-value < 0.05) in the clean test set. The qualitative assessment demonstrated that FTL-ASC significantly improved image quality and diagnostic confidence and decreased image artefacts, compared to CT-ASC in 68Ga-PET imaging. In addition, mismatch and halo artefacts were successfully detected and disentangled in the chest, abdomen and pelvic regions in 68Ga-PET imaging. CONCLUSION The proposed approach benefits from using large datasets from multiple centres while preserving patient privacy. Qualitative assessment by nuclear medicine physicians showed that the proposed model correctly addressed two main challenging artefacts in 68Ga-PET imaging. This technique could be integrated in the clinic for 68Ga-PET imaging artefact detection and disentanglement using multicentric heterogeneous datasets

Bolton discrepancy in an Iranian population and its relation with maxillary lateral incisors’ size

Background: Bolton’s two main ratios describing the proportional size of upper and lower teeth, could contribute to estimating the excess or deficiency of tooth size necessary to obtain an ideal occlusion. However, the mean Bolton values are not the same among different societies. Determining the prevalence of tooth size deviations from population-specific Bolton indices might help local orthodontists to have a more concise treatment plan. Objective: The study aimed to define the prevalence of clinically significant tooth size discrepancies (TSD) in an Iranian population and to evaluate the influence of lateral incisors’ size on this discrepancy. Methods: This cross-sectional study was conducted on study casts of orthodontic patients attending Imam Reza Dental Clinic from September 2008 to December 2016. The sample comprised of 150 randomly selected pretreatment study casts (64 males and 86 females from 17 to 28). The mesiodistal diameter of all permanent teeth from the first molar on the right to the first molar on the left was measured using 2 similar digital calipers, and Bolton analysis was calculated. Subjective visual estimation of Bolton discrepancy was also performed. SPSS v18.0, Wilcoxon signed ranks test, Pearson correlation and Receiver Operating Characteristic (ROC) curve analysis were used for statistical analysis. A p<0.05 was considered statistically significant. Results: In the sample group, 34.7% had anterior Bolton index (ABI) and 20.7% had total Bolton index (TBI) greater than 2 Standard Deviations (2SDs) of Bolton’s means, and about half of them required correction of the ABI considering the actual size of discrepancies (mm). The sensitivity of estimating clinically significant tooth size discrepancy more than 2SDs of Bolton’s ABI and the visual judgment was 96.0% and a cut-off point of - 0.12mm was obtained. Conclusion: Bolton's analysis should be routinely performed in all orthodontic patients, and visual estimation of TSD would be suggested as a screening method in the first visit prior to measurements and set-ups

Differential privacy preserved federated transfer learning for multi-institutional ⁶⁸Ga-PET image artefact detection and disentanglement

Purpose: Image artefacts continue to pose challenges in clinical molecular imaging, resulting in misdiagnoses, additional radiation doses to patients and financial costs. Mismatch and halo artefacts occur frequently in gallium-68 (68Ga)-labelled compounds whole-body PET/CT imaging. Correcting for these artefacts is not straightforward and requires algorithmic developments, given that conventional techniques have failed to address them adequately. In the current study, we employed differential privacy-preserving federated transfer learning (FTL) to manage clinical data sharing and tackle privacy issues for building centre-specific models that detect and correct artefacts present in PET images.Methods: Altogether, 1413 patients with 68Ga prostate-specific membrane antigen (PSMA)/DOTA-TATE (TOC) PET/CT scans from 3 countries, including 8 different centres, were enrolled in this study. CT-based attenuation and scatter correction (CT-ASC) was used in all centres for quantitative PET reconstruction. Prior to model training, an experienced nuclear medicine physician reviewed all images to ensure the use of high-quality, artefact-free PET images (421 patients’ images). A deep neural network (modified U2Net) was trained on 80% of the artefact-free PET images to utilize centre-based (CeBa), centralized (CeZe) and the proposed differential privacy FTL frameworks. Quantitative analysis was performed in 20% of the clean data (with no artefacts) in each centre. A panel of two nuclear medicine physicians conducted qualitative assessment of image quality, diagnostic confidence and image artefacts in 128 patients with artefacts (256 images for CT-ASC and FTL-ASC).Results: The three approaches investigated in this study for 68Ga-PET imaging (CeBa, CeZe and FTL) resulted in a mean absolute error (MAE) of 0.42 ± 0.21 (CI 95%: 0.38 to 0.47), 0.32 ± 0.23 (CI 95%: 0.27 to 0.37) and 0.28 ± 0.15 (CI 95%: 0.25 to 0.31), respectively. Statistical analysis using the Wilcoxon test revealed significant differences between the three approaches, with FTL outperforming CeBa and CeZe (p-value &lt; 0.05) in the clean test set. The qualitative assessment demonstrated that FTL-ASC significantly improved image quality and diagnostic confidence and decreased image artefacts, compared to CT-ASC in 68Ga-PET imaging. In addition, mismatch and halo artefacts were successfully detected and disentangled in the chest, abdomen and pelvic regions in 68Ga-PET imaging.Conclusion: The proposed approach benefits from using large datasets from multiple centres while preserving patient privacy. Qualitative assessment by nuclear medicine physicians showed that the proposed model correctly addressed two main challenging artefacts in 68Ga-PET imaging. This technique could be integrated in the clinic for 68Ga-PET imaging artefact detection and disentanglement using multicentric heterogeneous datasets.</p

Differential privacy preserved federated transfer learning for multi-institutional ⁶⁸Ga-PET image artefact detection and disentanglement

Purpose: Image artefacts continue to pose challenges in clinical molecular imaging, resulting in misdiagnoses, additional radiation doses to patients and financial costs. Mismatch and halo artefacts occur frequently in gallium-68 (68Ga)-labelled compounds whole-body PET/CT imaging. Correcting for these artefacts is not straightforward and requires algorithmic developments, given that conventional techniques have failed to address them adequately. In the current study, we employed differential privacy-preserving federated transfer learning (FTL) to manage clinical data sharing and tackle privacy issues for building centre-specific models that detect and correct artefacts present in PET images.Methods: Altogether, 1413 patients with 68Ga prostate-specific membrane antigen (PSMA)/DOTA-TATE (TOC) PET/CT scans from 3 countries, including 8 different centres, were enrolled in this study. CT-based attenuation and scatter correction (CT-ASC) was used in all centres for quantitative PET reconstruction. Prior to model training, an experienced nuclear medicine physician reviewed all images to ensure the use of high-quality, artefact-free PET images (421 patients’ images). A deep neural network (modified U2Net) was trained on 80% of the artefact-free PET images to utilize centre-based (CeBa), centralized (CeZe) and the proposed differential privacy FTL frameworks. Quantitative analysis was performed in 20% of the clean data (with no artefacts) in each centre. A panel of two nuclear medicine physicians conducted qualitative assessment of image quality, diagnostic confidence and image artefacts in 128 patients with artefacts (256 images for CT-ASC and FTL-ASC).Results: The three approaches investigated in this study for 68Ga-PET imaging (CeBa, CeZe and FTL) resulted in a mean absolute error (MAE) of 0.42 ± 0.21 (CI 95%: 0.38 to 0.47), 0.32 ± 0.23 (CI 95%: 0.27 to 0.37) and 0.28 ± 0.15 (CI 95%: 0.25 to 0.31), respectively. Statistical analysis using the Wilcoxon test revealed significant differences between the three approaches, with FTL outperforming CeBa and CeZe (p-value &lt; 0.05) in the clean test set. The qualitative assessment demonstrated that FTL-ASC significantly improved image quality and diagnostic confidence and decreased image artefacts, compared to CT-ASC in 68Ga-PET imaging. In addition, mismatch and halo artefacts were successfully detected and disentangled in the chest, abdomen and pelvic regions in 68Ga-PET imaging.Conclusion: The proposed approach benefits from using large datasets from multiple centres while preserving patient privacy. Qualitative assessment by nuclear medicine physicians showed that the proposed model correctly addressed two main challenging artefacts in 68Ga-PET imaging. This technique could be integrated in the clinic for 68Ga-PET imaging artefact detection and disentanglement using multicentric heterogeneous datasets.</p