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Objective: The three-dimensional morphological structures of periodontal ligaments (PDLs) are important data for periodontal, orthodontic, prosthodontic, and implant interventions. This study aimed to employ a deep learning (DL) algorithm to segment the PDL automatically in cone-beam computed tomography (CBCT). Method: This was a retrospective study. We randomly selected 389 patients and 1734 axial CBCT images from the CBCT database, and designed a fully automatic PDL segmentation computer-aided model based on instance segmentation Mask R-CNN network. The labels of the model training were ‘teeth’ and ‘alveolar bone’, and the ‘PDL’ is defined as the region where the ‘teeth’ and ‘alveolar bone’ overlap. The model’s segmentation performance was evaluated using CBCT data from eight patients outside the database. Results: Qualitative evaluation indicates that the PDL segmentation accuracy of incisors, canines, premolars, wisdom teeth, and implants reached 100%. The segmentation accuracy of molars was 96.4%. Quantitative evaluation indicates that the mIoU and mDSC of PDL segmentation were 0.667 ± 0.015 (>0.6) and 0.799 ± 0.015 (>0.7) respectively. Conclusion: This study analysed a unique approach to AI-driven automatic segmentation of PDLs on CBCT imaging, possibly enabling chair-side measurements of PDLs to facilitate periodontists, orthodontists, prosthodontists, and implantologists in more efficient and accurate diagnosis and treatment planning.

Abstract Environmental degradation has profoundly impacted both human society and ecosystems. The environmental Kuznets curve (EKC) illuminates the intricate relationship between economic growth and environmental decline. However, the recent surge in trade protectionism has heightened global economic uncertainties, posing a severe threat to global environmental sustainability. This research aims to investigate the intricate pathways through which trade protection, assessed by available trade openness data, influences the nexus between economic growth and environmental degradation. Leveraging comprehensive global panel data spanning 147 countries from 1995 to 2018, this study meticulously examines the non-linear dynamics among trade, economy, and the environment, with a particular emphasis on validating the EKC hypothesis. This study encompasses exhaustive global and panel data regressions categorized across four income groups. The research substantiates the validity of the EKC hypothesis within the confines of this investigation. As income levels rise, the impact of economic growth on environmental degradation initially intensifies before displaying a diminishing trend. Additionally, trade protection manifests as a detriment to improving global environmental quality. The ramifications of trade protectionism display nuanced variations across income strata. In high-income nations, trade protection appears to contribute to mitigating environmental degradation. Conversely, within other income brackets, the stimulating effect of trade protection on environmental pressure is more conspicuous. In other words, trade protectionism exacerbates environmental degradation, particularly affecting lower-income countries, aligning with the concept of pollution havens. The study’s results illuminate nuanced thresholds in the relationship between trade, economic growth, and environmental degradation across income groups, emphasizing the heterogeneous impact and underlying mechanisms. These findings provide valuable insights for policymakers, urging collaborative efforts among nations to achieve a harmonious balance between economic advancement and environmental preservation on a global scale.