Artificial intelligence(AI)has been utilized in soft-tissue analysis and prediction in orthodontic treatment planning,although its reliability has not been systematically assessed.This scoping review was conducted to ...Artificial intelligence(AI)has been utilized in soft-tissue analysis and prediction in orthodontic treatment planning,although its reliability has not been systematically assessed.This scoping review was conducted to outline the development of AI in terms of predicting soft-tissue changes after orthodontic treatment,as well as to comprehensively evaluate its prediction accuracy.Six electronic databases(PubMed,EBSCOhost,Web of Science,Embase,Cochrane Library,and Scopus)were searched up to March 14,2023.Clinical studies investigating the performance of AI-based systems in predicting post-orthodontic soft-tissue alterations were included.The Quality Assessment of Diagnostic Accuracy Studies-2(QUADAS-2)and Joanna Briggs Institute(JBI)appraisal checklist for diagnostic test accuracy studies were applied to assess risk of bias,while the Grading of Recommendation,Assessment,Development,and Evaluation(GRADE)assessment was conducted to evaluate the certainty of outcomes.After screening 2500 studies,four non-randomized clinical trials were finally included for full-text evaluation.We found a low level of evidence indicating an estimated high overall accuracy of AI-generated prediction,whereas the lower lip and chin seemed to be the least predictable regions.Furthermore,the facial morphology simulated by AI via the fusion of multimodality images was considered to be reasonably true.Since all of the included studies that were not randomized clinical trials(non-RCTs)showed a moderate to high risk of bias,more well-designed clinical trials with sufficient sample size are needed in future work.展开更多
Intervertebral disc degeneration （IDD） is characterized by disc dehydration and herniation, which is often associated with low back pain and lumbar radiculopathy due to nerve root compression or inflammation. The pa...Intervertebral disc degeneration （IDD） is characterized by disc dehydration and herniation, which is often associated with low back pain and lumbar radiculopathy due to nerve root compression or inflammation. The pathophysiology of IDD is not completely elucidated so far. Some researchers have indicated that disc degeneration begins as early as the second decade of life （Mayer et al., 2013）. Common risk factors are considered to associate with age, gender, smoking history, occupation, disc injury, and biomechanical factors. However, some epidemiologic studies highlighted that disc degeneration may be caused to a large degree by hereditary factors with apparently a relatively minor effects of environmental and behavioral risk factors （Videman et al., 1998; Cheung et al., 2006; Eser et al., 2010; Mayer et al., 2013; Vieira et al., 2014）, which indicated that genetic factors might play an important role in the pathogenesis of IDD.展开更多
Shale oil and gas plays in continental rift basins are complicated and have not been reported elsewhere.In the Luojia area of the Jiyang Depression,an evaluation workflow for shale oil and gas in this continental rift...Shale oil and gas plays in continental rift basins are complicated and have not been reported elsewhere.In the Luojia area of the Jiyang Depression,an evaluation workflow for shale oil and gas in this continental rift basin is proposed.Based on analysis of oil-and gas-related geological conditions,a favorable area of shale oil and gas can be identified,and a high-frequency sequence stratigraphic framework of the target area can be established,therefore,the spatiotemporal distribution of shale has been elucidated in the Luojia area.According to the rock texture,structure,composition and color,petrographic classification criteria for shale are determined,and well log data are used to demarcate,track and predict high-quality lithofacies.Based on geochemical analyses and physical simulations of hydrocarbon generation,abundance,types and maturity of organic matter are analyzed,furthermore,geochemical parameters criteria of hydrocarbon generation and the characteristics of oil and gas occurrence in shales can be determined.Storage space types,assemblages and evolution characteristics of shale reservoirs are studied through core observation,thin-section analysis,electron microscopy examination and fluorescence spectrometry.Combined with analysis of reservoir physical properties,the reservoir performance is evaluated.A saturation model is established based on core analysis,well-log interpretation and well-test production data.The model is further used for evaluation of the movable hydrocarbon contents and integrated assessment of the oil potential.Finally,the shale oil and gas production capacity and exploration prospects in the Luojia area are forecasted based on the analyses of factors controlling production capacity and the rock fracability.Through an integrated analysis of multi-factors(including the lithofacies,source rocks,reservoir properties,oil saturation,and production capacity),the shales in the Luojia area can be divided into three categories,i.e.,Class I(high porosity-high resistivity),Class II(medium porosity-medium resistivity),and Class III(low porosity-medium resistivity).展开更多
基金supported by the Research Grants Council of the Hong Kong,China (No.17109619).
文摘Artificial intelligence(AI)has been utilized in soft-tissue analysis and prediction in orthodontic treatment planning,although its reliability has not been systematically assessed.This scoping review was conducted to outline the development of AI in terms of predicting soft-tissue changes after orthodontic treatment,as well as to comprehensively evaluate its prediction accuracy.Six electronic databases(PubMed,EBSCOhost,Web of Science,Embase,Cochrane Library,and Scopus)were searched up to March 14,2023.Clinical studies investigating the performance of AI-based systems in predicting post-orthodontic soft-tissue alterations were included.The Quality Assessment of Diagnostic Accuracy Studies-2(QUADAS-2)and Joanna Briggs Institute(JBI)appraisal checklist for diagnostic test accuracy studies were applied to assess risk of bias,while the Grading of Recommendation,Assessment,Development,and Evaluation(GRADE)assessment was conducted to evaluate the certainty of outcomes.After screening 2500 studies,four non-randomized clinical trials were finally included for full-text evaluation.We found a low level of evidence indicating an estimated high overall accuracy of AI-generated prediction,whereas the lower lip and chin seemed to be the least predictable regions.Furthermore,the facial morphology simulated by AI via the fusion of multimodality images was considered to be reasonably true.Since all of the included studies that were not randomized clinical trials(non-RCTs)showed a moderate to high risk of bias,more well-designed clinical trials with sufficient sample size are needed in future work.
文摘Intervertebral disc degeneration （IDD） is characterized by disc dehydration and herniation, which is often associated with low back pain and lumbar radiculopathy due to nerve root compression or inflammation. The pathophysiology of IDD is not completely elucidated so far. Some researchers have indicated that disc degeneration begins as early as the second decade of life （Mayer et al., 2013）. Common risk factors are considered to associate with age, gender, smoking history, occupation, disc injury, and biomechanical factors. However, some epidemiologic studies highlighted that disc degeneration may be caused to a large degree by hereditary factors with apparently a relatively minor effects of environmental and behavioral risk factors （Videman et al., 1998; Cheung et al., 2006; Eser et al., 2010; Mayer et al., 2013; Vieira et al., 2014）, which indicated that genetic factors might play an important role in the pathogenesis of IDD.
基金This work was funded by National Science and Technology Major Project of China(Grant No.2011ZX05006-003).
文摘Shale oil and gas plays in continental rift basins are complicated and have not been reported elsewhere.In the Luojia area of the Jiyang Depression,an evaluation workflow for shale oil and gas in this continental rift basin is proposed.Based on analysis of oil-and gas-related geological conditions,a favorable area of shale oil and gas can be identified,and a high-frequency sequence stratigraphic framework of the target area can be established,therefore,the spatiotemporal distribution of shale has been elucidated in the Luojia area.According to the rock texture,structure,composition and color,petrographic classification criteria for shale are determined,and well log data are used to demarcate,track and predict high-quality lithofacies.Based on geochemical analyses and physical simulations of hydrocarbon generation,abundance,types and maturity of organic matter are analyzed,furthermore,geochemical parameters criteria of hydrocarbon generation and the characteristics of oil and gas occurrence in shales can be determined.Storage space types,assemblages and evolution characteristics of shale reservoirs are studied through core observation,thin-section analysis,electron microscopy examination and fluorescence spectrometry.Combined with analysis of reservoir physical properties,the reservoir performance is evaluated.A saturation model is established based on core analysis,well-log interpretation and well-test production data.The model is further used for evaluation of the movable hydrocarbon contents and integrated assessment of the oil potential.Finally,the shale oil and gas production capacity and exploration prospects in the Luojia area are forecasted based on the analyses of factors controlling production capacity and the rock fracability.Through an integrated analysis of multi-factors(including the lithofacies,source rocks,reservoir properties,oil saturation,and production capacity),the shales in the Luojia area can be divided into three categories,i.e.,Class I(high porosity-high resistivity),Class II(medium porosity-medium resistivity),and Class III(low porosity-medium resistivity).
