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Artificial intelligence in radiotherapy: a technological review

Ke Sheng

Frontiers of Medicine 2020, Volume 14, Issue 4,   Pages 431-449 doi: 10.1007/s11684-020-0761-1

Abstract: the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcomeprediction, that benefit from AI.

Keywords： artificial intelligence     radiation therapy     medical imaging     treatment planning     quality assurance     outcomeprediction    

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Fertility outcome analysis after modified laparoscopic microsurgical tubal anastomosis

Jihui Ai, Pei Zhang, Lei Jin, Yufeng Li, Jing Yue, Ding Ma, Hanwang Zhang

Frontiers of Medicine 2011, Volume 5, Issue 3,   Pages 310-314 doi: 10.1007/s11684-011-0152-8

Abstract: The current study aims to evaluate the fertility outcome after modified laparoscopic microsurgical tubalunderwent modified laparoscopic microsurgical tubal anastomosis were monitored to investigate the fertility outcome

Keywords： modified laparoscopy     tubal anastomosis     microsurgery    

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FMO3--TMAO axis modulates the clinical outcome in chronic heart-failure patients with reduced ejection

Frontiers of Medicine 2022, Volume 16, Issue 2,   Pages 295-305 doi: 10.1007/s11684-021-0857-2

Abstract: tertile of plasma TMAO was associated with a significant increment in hazard ratio (HR) for the composite outcomeThus, higher plasma TMAO levels indicated increased risk of the composite outcome of cardiovascular death

Keywords： chronic heart failure     trimethylamine-N-oxide     flavin monooxygenase 3     single nucleotide polymorphism    

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Challenges and opportunities in improving left ventricular remodelling and clinical outcome following

Frontiers of Medicine 2021, Volume 15, Issue 3,   Pages 416-437 doi: 10.1007/s11684-021-0852-7

Abstract: with individualised management of hypertension and atrial fibrillation is likely to improve patient outcomepara-valvular aortic regurgitation along with therapy of angiotensin receptor blockade will improve patient outcome

Keywords： surgical aortic valve replacement     trans-catheter aortic valve implantation     left ventricular hypertrophy and fibrosis     myocardial force-velocity relationship     His-Purkinje pacing     renin-angiotensin system inhibitors     coronary access impairment    

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Spatial prediction of soil contamination based on machine learning: a review

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1

Abstract:

● A review of machine learning (ML) for spatial prediction of soil

Keywords： Soil contamination     Machine learning     Prediction     Spatial distribution    

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Outcome of Stretta radiofrequency and fundoplication for GERD-related severe asthmatic symptoms

Zhiwei Hu,Jimin Wu,Zhonggao Wang,Yu Zhang,Weitao Liang,Chao Yan

Frontiers of Medicine 2015, Volume 9, Issue 4,   Pages 437-443 doi: 10.1007/s11684-015-0422-y

Abstract:

This study aimed to investigate the outcome of treatment with Stretta radiofrequency (SRF) or laparoscopicThe outcome of LNF was significantly better than that of SRF in terms of digestive (P <

Keywords： asthma     gastroesophageal reflux     Stretta radiofrequency     laparoscopic Nissen fundoplication    

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Frontiers of Medicine 2022, Volume 16, Issue 3,   Pages 496-506 doi: 10.1007/s11684-021-0828-7

Abstract: The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

Keywords： XGBoost     deep neural network     healthcare     risk prediction    

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Position-varying surface roughness prediction method considering compensated acceleration in milling

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 855-867 doi: 10.1007/s11465-021-0649-z

Abstract: Aiming at surface roughness prediction in the machining process, this paper proposes a position-varyingsurface roughness prediction method based on compensated acceleration by using regression analysis.i>R-square proving the effectiveness of the filtering features, is selected as the input of the predictionMoreover, the prediction curve matches and agrees well with the actual surface state, which verifies

Keywords： surface roughness prediction     compensated acceleration     milling     thin-walled workpiece    

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Improved prediction of pile bending moment and deflection due to adjacent braced excavation

Frontiers of Structural and Civil Engineering doi: 10.1007/s11709-023-0961-2

Abstract: Deep excavations in dense urban areas have caused damage to nearby existing structures in numerous past construction cases. Proper assessment is crucial in the initial design stages. This study develops equations to predict the existing pile bending moment and deflection produced by adjacent braced excavations. Influential parameters (i.e., the excavation geometry, diaphragm wall thickness, pile geometry, strength and small-strain stiffness of the soil, and soft clay thickness) were considered and employed in the developed equations. It is practically unfeasible to obtain measurement data; hence, artificial data for the bending moment and deflection of existing piles were produced from well-calibrated numerical analyses of hypothetical cases, using the three-dimensional finite element method. The developed equations were established through a multiple linear regression analysis of the artificial data, using the transformation technique. In addition, the three-dimensional nature of the excavation work was characterized by considering the excavation corner effect, using the plane strain ratio parameter. The estimation results of the developed equations can provide satisfactory pile bending moment and deflection data and are more accurate than those found in previous studies.

Keywords： pile responses     excavation     prediction     deflection     bending moments    

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Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

Frontiers in Energy 2016, Volume 10, Issue 4,   Pages 479-488 doi: 10.1007/s11708-016-0425-7

Abstract: In this paper a novel method for reliability prediction and validation of nuclear power units in serviceThe accuracy of the reliability prediction can be evaluated according to the comparison between the predictedFurthermore, the reliability prediction method is validated using the nuclear power units in North American

Keywords： nuclear power units in service     reliability     reliability prediction     equivalent availability factors    

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Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

Frontiers of Mechanical Engineering 2010, Volume 5, Issue 2,   Pages 171-175 doi: 10.1007/s11465-009-0091-0

Abstract: Trend prediction technology is the key technology to achieve condition-based maintenance of mechanicalTo ensure the normal operation of units and save maintenance costs, trend prediction technology is studiedThe main methods of the technology are given, the trend prediction method based on neural network isThe industrial site verification shows that the proposed trend prediction technology can reflect the

Keywords： water injection units     condition-based maintenance     trend prediction    

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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Frontiers of Structural and Civil Engineering   Pages 994-1010 doi: 10.1007/s11709-023-0942-5

Abstract: Developing prediction models to support drivers in performing rectifications in advance can effectivelysubsequently, they are preprocessed and used to establish GRU-based multivariate multistep-ahead direct predictionIn addition, the effects of the activation function and input time-step length on the prediction performance

Keywords： dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

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Prediction of the shear wave velocity

Amoroso SARA

Frontiers of Structural and Civil Engineering 2014, Volume 8, Issue 1,   Pages 83-92 doi: 10.1007/s11709-013-0234-6

Abstract: The paper examines the correlations to obtain rough estimates of the shear wave velocity from non-seismic dilatometer tests (DMT) and cone penetration tests (CPT). While the direct measurement of is obviously preferable, these correlations may turn out useful in various circumstances. The experimental results at six international research sites suggest that the DMT predictions of from the parameters (material index), (horizontal stress index), (constrained modulus) are more reliable and consistent than the CPT predictions from (cone resistance), presumably because of the availability, by DMT, of the stress history index .

Keywords： horizontal stress index     shear wave velocity     flat dilatometer test     cone penetration test    

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Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 1,   Pages 72-82 doi: 10.1007/s11709-013-0185-y

Abstract: A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibilityThis paper examines the potential of SVM model in prediction of liquefaction using actual field coneUsing cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefactionto simplify the model, requiring only two parameters ( and maximum horizontal acceleration ), for predictionThe study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based

Keywords： earthquake     cone penetration test     liquefaction     support vector machine (SVM)     prediction    

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Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 4,   Pages 523-535 doi: 10.1007/s11705-021-2083-5

Abstract: Solubility prediction, as an alternative to experiments which can reduce waste and improve crystallizationHerein we used seven descriptors based on understanding dissolution behavior to establish two solubility predictionThe solubility data of 120 active pharmaceutical ingredients (APIs) in ethanol were considered in the predictionFurthermore, a comparison with traditional prediction methods including the modified solubility equationThe highest accuracy shown by the testing set proves that the ML models have the best solubility prediction

Keywords： solubility prediction     machine learning     artificial neural network     random decision forests    

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Title Author Date Type Operation

Artificial intelligence in radiotherapy: a technological review

Ke Sheng

Journal Article

Fertility outcome analysis after modified laparoscopic microsurgical tubal anastomosis

Jihui Ai, Pei Zhang, Lei Jin, Yufeng Li, Jing Yue, Ding Ma, Hanwang Zhang

Journal Article

FMO3--TMAO axis modulates the clinical outcome in chronic heart-failure patients with reduced ejection

Journal Article

Challenges and opportunities in improving left ventricular remodelling and clinical outcome following

Journal Article

Spatial prediction of soil contamination based on machine learning: a review

Journal Article

Outcome of Stretta radiofrequency and fundoplication for GERD-related severe asthmatic symptoms

Zhiwei Hu,Jimin Wu,Zhonggao Wang,Yu Zhang,Weitao Liang,Chao Yan

Journal Article

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Journal Article

Position-varying surface roughness prediction method considering compensated acceleration in milling

Journal Article

Improved prediction of pile bending moment and deflection due to adjacent braced excavation

Journal Article

Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

Journal Article

Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

Journal Article

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Journal Article

Prediction of the shear wave velocity

Amoroso SARA

Journal Article

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Journal Article

Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Journal Article