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Artificial intelligence in radiotherapy: a technological review

Ke Sheng

《医学前沿（英文）》 2020年 第14卷 第4期   页码 431-449 doi: 10.1007/s11684-020-0761-1

摘要： Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned.

关键词： artificial intelligence     radiation therapy     medical imaging     treatment planning     quality assurance     outcome prediction    

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Fertility outcome analysis after modified laparoscopic microsurgical tubal anastomosis

null

《医学前沿（英文）》 2011年 第5卷 第3期   页码 310-314 doi: 10.1007/s11684-011-0152-8

摘要：

Modified laparoscopic microsurgical tubal anastomosis is an alternative for microsurgical anastomosis via laparotomy to reverse sterilization in women with renewed child wish. The current study aims to evaluate the fertility outcome after modified laparoscopic microsurgical tubal anastomosis. A retrospective study was performed. Fifty-eight women who underwent modified laparoscopic microsurgical tubal anastomosis were monitored to investigate the fertility outcome and characteristics of this new technology. Of the 58 patients, the cumulative pregnancy rate (PR) in the 42 patients with follow-up data was 23.8% (10/42), 57.1% (24/42), 66.7% (28/42), and 73.8% (31/42) within 6, 12, 24, and 36 months after surgery, respectively. The intrauterine PR was 69.0% (29/42). Two patients (4.8%) had ectopic pregnancies that occurred within 24 months of surgery; three cases ended in spontaneous abortion. The delivery rate was 83.9% (26/31). The length of operating time was 1.2±0.3 h, with a range of 1.0–2.5 h (60–145 min), and the mean time was approximately 75 min. The blood loss was relatively small, between 10 and 50 ml with an average amount of 22 ml. Thus, the modified laparoscopic tubal anastomosis is a highly successful procedure and a viable alternative to open abdominal microsurgical approaches. Compared with the traditional laparoscopic tubal sterilization reversal, this modified approach has three advantages: (1) less invasive approach via a trocar reduction; (2) remodeling of tube is better performing tied together after 3–4 sutures; and (3) faster operating time.

关键词： modified laparoscopy     tubal anastomosis     microsurgery    

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

《医学前沿（英文）》 2022年 第16卷 第2期   页码 295-305 doi: 10.1007/s11684-021-0857-2

摘要： The association among plasma trimethylamine-N-oxide (TMAO), FMO3 polymorphisms, and chronic heart failure (CHF) remains to be elucidated. TMAO is a microbiota-dependent metabolite from dietary choline and carnitine. A prospective study was performed including 955 consecutively diagnosed CHF patients with reduced ejection fraction, with the longest follow-up of 7 years. The concentrations of plasma TMAO and its precursors, namely, choline and carnitine, were determined by liquid chromatography-mass spectrometry, and the FMO3 E158K polymorphisms (rs2266782) were genotyped. The top tertile of plasma TMAO was associated with a significant increment in hazard ratio (HR) for the composite outcome of cardiovascular death or heart transplantation (HR=1.47, 95% CI=1.13–1.91, P=0.004) compared with the lowest tertile. After adjustments of the potential confounders, higher TMAO could still be used to predict the risk of the primary endpoint (adjusted HR=1.33, 95% CI=1.01–1.74, P=0.039). This result was also obtained after further adjustment for carnitine (adjusted HR=1.33, 95% CI=1.01–1.74, P=0.039). The FMO3 rs2266782 polymorphism was associated with the plasma TMAO concentrations in our cohort, and lower TMAO levels were found in the AA-genotype. Thus, higher plasma TMAO levels indicated increased risk of the composite outcome of cardiovascular death or heart transplantation independent of potential confounders, and the FMO3 AA-genotype in rs2266782 was related to lower plasma TMAO levels.

关键词： 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

《医学前沿（英文）》 2021年 第15卷 第3期   页码 416-437 doi: 10.1007/s11684-021-0852-7

摘要： Over the last half century, surgical aortic valve replacement (SAVR) has evolved to offer a durable and efficient valve haemodynamically, with low procedural complications that allows favourable remodelling of left ventricular (LV) structure and function. The latter has become more challenging among elderly patients, particularly following trans-catheter aortic valve implantation (TAVI). Precise understanding of myocardial adaptation to pressure and volume overloading and its responses to valve surgery requires comprehensive assessments from aortic valve energy loss, valvular-vascular impedance to myocardial activation, force-velocity relationship, and myocardial strain. LV hypertrophy and myocardial fibrosis remains as the structural and morphological focus in this endeavour. Early intervention in asymptomatic aortic stenosis or regurgitation along with individualised management of hypertension and atrial fibrillation is likely to improve patient outcome. Physiological pacing via the His-Purkinje system for conduction abnormalities, further reduction in para-valvular aortic regurgitation along with therapy of angiotensin receptor blockade will improve patient outcome by facilitating hypertrophy regression, LV coordinate contraction, and global vascular function. TAVI leaflet thromboses require anticoagulation while impaired access to coronary ostia risks future TAVI-in-TAVI or coronary interventions. Until comparable long-term durability and the resolution of TAVI related complications become available, SAVR remains the first choice for lower risk younger patients.

关键词： 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

《环境科学与工程前沿（英文）》 2023年 第17卷 第8期 doi: 10.1007/s11783-023-1693-1

摘要：

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

关键词： Soil contamination     Machine learning     Prediction     Spatial distribution    

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

null

《医学前沿（英文）》 2015年 第9卷 第4期   页码 437-443 doi: 10.1007/s11684-015-0422-y

摘要：

This study aimed to investigate the outcome of treatment with Stretta radiofrequency (SRF) or laparoscopic Nissen fundoplication (LNF). A total of 137 gastroesophageal reflux disease (GERD) patients with severe asthmatic symptoms who responded inadequately to medical treatment for asthma were investigated. The patients were followed up 1 year and 5 years after SRF (n = 82) or LNF (n = 55) treatment. A questionnaire covering 29 related symptoms and medication use was employed. Digestive, respiratory, and ear-nose-throat (ENT) symptom scores significantly decreased after antireflux treatment. Symptom scores respectively changed from 17.2±10.1, 31.9±6.6, and 21.1±11.8 to 5.0±6.2, 11.5±10.2, and 6.3±6.8 at 1 year and to 5.6±6.5, 13.1±10.1, and 7.8±7.2 at 5 years (P <0.001). The outcome of LNF was significantly better than that of SRF in terms of digestive (P <0.001, P = 0.001), respiratory (P = 0.006, P = 0.001), and ENT symptoms (P = 0.006, P = 0.003) at both 1 year and 5 years. SRF and LNF were both effective against the digestive symptoms of GERD as well as GERD-related severe asthmatic and ENT symptoms, with better outcomes exhibited by the LNF group. Severe asthmatic symptoms and GERD were closely associated, and this finding warrants further study.

关键词： 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

《医学前沿（英文）》 2022年 第16卷 第3期   页码 496-506 doi: 10.1007/s11684-021-0828-7

摘要： 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.

关键词： XGBoost     deep neural network     healthcare     risk prediction    

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

《机械工程前沿（英文）》 2021年 第16卷 第4期   页码 855-867 doi: 10.1007/s11465-021-0649-z

摘要： Machined surface roughness will affect parts’ service performance. Thus, predicting it in the machining is important to avoid rejects. Surface roughness will be affected by system position dependent vibration even under constant parameter with certain toolpath processing in the finishing. Aiming at surface roughness prediction in the machining process, this paper proposes a position-varying surface roughness prediction method based on compensated acceleration by using regression analysis. To reduce the stochastic error of measuring the machined surface profile height, the surface area is repeatedly measured three times, and Pauta criterion is adopted to eliminate abnormal points. The actual vibration state at any processing position is obtained through the single-point monitoring acceleration compensation model. Seven acceleration features are extracted, and valley, which has the highest R-square proving the effectiveness of the filtering features, is selected as the input of the prediction model by mutual information coefficients. Finally, by comparing the measured and predicted surface roughness curves, they have the same trends, with the average error of 16.28% and the minimum error of 0.16%. Moreover, the prediction curve matches and agrees well with the actual surface state, which verifies the accuracy and reliability of the model.

关键词： 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

《结构与土木工程前沿（英文）》 doi: 10.1007/s11709-023-0961-2

摘要： 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.

关键词： 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

《能源前沿（英文）》 2016年 第10卷 第4期   页码 479-488 doi: 10.1007/s11708-016-0425-7

摘要： In this paper a novel method for reliability prediction and validation of nuclear power units in service is proposed. The equivalent availability factor is used to measure the reliability, and the equivalent availability factor deducting planed outage hours from period hours and maintenance factor are used for the measurement of inherent reliability. By statistical analysis of historical reliability data, the statistical maintenance factor and the undetermined parameter in its numerical model can be determined. The numerical model based on the maintenance factor predicts the equivalent availability factor deducting planed outage hours from period hours, and the planed outage factor can be obtained by using the planned maintenance days. Using these factors, the equivalent availability factor of nuclear power units in the following 3 years can be obtained. Besides, the equivalent availability factor can be predicted by using the historical statistics of planed outage factor and the predicted equivalent availability factor deducting planed outage hours from period hours. The accuracy of the reliability prediction can be evaluated according to the comparison between the predicted and statistical equivalent availability factors. Furthermore, the reliability prediction method is validated using the nuclear power units in North American Electric Reliability Council (NERC) and China. It is found that the relative errors of the predicted equivalent availability factors for nuclear power units of NERC and China are in the range of –2.16% to 5.23% and –2.15% to 3.71%, respectively. The method proposed can effectively predict the reliability index in the following 3 years, thus providing effective reliability management and maintenance optimization methods for nuclear power units.

关键词： 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

《机械工程前沿（英文）》 2010年 第5卷 第2期   页码 171-175 doi: 10.1007/s11465-009-0091-0

摘要： Trend prediction technology is the key technology to achieve condition-based maintenance of mechanical equipment. Large-sized water injection units are key equipment in oilfields. The traditional preventive maintenance is not economical and cannot completely avoid vicious accidents. To ensure the normal operation of units and save maintenance costs, trend prediction technology is studied to achieve condition-based maintenance for water injection units. The main methods of the technology are given, the trend prediction method based on neural network is put forward, and the expert system based on the knowledge is developed. The industrial site verification shows that the proposed trend prediction technology can reflect the operating condition trend change of the water injection units and provide technical means to achieve condition-based predictive maintenance.

关键词： 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

《结构与土木工程前沿（英文）》   页码 994-1010 doi: 10.1007/s11709-023-0942-5

摘要： The moving trajectory of the pipe-jacking machine (PJM), which primarily determines the end quality of jacked tunnels, must be controlled strictly during the entire jacking process. Developing prediction models to support drivers in performing rectifications in advance can effectively avoid considerable trajectory deviations from the designed jacking axis. Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predict the moving trajectory of the PJM. In this framework, operational data are first extracted from a data acquisition system; subsequently, they are preprocessed and used to establish GRU-based multivariate multistep-ahead direct prediction models. To verify the performance of the proposed framework, a case study of a large pipe-jacking project in Shanghai and comparisons with other conventional models (i.e., long short-term memory (LSTM) network and recurrent neural network (RNN)) are conducted. In addition, the effects of the activation function and input time-step length on the prediction performance of the proposed framework are investigated and discussed. The results show that the proposed framework can dynamically and precisely predict the PJM moving trajectory during the pipe-jacking process, with a minimum mean absolute error and root mean squared error (RMSE) of 0.1904 and 0.5011 mm, respectively. The RMSE of the GRU-based models is lower than those of the LSTM- and RNN-based models by 21.46% and 46.40% at the maximum, respectively. The proposed framework is expected to provide an effective decision support for moving trajectory control and serve as a foundation for the application of deep learning in the automatic control of pipe jacking.

关键词： dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

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Prediction of the shear wave velocity

Amoroso SARA

《结构与土木工程前沿（英文）》 2014年 第8卷 第1期   页码 83-92 doi: 10.1007/s11709-013-0234-6

摘要： 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 .

关键词： 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

《结构与土木工程前沿（英文）》 2013年 第7卷 第1期   页码 72-82 doi: 10.1007/s11709-013-0185-y

摘要： A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibility as a classification problem, which is an imperative task in earthquake engineering. This paper examines the potential of SVM model in prediction of liquefaction using actual field cone penetration test (CPT) data from the 1999 Chi-Chi, Taiwan earthquake. The SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRM) induction principle to minimize the error. Using cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefaction using SVM. Further an attempt has been made to simplify the model, requiring only two parameters ( and maximum horizontal acceleration ), for prediction of liquefaction. Further, developed SVM model has been applied to different case histories available globally and the results obtained confirm the capability of SVM model. For Chi-Chi earthquake, the model predicts with accuracy of 100%, and in the case of global data, SVM model predicts with accuracy of 89%. The effect of capacity factor ( ) on number of support vector and model accuracy has also been investigated. The study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based on field CPT data.

关键词： 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

《化学科学与工程前沿（英文）》 2022年 第16卷 第4期   页码 523-535 doi: 10.1007/s11705-021-2083-5

摘要： Solubility has been widely regarded as a fundamental property of small molecule drugs and drug candidates, as it has a profound impact on the crystallization process. Solubility prediction, as an alternative to experiments which can reduce waste and improve crystallization process efficiency, has attracted increasing attention. However, there are still many urgent challenges thus far. Herein we used seven descriptors based on understanding dissolution behavior to establish two solubility prediction models by machine learning algorithms. The solubility data of 120 active pharmaceutical ingredients (APIs) in ethanol were considered in the prediction models, which were constructed by random decision forests and artificial neural network with optimized data structure and model accuracy. Furthermore, a comparison with traditional prediction methods including the modified solubility equation and the quantitative structure-property relationships model was carried out. The highest accuracy shown by the testing set proves that the ML models have the best solubility prediction ability. Multiple linear regression and stepwise regression were used to further investigate the critical factor in determining solubility value. The results revealed that the API properties and the solute-solvent interaction both provide a nonnegligible contribution to the solubility value.

关键词： solubility prediction     machine learning     artificial neural network     random decision forests    

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标题 作者 时间 类型 操作

Artificial intelligence in radiotherapy: a technological review

Ke Sheng

期刊论文

Fertility outcome analysis after modified laparoscopic microsurgical tubal anastomosis

null

期刊论文

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

期刊论文

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

期刊论文

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

期刊论文

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

null

期刊论文

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

期刊论文

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

期刊论文

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

期刊论文

Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

期刊论文

Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

期刊论文

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

期刊论文

Prediction of the shear wave velocity

Amoroso SARA

期刊论文

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

Pijush SAMUI

期刊论文

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

期刊论文