Neutron radiography is a crucial nondestructive testing technology widely used in the aerospace,military,and nuclear industries.However,because of the physical limitations of neutron sources and collimators,the result...Neutron radiography is a crucial nondestructive testing technology widely used in the aerospace,military,and nuclear industries.However,because of the physical limitations of neutron sources and collimators,the resulting neutron radiographic images inevitably exhibit multiple distortions,including noise,geometric unsharpness,and white spots.Furthermore,these distortions are particularly significant in compact neutron radiography systems with low neutron fluxes.Therefore,in this study,we devised a multi-distortion suppression network that employs a modified generative adversarial network to improve the quality of degraded neutron radiographic images.Real neutron radiographic image datasets with various types and levels of distortion were built for the first time as multi-distortion suppression datasets.Thereafter,the coordinate attention mechanism was incorporated into the backbone network to augment the capability of the proposed network to learn the abstract relationship between ideally clear and degraded images.Extensive experiments were performed;the results show that the proposed method can effectively suppress multiple distortions in real neutron radiographic images and achieve state-of-theart perceptual visual quality,thus demonstrating its application potential in neutron radiography.展开更多
Artificial intelligence(AI)and deep learning are becoming increasingly powerful tools in diagnostic and radiographic medicine.Deep learning has already been utilized for automated detection of pneumonia from chest rad...Artificial intelligence(AI)and deep learning are becoming increasingly powerful tools in diagnostic and radiographic medicine.Deep learning has already been utilized for automated detection of pneumonia from chest radiographs,diabetic retinopathy,breast cancer,skin carcinoma classification,and metastatic lymphadenopathy detection,with diagnostic reliability akin to medical experts.In the World Journal of Orthopedics article,the authors apply an automated and AIassisted technique to determine the hallux valgus angle(HVA)for assessing HV foot deformity.With the U-net neural network,the authors constructed an algorithm for pattern recognition of HV foot deformity from anteroposterior highresolution radiographs.The performance of the deep learning algorithm was compared to expert clinician manual performance and assessed alongside clinician-clinician variability.The authors found that the AI tool was sufficient in assessing HVA and proposed the system as an instrument to augment clinical efficiency.Though further sophistication is needed to establish automated algorithms for more complicated foot pathologies,this work adds to the growing evidence supporting AI as a powerful diagnostic tool.展开更多
BACKGROUND:Swallowing disorder is a common clinical symptom that can lead to a series of complications,including aspiration,aspiration pneumonia,and malnutrition.This study aimed to investigate risk factors of post-ex...BACKGROUND:Swallowing disorder is a common clinical symptom that can lead to a series of complications,including aspiration,aspiration pneumonia,and malnutrition.This study aimed to investigate risk factors of post-extubation dysphagia(PED)in intensive care unit(ICU)patients with endotracheal intubation,and to develop a risk-predictive model for PED,which could serve as an assessment tool for the prevention and control of PED.METHODS:Patients retrospectively selected from June to December 2021 in a tertiary hospital served as the derivation cohort.Patients recruited from the same hospital from March to June 2022served as the external validation cohort for the predictive model.We used a combination of variable screening and least absolute shrinkage and selection operator(LASSO)regression to select the most useful candidate predictors and checked the multicollinearity of independent variables using the variance inflation factor method.Multivariate logistic regression analysis was performed to calculate the odds ratio(OR;95%confidence interval[95%CI])and P-value for each variable to predict diagnosis.The screened risk factors were introduced into R software to build a nomogram model.The performance of the model,including discrimination ability,calibration,and clinical benefit,was evaluated by plotting the receiver operating characteristic(ROC),calibration,and decision curves.RESULTS:A total of 305 patients were included in this study.Among them,235 patients(53PED vs.182 non-PED)were enrolled in the derivation cohort,while 70 patients(17 PED vs.53 nonPED)were enrolled in the validation cohort.The independent predictors included age,pause of sedatives,level of consciousness,activities of daily living(ADL)score,nasogastric tube,sore throat,and voice disorder.These predictors were used to establish the predictive nomogram model.The model demonstrated good discriminative ability,and the area under the ROC curve(AUC)was 0.945(95%CI 0.904-0.970).Applying the predictive model to the validation cohort demonstrated good discrimination with an AUC of 0.907(95%CI 0.831-0.983)and good calibration.The decision-curve analysis of this nomogram showed a net benefit of the model.CONCLUSION:A predictive model that incorporates age,pause of sedatives,level of consciousness,ADL score,nasogastric tube,sore throat,and voice disorder may have the potential to predict PED in ICU patients.展开更多
Owing to the immobility of traditional reactors and spallation neutron sources,the demand for compact thermal neutron radiography(CTNR)based on accelerator neutron sources has rapidly increased in industrial applicati...Owing to the immobility of traditional reactors and spallation neutron sources,the demand for compact thermal neutron radiography(CTNR)based on accelerator neutron sources has rapidly increased in industrial applications.Recently,thermal neutron radiography experiments based on a D-T neutron generator performed by Hefei Institutes of Physical Science indicated a significant resolution deviation between the experimental results and the values calculated using the traditional resolution model.The experimental result was up to 23%lower than the calculated result,which hinders the achievement of the design goal of a compact neutron radiography system.A GEANT4 Monte Carlo code was developed to simulate the CTNR process,aiming to identify the key factors leading to resolution deviation.The effects of a low collimation ratio and high-energy neutrons were analyzed based on the neutron beam environment of the CTNR system.The results showed that the deviation was primarily caused by geometric distortion at low collimation ratios and radiation noise induced by highenergy neutrons.Additionally,the theoretical model was modified by considering the imaging position and radiation noise factors.The modified theoretical model was in good agreement with the experimental results,and the maximum deviation was reduced to 4.22%.This can be useful for the high-precision design of CTNR systems.展开更多
Dysphagia has been classified as a“geriatric syndrome”and can lead to serious complications that result in a tremendous burden on population health and healthcare resources worldwide.A characteristic age-related cha...Dysphagia has been classified as a“geriatric syndrome”and can lead to serious complications that result in a tremendous burden on population health and healthcare resources worldwide.A characteristic age-related change in swa-llowing is defined as“presbyphagia.”Medical imaging has shown some changes that seriously affect the safety and efficacy of swallowing.However,there is a general lack of awareness of the effects of aging on swallowing function and a belief that these changes are part of normal aging.Our review provides an overview of presbyphagia,which has been a neglected health problem for a long time.Attention and awareness of dysphagia in the elderly population should be strengthened,and targeted intervention measures should be actively imple-mented.展开更多
This article summarizes the current rehabilitation treatment methods for dysphagia after stroke,including feeding-swallowing function treatment,acupuncture treatment,neuromuscular electrical stimulation,balloon cathet...This article summarizes the current rehabilitation treatment methods for dysphagia after stroke,including feeding-swallowing function treatment,acupuncture treatment,neuromuscular electrical stimulation,balloon catheter dilatation,etc.,all of which have many clinical applications,hoping to play a guiding role in future research.展开更多
Objective:To evaluate the improvement of dysphagia and the daily amounts of oral fluids served among people who had recently experienced a cerebrovascular stroke(CVS)after applying a nursing intervention protocol.Meth...Objective:To evaluate the improvement of dysphagia and the daily amounts of oral fluids served among people who had recently experienced a cerebrovascular stroke(CVS)after applying a nursing intervention protocol.Methods:A quasi-experimental design was used to examine 60 stroke patients who were randomly and alternatively divided equally into a study group and a control group.Gugging Swallowing Screen(GUSS)and fluid balance chart were used.Within the first 24 h of a patient’s admission,expert nurses were trained in nursing intervention protocol to manage dysphagia and daily oral fluids.Results:Post-nursing intervention,the severity of dysphagia decreased among the study group more than in the control group.Additionally,the study group began taking greater amounts of fluids by the oral route than before the nursing intervention.Conclusions:A standardized nursing intervention protocol is needed to decrease the severity of dysphagia after CVS and increase the amount of daily oral fluids.The presence of a structured theory-based nursing intervention protocol for dysphagia management will greatly contribute to decreasing the consequences of dysphagia after CVS,and can also be expected to attenuate the effects of aspiration pneumonia,dehydration,and malnutrition;further,it also increases adherence to the protocol by both nurses and patients.展开更多
基金supported by National Natural Science Foundation of China(Nos.11905028,12105040)Scientific Research Project of Education Department of Jilin Province(No.JJKH20231294KJ)。
文摘Neutron radiography is a crucial nondestructive testing technology widely used in the aerospace,military,and nuclear industries.However,because of the physical limitations of neutron sources and collimators,the resulting neutron radiographic images inevitably exhibit multiple distortions,including noise,geometric unsharpness,and white spots.Furthermore,these distortions are particularly significant in compact neutron radiography systems with low neutron fluxes.Therefore,in this study,we devised a multi-distortion suppression network that employs a modified generative adversarial network to improve the quality of degraded neutron radiographic images.Real neutron radiographic image datasets with various types and levels of distortion were built for the first time as multi-distortion suppression datasets.Thereafter,the coordinate attention mechanism was incorporated into the backbone network to augment the capability of the proposed network to learn the abstract relationship between ideally clear and degraded images.Extensive experiments were performed;the results show that the proposed method can effectively suppress multiple distortions in real neutron radiographic images and achieve state-of-theart perceptual visual quality,thus demonstrating its application potential in neutron radiography.
文摘Artificial intelligence(AI)and deep learning are becoming increasingly powerful tools in diagnostic and radiographic medicine.Deep learning has already been utilized for automated detection of pneumonia from chest radiographs,diabetic retinopathy,breast cancer,skin carcinoma classification,and metastatic lymphadenopathy detection,with diagnostic reliability akin to medical experts.In the World Journal of Orthopedics article,the authors apply an automated and AIassisted technique to determine the hallux valgus angle(HVA)for assessing HV foot deformity.With the U-net neural network,the authors constructed an algorithm for pattern recognition of HV foot deformity from anteroposterior highresolution radiographs.The performance of the deep learning algorithm was compared to expert clinician manual performance and assessed alongside clinician-clinician variability.The authors found that the AI tool was sufficient in assessing HVA and proposed the system as an instrument to augment clinical efficiency.Though further sophistication is needed to establish automated algorithms for more complicated foot pathologies,this work adds to the growing evidence supporting AI as a powerful diagnostic tool.
文摘BACKGROUND:Swallowing disorder is a common clinical symptom that can lead to a series of complications,including aspiration,aspiration pneumonia,and malnutrition.This study aimed to investigate risk factors of post-extubation dysphagia(PED)in intensive care unit(ICU)patients with endotracheal intubation,and to develop a risk-predictive model for PED,which could serve as an assessment tool for the prevention and control of PED.METHODS:Patients retrospectively selected from June to December 2021 in a tertiary hospital served as the derivation cohort.Patients recruited from the same hospital from March to June 2022served as the external validation cohort for the predictive model.We used a combination of variable screening and least absolute shrinkage and selection operator(LASSO)regression to select the most useful candidate predictors and checked the multicollinearity of independent variables using the variance inflation factor method.Multivariate logistic regression analysis was performed to calculate the odds ratio(OR;95%confidence interval[95%CI])and P-value for each variable to predict diagnosis.The screened risk factors were introduced into R software to build a nomogram model.The performance of the model,including discrimination ability,calibration,and clinical benefit,was evaluated by plotting the receiver operating characteristic(ROC),calibration,and decision curves.RESULTS:A total of 305 patients were included in this study.Among them,235 patients(53PED vs.182 non-PED)were enrolled in the derivation cohort,while 70 patients(17 PED vs.53 nonPED)were enrolled in the validation cohort.The independent predictors included age,pause of sedatives,level of consciousness,activities of daily living(ADL)score,nasogastric tube,sore throat,and voice disorder.These predictors were used to establish the predictive nomogram model.The model demonstrated good discriminative ability,and the area under the ROC curve(AUC)was 0.945(95%CI 0.904-0.970).Applying the predictive model to the validation cohort demonstrated good discrimination with an AUC of 0.907(95%CI 0.831-0.983)and good calibration.The decision-curve analysis of this nomogram showed a net benefit of the model.CONCLUSION:A predictive model that incorporates age,pause of sedatives,level of consciousness,ADL score,nasogastric tube,sore throat,and voice disorder may have the potential to predict PED in ICU patients.
基金supported by the Nuclear Energy Development Project of China (No.[2019]1342)the Presidential Foundation of HFIPS (No.YZJJ2022QN40)。
文摘Owing to the immobility of traditional reactors and spallation neutron sources,the demand for compact thermal neutron radiography(CTNR)based on accelerator neutron sources has rapidly increased in industrial applications.Recently,thermal neutron radiography experiments based on a D-T neutron generator performed by Hefei Institutes of Physical Science indicated a significant resolution deviation between the experimental results and the values calculated using the traditional resolution model.The experimental result was up to 23%lower than the calculated result,which hinders the achievement of the design goal of a compact neutron radiography system.A GEANT4 Monte Carlo code was developed to simulate the CTNR process,aiming to identify the key factors leading to resolution deviation.The effects of a low collimation ratio and high-energy neutrons were analyzed based on the neutron beam environment of the CTNR system.The results showed that the deviation was primarily caused by geometric distortion at low collimation ratios and radiation noise induced by highenergy neutrons.Additionally,the theoretical model was modified by considering the imaging position and radiation noise factors.The modified theoretical model was in good agreement with the experimental results,and the maximum deviation was reduced to 4.22%.This can be useful for the high-precision design of CTNR systems.
基金Supported by 2021 China Disabled Persons'Federation to Fund Projects,No.2021CDPFAT-45.
文摘Dysphagia has been classified as a“geriatric syndrome”and can lead to serious complications that result in a tremendous burden on population health and healthcare resources worldwide.A characteristic age-related change in swa-llowing is defined as“presbyphagia.”Medical imaging has shown some changes that seriously affect the safety and efficacy of swallowing.However,there is a general lack of awareness of the effects of aging on swallowing function and a belief that these changes are part of normal aging.Our review provides an overview of presbyphagia,which has been a neglected health problem for a long time.Attention and awareness of dysphagia in the elderly population should be strengthened,and targeted intervention measures should be actively imple-mented.
基金Supported by Hospital-level Project of Shiyan Taihe Hospital(2019JJXM117)。
文摘This article summarizes the current rehabilitation treatment methods for dysphagia after stroke,including feeding-swallowing function treatment,acupuncture treatment,neuromuscular electrical stimulation,balloon catheter dilatation,etc.,all of which have many clinical applications,hoping to play a guiding role in future research.
文摘Objective:To evaluate the improvement of dysphagia and the daily amounts of oral fluids served among people who had recently experienced a cerebrovascular stroke(CVS)after applying a nursing intervention protocol.Methods:A quasi-experimental design was used to examine 60 stroke patients who were randomly and alternatively divided equally into a study group and a control group.Gugging Swallowing Screen(GUSS)and fluid balance chart were used.Within the first 24 h of a patient’s admission,expert nurses were trained in nursing intervention protocol to manage dysphagia and daily oral fluids.Results:Post-nursing intervention,the severity of dysphagia decreased among the study group more than in the control group.Additionally,the study group began taking greater amounts of fluids by the oral route than before the nursing intervention.Conclusions:A standardized nursing intervention protocol is needed to decrease the severity of dysphagia after CVS and increase the amount of daily oral fluids.The presence of a structured theory-based nursing intervention protocol for dysphagia management will greatly contribute to decreasing the consequences of dysphagia after CVS,and can also be expected to attenuate the effects of aspiration pneumonia,dehydration,and malnutrition;further,it also increases adherence to the protocol by both nurses and patients.
