当前,疾病编码作为国家数据已经在医院管理、绩效考核、院际交流、医保支付等不同领域发挥着重要作用,对编码的详细、准确性要求不断提高。国家卫生健康委发布的手术操作分类代码国家临床版3.0(2022修订)要求介入性超声作为首页操作必...当前,疾病编码作为国家数据已经在医院管理、绩效考核、院际交流、医保支付等不同领域发挥着重要作用,对编码的详细、准确性要求不断提高。国家卫生健康委发布的手术操作分类代码国家临床版3.0(2022修订)要求介入性超声作为首页操作必填项。编码员应根据手术操作编码的三大要素:疾病部位、手术方法、手术入路,选择正确的主导词查找编码,并核对编码,直至每个要素都涵盖并与实际操作贴合,才能得到一个正确且唯一的编码。本文按照国际疾病分类第9版临床修订本手术与操作(ICD-9-CM-3)2011版的编码原则,结合手术操作分类代码国家临床版3.0(2022修订),对各种超声引导下介入治疗的手术操作编码进行分类探讨,以期提高编码准确性,促进疾病编码的区域性统一,为国家数据库和疾病诊断相关分组(diagnosis related groups,DRGs)提供更准确可靠的数据。展开更多
Objective: The current research study aims to calculate entrance surface air kerma for skull, chest, cervical spine, lumbar spine, and pelvic X-ray examinations in interior posterior and posterior interior positions a...Objective: The current research study aims to calculate entrance surface air kerma for skull, chest, cervical spine, lumbar spine, and pelvic X-ray examinations in interior posterior and posterior interior positions and generate a method for chest dose reduction to decrease radiation risk. Materials and Methods: The indirect dose measurement was used in the current research. The X-ray tube output was measured using RAD-CHECK Plus ionization chamber and the indirect entrance surface air kerma was calculated via applying physical acquisition parameters such as a focus on skin distance, tube current times exposure time (mAs), and applied tube voltage (kV), and applying a mathematical model. Results: The main findings were obtained from comparing the radiation doses with the reference levels of International organizations such as the American College of Radiology and the International Atomic Energy Authority. The mean entrance skin dose for the skull (AP), skull (PA), skull (LAT), cervical spine (PA), cervical spine (LAT), lumbar spine (AP), lumbar spine (LAT), pelvis (AP), and pelvis (LAT) of adult X-ray examinations was within the diagnostic reference dose level values obtained by ACR (2018) except for the ESD for chest (AP) which was 0.88 mGy. Conclusions: The results of the study concluded that by adjusting the applied tube voltage, kV, and tube current product time, mAs decreased the radiation dose to the chest X-ray by 58%.展开更多
COVID-19 disease caused by the SARS-CoV-2 virus has created social and economic disruption across theworld.The ability of the COVID-19 virus to quickly mutate and transfer has created serious concerns across the world...COVID-19 disease caused by the SARS-CoV-2 virus has created social and economic disruption across theworld.The ability of the COVID-19 virus to quickly mutate and transfer has created serious concerns across the world.It is essential to detectCOVID-19 infection caused by different variants to take preventive measures accordingly.The existing method of detection of infections caused by COVID-19 and its variants is costly and time-consuming.The impacts of theCOVID-19 pandemic in developing countries are very drastic due to the unavailability of medical facilities and infrastructure to handle the pandemic.Pneumonia is the major symptom of COVID-19 infection.The radiology of the lungs in varies in the case of bacterial pneumonia as compared to COVID-19-caused pneumonia.The pattern of pneumonia in lungs in radiology images can also be used to identify the cause associated with pneumonia.In this paper,we propose the methodology of identifying the cause(either due to COVID-19 or other types of infections)of pneumonia from radiology images.Furthermore,because different variants of COVID-19 lead to different patterns of pneumonia,the proposed methodology identifies pneumonia,the COVID-19 caused pneumonia,and Omicron caused pneumonia from the radiology images.To fulfill the above-mentioned tasks,we have used three Convolution Neural Networks(CNNs)at each stage of the proposed methodology.The results unveil that the proposed step-by-step solution enhances the accuracy of pneumonia detection along with finding its cause,despite having a limited dataset.展开更多
Automated and autonomous decisions of image classification systems have essential applicability in this modern age even.Image-based decisions are commonly taken through explicit or auto-feature engineering of images.I...Automated and autonomous decisions of image classification systems have essential applicability in this modern age even.Image-based decisions are commonly taken through explicit or auto-feature engineering of images.In forensic radiology,auto decisions based on images significantly affect the automation of various tasks.This study aims to assist forensic radiology in its biological profile estimation when only bones are left.A benchmarked dataset Radiology Society of North America(RSNA)has been used for research and experiments.Additionally,a locally developed dataset has also been used for research and experiments to cross-validate the results.A Convolutional Neural Network(CNN)-based model named computer vision and image processing-net(CVIP-Net)has been proposed to learn and classify image features.Experiments have also been performed on state-of-the-art pertained models,which are alex_net,inceptionv_3,google_net,Residual Network(resnet)_50,and Visual Geometry Group(VGG)-19.Experiments proved that the proposed CNN model is more accurate than other models when panoramic dental x-ray images are used to identify age and gender.The specially designed CNN-based achieved results in terms of standard evaluation measures including accuracy(98.90%),specificity(97.99%),sensitivity(99.34%),and Area under the Curve(AUC)-value(0.99)on the locally developed dataset to detect age.The classification rates of the proposed model for gender estimation were 99.57%,97.67%,98.99%,and 0.98,achieved in terms of accuracy,specificity,sensitivity,and AUC-value,respectively,on the local dataset.The classification rates of the proposed model for age estimation were 96.80%,96.80%,97.03%,and 0.99 achieved in terms of accuracy,specificity,sensitivity,and AUC-value,respectively,on the RSNA dataset.展开更多
文摘当前,疾病编码作为国家数据已经在医院管理、绩效考核、院际交流、医保支付等不同领域发挥着重要作用,对编码的详细、准确性要求不断提高。国家卫生健康委发布的手术操作分类代码国家临床版3.0(2022修订)要求介入性超声作为首页操作必填项。编码员应根据手术操作编码的三大要素:疾病部位、手术方法、手术入路,选择正确的主导词查找编码,并核对编码,直至每个要素都涵盖并与实际操作贴合,才能得到一个正确且唯一的编码。本文按照国际疾病分类第9版临床修订本手术与操作(ICD-9-CM-3)2011版的编码原则,结合手术操作分类代码国家临床版3.0(2022修订),对各种超声引导下介入治疗的手术操作编码进行分类探讨,以期提高编码准确性,促进疾病编码的区域性统一,为国家数据库和疾病诊断相关分组(diagnosis related groups,DRGs)提供更准确可靠的数据。
文摘Objective: The current research study aims to calculate entrance surface air kerma for skull, chest, cervical spine, lumbar spine, and pelvic X-ray examinations in interior posterior and posterior interior positions and generate a method for chest dose reduction to decrease radiation risk. Materials and Methods: The indirect dose measurement was used in the current research. The X-ray tube output was measured using RAD-CHECK Plus ionization chamber and the indirect entrance surface air kerma was calculated via applying physical acquisition parameters such as a focus on skin distance, tube current times exposure time (mAs), and applied tube voltage (kV), and applying a mathematical model. Results: The main findings were obtained from comparing the radiation doses with the reference levels of International organizations such as the American College of Radiology and the International Atomic Energy Authority. The mean entrance skin dose for the skull (AP), skull (PA), skull (LAT), cervical spine (PA), cervical spine (LAT), lumbar spine (AP), lumbar spine (LAT), pelvis (AP), and pelvis (LAT) of adult X-ray examinations was within the diagnostic reference dose level values obtained by ACR (2018) except for the ESD for chest (AP) which was 0.88 mGy. Conclusions: The results of the study concluded that by adjusting the applied tube voltage, kV, and tube current product time, mAs decreased the radiation dose to the chest X-ray by 58%.
文摘COVID-19 disease caused by the SARS-CoV-2 virus has created social and economic disruption across theworld.The ability of the COVID-19 virus to quickly mutate and transfer has created serious concerns across the world.It is essential to detectCOVID-19 infection caused by different variants to take preventive measures accordingly.The existing method of detection of infections caused by COVID-19 and its variants is costly and time-consuming.The impacts of theCOVID-19 pandemic in developing countries are very drastic due to the unavailability of medical facilities and infrastructure to handle the pandemic.Pneumonia is the major symptom of COVID-19 infection.The radiology of the lungs in varies in the case of bacterial pneumonia as compared to COVID-19-caused pneumonia.The pattern of pneumonia in lungs in radiology images can also be used to identify the cause associated with pneumonia.In this paper,we propose the methodology of identifying the cause(either due to COVID-19 or other types of infections)of pneumonia from radiology images.Furthermore,because different variants of COVID-19 lead to different patterns of pneumonia,the proposed methodology identifies pneumonia,the COVID-19 caused pneumonia,and Omicron caused pneumonia from the radiology images.To fulfill the above-mentioned tasks,we have used three Convolution Neural Networks(CNNs)at each stage of the proposed methodology.The results unveil that the proposed step-by-step solution enhances the accuracy of pneumonia detection along with finding its cause,despite having a limited dataset.
文摘Automated and autonomous decisions of image classification systems have essential applicability in this modern age even.Image-based decisions are commonly taken through explicit or auto-feature engineering of images.In forensic radiology,auto decisions based on images significantly affect the automation of various tasks.This study aims to assist forensic radiology in its biological profile estimation when only bones are left.A benchmarked dataset Radiology Society of North America(RSNA)has been used for research and experiments.Additionally,a locally developed dataset has also been used for research and experiments to cross-validate the results.A Convolutional Neural Network(CNN)-based model named computer vision and image processing-net(CVIP-Net)has been proposed to learn and classify image features.Experiments have also been performed on state-of-the-art pertained models,which are alex_net,inceptionv_3,google_net,Residual Network(resnet)_50,and Visual Geometry Group(VGG)-19.Experiments proved that the proposed CNN model is more accurate than other models when panoramic dental x-ray images are used to identify age and gender.The specially designed CNN-based achieved results in terms of standard evaluation measures including accuracy(98.90%),specificity(97.99%),sensitivity(99.34%),and Area under the Curve(AUC)-value(0.99)on the locally developed dataset to detect age.The classification rates of the proposed model for gender estimation were 99.57%,97.67%,98.99%,and 0.98,achieved in terms of accuracy,specificity,sensitivity,and AUC-value,respectively,on the local dataset.The classification rates of the proposed model for age estimation were 96.80%,96.80%,97.03%,and 0.99 achieved in terms of accuracy,specificity,sensitivity,and AUC-value,respectively,on the RSNA dataset.
