Background Digital twins are virtual representations of devices and processes that capture the physical properties of the environment and operational algorithms/techniques in the context of medical devices and tech-no...Background Digital twins are virtual representations of devices and processes that capture the physical properties of the environment and operational algorithms/techniques in the context of medical devices and tech-nologies.Digital twins may allow healthcare organizations to determine methods of improving medical processes,enhancing patient experience,lowering operating expenses,and extending the value of care.During the present COVID-19 pandemic,various medical devices,such as X-rays and CT scan machines and processes,are constantly being used to collect and analyze medical images.When collecting and processing an extensive volume of data in the form of images,machines and processes sometimes suffer from system failures,creating critical issues for hospitals and patients.Methods To address this,we introduce a digital-twin-based smart healthcare system in-tegrated with medical devices to collect information regarding the current health condition,configuration,and maintenance history of the device/machine/system.Furthermore,medical images,that is,X-rays,are analyzed by using a deep-learning model to detect the infection of COVID-19.The designed system is based on the cascade recurrent convolution neural network(RCNN)architecture.In this architecture,the detector stages are deeper and more sequentially selective against small and close false positives.This architecture is a multi-stage extension of the RCNN model and sequentially trained using the output of one stage for training the other.At each stage,the bounding boxes are adjusted to locate a suitable value of the nearest false positives during the training of the different stages.In this manner,the arrangement of detectors is adjusted to increase the intersection over union,overcoming the problem of overfitting.We train the model by using X-ray images as the model was previously trained on another dataset.Results The developed system achieves good accuracy during the detection phase of COVID-19.The experimental outcomes reveal the efficiency of the detection architecture,which yields a mean average precision rate of 0.94.展开更多
Currently, the diagnosis of tuberculosis (TB) is mainly based on the comprehensive consideration of the patient’s symptoms and signs, laboratory examinations and chest radiography (CXR). CXR plays a pivotal role to s...Currently, the diagnosis of tuberculosis (TB) is mainly based on the comprehensive consideration of the patient’s symptoms and signs, laboratory examinations and chest radiography (CXR). CXR plays a pivotal role to support the early diagnosis of TB, especially when used for TB screening and differential diagnosis. However, high cost of CXR hardware and shortage of certified radiologists poses a major challenge for CXR application in TB screening in resource limited settings. The latest development of artificial intelligence (AI) combined with the accumulation of a large number of medical images provides new opportunities for the establishment of computer-aided detection (CAD) systems in the medical applications, especially in the era of deep learning (DL) technology. Several CAD solutions are now commercially available and there is growing evidence demonstrate their value in imaging diagnosis. Recently, WHO published a rapid communication which stated that CAD may be used as an alternative to human reader interpretation of plain digital CXRs for screening and triage of TB.展开更多
A high energy X-ray digital radiography(DR)nondestructive testing(NDT)system has been developed to detect the operating state of a driving mechanism.The system consists of five main subsystems,namely,X-ray generator,i...A high energy X-ray digital radiography(DR)nondestructive testing(NDT)system has been developed to detect the operating state of a driving mechanism.The system consists of five main subsystems,namely,X-ray generator,image intensifier,image processor,mechanical platform and control subsystem.Owning to the mechanical platform,the X-ray generator and image intensifier are able to rotate around the vertical axis from 0°to 360°in 35 s and move along vertical axis within the range of 500 mm in 20 s.The 450 kV X-ray generator provides a maximum 100 mm penetration depth and a coverage angle of 40°,and the resolution of the scanned image is 66 lp/cm.As is indicated by its applications,the system is featured with fast scanning speed,wide detection range and high imaging quality.It can be applied to inspect the defects in the driving mechanism as well.展开更多
文摘Background Digital twins are virtual representations of devices and processes that capture the physical properties of the environment and operational algorithms/techniques in the context of medical devices and tech-nologies.Digital twins may allow healthcare organizations to determine methods of improving medical processes,enhancing patient experience,lowering operating expenses,and extending the value of care.During the present COVID-19 pandemic,various medical devices,such as X-rays and CT scan machines and processes,are constantly being used to collect and analyze medical images.When collecting and processing an extensive volume of data in the form of images,machines and processes sometimes suffer from system failures,creating critical issues for hospitals and patients.Methods To address this,we introduce a digital-twin-based smart healthcare system in-tegrated with medical devices to collect information regarding the current health condition,configuration,and maintenance history of the device/machine/system.Furthermore,medical images,that is,X-rays,are analyzed by using a deep-learning model to detect the infection of COVID-19.The designed system is based on the cascade recurrent convolution neural network(RCNN)architecture.In this architecture,the detector stages are deeper and more sequentially selective against small and close false positives.This architecture is a multi-stage extension of the RCNN model and sequentially trained using the output of one stage for training the other.At each stage,the bounding boxes are adjusted to locate a suitable value of the nearest false positives during the training of the different stages.In this manner,the arrangement of detectors is adjusted to increase the intersection over union,overcoming the problem of overfitting.We train the model by using X-ray images as the model was previously trained on another dataset.Results The developed system achieves good accuracy during the detection phase of COVID-19.The experimental outcomes reveal the efficiency of the detection architecture,which yields a mean average precision rate of 0.94.
基金National Science and Technology Major Project of China(2017ZX10201302-008)。
文摘Currently, the diagnosis of tuberculosis (TB) is mainly based on the comprehensive consideration of the patient’s symptoms and signs, laboratory examinations and chest radiography (CXR). CXR plays a pivotal role to support the early diagnosis of TB, especially when used for TB screening and differential diagnosis. However, high cost of CXR hardware and shortage of certified radiologists poses a major challenge for CXR application in TB screening in resource limited settings. The latest development of artificial intelligence (AI) combined with the accumulation of a large number of medical images provides new opportunities for the establishment of computer-aided detection (CAD) systems in the medical applications, especially in the era of deep learning (DL) technology. Several CAD solutions are now commercially available and there is growing evidence demonstrate their value in imaging diagnosis. Recently, WHO published a rapid communication which stated that CAD may be used as an alternative to human reader interpretation of plain digital CXRs for screening and triage of TB.
文摘A high energy X-ray digital radiography(DR)nondestructive testing(NDT)system has been developed to detect the operating state of a driving mechanism.The system consists of five main subsystems,namely,X-ray generator,image intensifier,image processor,mechanical platform and control subsystem.Owning to the mechanical platform,the X-ray generator and image intensifier are able to rotate around the vertical axis from 0°to 360°in 35 s and move along vertical axis within the range of 500 mm in 20 s.The 450 kV X-ray generator provides a maximum 100 mm penetration depth and a coverage angle of 40°,and the resolution of the scanned image is 66 lp/cm.As is indicated by its applications,the system is featured with fast scanning speed,wide detection range and high imaging quality.It can be applied to inspect the defects in the driving mechanism as well.
