BACKGROUND Performing ultrasound during the current pandemic time is quite challenging.To reduce the chances of cross-infection and keep healthcare workers safe,a robotic ultrasound system was developed,which can be c...BACKGROUND Performing ultrasound during the current pandemic time is quite challenging.To reduce the chances of cross-infection and keep healthcare workers safe,a robotic ultrasound system was developed,which can be controlled remotely.It will also pave way for broadening the reach of ultrasound in remote distant rural areas as well.AIM To assess the feasibility of a robotic system in performing abdominal ultrasound and compare it with the conventional ultrasound system.METHODS A total of 21 healthy volunteers were recruited.Ultrasound was performed in two settings,using the robotic arm and conventional hand-held procedure.Images acquired were analyzed by separate radiologists.RESULTS Our study showed that the robotic arm model was feasible,and the results varied based on the organ imaged.The liver images showed no significant difference.For other organs,the need for repeat imaging was higher in the robotic arm,which could be attributed to the radiologist’s learning curve and ability to control the haptic device.The doctor and volunteer surveys also showed significant comfort with acceptance of the technology and they expressed their desire to use it in the future.CONCLUSIONThis study shows that robotic ultrasound is feasible and is the need of the hour during thepandemic.展开更多
The use of machine learning and deep learning has enabled many applications,previously thought of as being impossible.Among all medical fields,cancer care is arguably the most significantly impacted,with precision med...The use of machine learning and deep learning has enabled many applications,previously thought of as being impossible.Among all medical fields,cancer care is arguably the most significantly impacted,with precision medicine now truly being a possibility.The effect of these technologies,loosely known as artificial intelligence,is particularly striking in fields involving images(such as radiology and pathology)and fields involving large amounts of data(such as genomics).Practicing oncologists are often confronted with new technologies claiming to predict response to therapy or predict the genomic make-up of patients.Understanding these new claims and technologies requires a deep understanding of the field.In this review,we provide an overview of the basis of deep learning.We describe various common tasks and their data requirements so that oncologists could be equipped to start such projects,as well as evaluate algorithms presented to them.展开更多
文摘BACKGROUND Performing ultrasound during the current pandemic time is quite challenging.To reduce the chances of cross-infection and keep healthcare workers safe,a robotic ultrasound system was developed,which can be controlled remotely.It will also pave way for broadening the reach of ultrasound in remote distant rural areas as well.AIM To assess the feasibility of a robotic system in performing abdominal ultrasound and compare it with the conventional ultrasound system.METHODS A total of 21 healthy volunteers were recruited.Ultrasound was performed in two settings,using the robotic arm and conventional hand-held procedure.Images acquired were analyzed by separate radiologists.RESULTS Our study showed that the robotic arm model was feasible,and the results varied based on the organ imaged.The liver images showed no significant difference.For other organs,the need for repeat imaging was higher in the robotic arm,which could be attributed to the radiologist’s learning curve and ability to control the haptic device.The doctor and volunteer surveys also showed significant comfort with acceptance of the technology and they expressed their desire to use it in the future.CONCLUSIONThis study shows that robotic ultrasound is feasible and is the need of the hour during thepandemic.
文摘The use of machine learning and deep learning has enabled many applications,previously thought of as being impossible.Among all medical fields,cancer care is arguably the most significantly impacted,with precision medicine now truly being a possibility.The effect of these technologies,loosely known as artificial intelligence,is particularly striking in fields involving images(such as radiology and pathology)and fields involving large amounts of data(such as genomics).Practicing oncologists are often confronted with new technologies claiming to predict response to therapy or predict the genomic make-up of patients.Understanding these new claims and technologies requires a deep understanding of the field.In this review,we provide an overview of the basis of deep learning.We describe various common tasks and their data requirements so that oncologists could be equipped to start such projects,as well as evaluate algorithms presented to them.
