Spine fractures account for a large portion of musculoskeletal injuries worldwide. A classification of spine fractures is necessary in order to develop a common language for treatment indications and outcomes. Several...Spine fractures account for a large portion of musculoskeletal injuries worldwide. A classification of spine fractures is necessary in order to develop a common language for treatment indications and outcomes. Several classification systems have been developed based on injury anatomy or mechanisms of action, but they have demonstrated poor reliability, have yielded little prognostic information, and have not been widely used. For this reason, the Arbeitsgemeinschaftfür Osteosynthesefragen(AO) committee has classified thorocolumbar spine injuries based on the pathomorphological criteria into3 types(A: Compression; B: Distraction; C: Axial torque and rotational deformity). Each of these types is further divided into 3 groups and 3 subgroups reflecting progressive scale of morphological damage and the degree of instability. Because of its highly detailed sub classifications, the AO system has shown limited interobserver variability. It is similar to its predecessors in that it does not incorporate the patient's neurologic status.The need for a reliable, reproducible, clinically relevant, prognostic classification system with an optimal balance of ease of use and detail of injury description contributed to the development of a new classification system, the thoracolumbar injury classification and severity score(TLICS). The TLICS defines injury based on three clinical characteristics: injury morphology, integrity of the posterior ligamentous complex, and neurologic status of the patient. The severity score offers prognostic information and is helpful in decision making about surgical vs nonsurgical management.展开更多
Recognition and characterization of central nervous system infections poses a formidable challenge to the neuro-radiologist.Imaging plays a vital role,the lesions typically being relatively inaccessible to tisue sampl...Recognition and characterization of central nervous system infections poses a formidable challenge to the neuro-radiologist.Imaging plays a vital role,the lesions typically being relatively inaccessible to tisue sampling.The results of an accurate diagnosis are endlessly re-warding,given the availability of excellent pharmaco-logical regimen.The availability of numerous magnetic resonance(MR)sequences which provide functional and molecular information is a powerful tool in the hands of the radiologist.However,the plethora of se-quences and the possibilities on each sequence is also intimidating,and often confusing as well as time con-suming.While a large number of reviews have already described in detail the possible imaging findings in each infection,we intend to classify infections based on their imaging characteristics.In this review we describe an algorithm for first classifying the imaging findings into patterns based on basic MR sequences(T1,T2 and enhancement pattern with Gadolinium),and then sub-classify them based on more advanced molecular and functional sequences(Diffusion,Perfusion,Susceptibili-ty imaging,MR Spectroscopy).This patterned approachis intended as a guide to radiologists in-training and in-practice for quickly narrowing their list of differentials when faced with a clinical challenge.The entire content of the article has also been summarised in the form of flow-charts for the purpose of quick reference.展开更多
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Spine fractures account for a large portion of musculoskeletal injuries worldwide. A classification of spine fractures is necessary in order to develop a common language for treatment indications and outcomes. Several classification systems have been developed based on injury anatomy or mechanisms of action, but they have demonstrated poor reliability, have yielded little prognostic information, and have not been widely used. For this reason, the Arbeitsgemeinschaftfür Osteosynthesefragen(AO) committee has classified thorocolumbar spine injuries based on the pathomorphological criteria into3 types(A: Compression; B: Distraction; C: Axial torque and rotational deformity). Each of these types is further divided into 3 groups and 3 subgroups reflecting progressive scale of morphological damage and the degree of instability. Because of its highly detailed sub classifications, the AO system has shown limited interobserver variability. It is similar to its predecessors in that it does not incorporate the patient's neurologic status.The need for a reliable, reproducible, clinically relevant, prognostic classification system with an optimal balance of ease of use and detail of injury description contributed to the development of a new classification system, the thoracolumbar injury classification and severity score(TLICS). The TLICS defines injury based on three clinical characteristics: injury morphology, integrity of the posterior ligamentous complex, and neurologic status of the patient. The severity score offers prognostic information and is helpful in decision making about surgical vs nonsurgical management.
文摘Recognition and characterization of central nervous system infections poses a formidable challenge to the neuro-radiologist.Imaging plays a vital role,the lesions typically being relatively inaccessible to tisue sampling.The results of an accurate diagnosis are endlessly re-warding,given the availability of excellent pharmaco-logical regimen.The availability of numerous magnetic resonance(MR)sequences which provide functional and molecular information is a powerful tool in the hands of the radiologist.However,the plethora of se-quences and the possibilities on each sequence is also intimidating,and often confusing as well as time con-suming.While a large number of reviews have already described in detail the possible imaging findings in each infection,we intend to classify infections based on their imaging characteristics.In this review we describe an algorithm for first classifying the imaging findings into patterns based on basic MR sequences(T1,T2 and enhancement pattern with Gadolinium),and then sub-classify them based on more advanced molecular and functional sequences(Diffusion,Perfusion,Susceptibili-ty imaging,MR Spectroscopy).This patterned approachis intended as a guide to radiologists in-training and in-practice for quickly narrowing their list of differentials when faced with a clinical challenge.The entire content of the article has also been summarised in the form of flow-charts for the purpose of quick reference.
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.
