COVID-19 has created a panic all around the globe.It is a contagious dis-ease caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2),originated from Wuhan in December 2019 and spread quickly all over th...COVID-19 has created a panic all around the globe.It is a contagious dis-ease caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2),originated from Wuhan in December 2019 and spread quickly all over the world.The healthcare sector of the world is facing great challenges tackling COVID cases.One of the problems many have witnessed is the misdiagnosis of COVID-19 cases with that of healthy and pneumonia cases.In this article,we propose a deep Convo-lutional Neural Network(CNN)based approach to detect COVID+(i.e.,patients with COVID-19),pneumonia and normal cases,from the chest X-ray images.COVID-19 detection from chest X-ray is suitable considering all aspects in compar-ison to Reverse Transcription Polymerase Chain Reaction(RT-PCR)and Computed Tomography(CT)scan.Several deep CNN models including VGG16,InceptionV3,DenseNet121,DenseNet201 and InceptionResNetV2 have been adopted in this pro-posed work.They have been trained individually to make particular predictions.Empirical results demonstrate that DenseNet201 provides overall better performance with accuracy,recall,F1-score and precision of 94.75%,96%,95%and 95%respec-tively.After careful comparison with results available in the literature,we have found to develop models with a higher reliability.All the studies were carried out using a publicly available chest X-ray(CXR)image data-set.展开更多
Purpose:During fracture fixation,the size of tibial nail is a vital factor affecting the outcomes and thus preoperative estimation of tibial nail length is very important.This study aims to find out whether"olecr...Purpose:During fracture fixation,the size of tibial nail is a vital factor affecting the outcomes and thus preoperative estimation of tibial nail length is very important.This study aims to find out whether"olecranon to 5th metacarpal head"(O-MH)measurement can be used to reliably predict the tibial nail length.Methods:This was a cross sectional study involving 100 volunteers.Measurements were done and recorded by two observers on two separate occasions.Tibial nail length estimation measurement was done from highest point of tibial tuberosity to the tip of the medial malleolus(TT-MM).O-MH measurement was taken from tip of olecranon to the tip of 5th metacarpal head with wrist in neutral position and hand clenched.Statistical analysis was done to find out correlation between two measurements and influence of age,gender and body mass index on them.Results:Paired t-test showed no systematic error between the readings.Intraclass correlation coefficient showed strong agreement in inter and intra observer settings.Strong correlation was found between the TT-MM&O-MH measurements using Pearson's correlation coefficient test(r=0.966).Hierarchical regression analysis showed age,gender and BMI have no statistically significant bearings on these measurements and their correlations.Conclusion:O-MH measurement is a useful and accurate means of estimating tibial nail length preoperatively.展开更多
文摘COVID-19 has created a panic all around the globe.It is a contagious dis-ease caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2),originated from Wuhan in December 2019 and spread quickly all over the world.The healthcare sector of the world is facing great challenges tackling COVID cases.One of the problems many have witnessed is the misdiagnosis of COVID-19 cases with that of healthy and pneumonia cases.In this article,we propose a deep Convo-lutional Neural Network(CNN)based approach to detect COVID+(i.e.,patients with COVID-19),pneumonia and normal cases,from the chest X-ray images.COVID-19 detection from chest X-ray is suitable considering all aspects in compar-ison to Reverse Transcription Polymerase Chain Reaction(RT-PCR)and Computed Tomography(CT)scan.Several deep CNN models including VGG16,InceptionV3,DenseNet121,DenseNet201 and InceptionResNetV2 have been adopted in this pro-posed work.They have been trained individually to make particular predictions.Empirical results demonstrate that DenseNet201 provides overall better performance with accuracy,recall,F1-score and precision of 94.75%,96%,95%and 95%respec-tively.After careful comparison with results available in the literature,we have found to develop models with a higher reliability.All the studies were carried out using a publicly available chest X-ray(CXR)image data-set.
文摘Purpose:During fracture fixation,the size of tibial nail is a vital factor affecting the outcomes and thus preoperative estimation of tibial nail length is very important.This study aims to find out whether"olecranon to 5th metacarpal head"(O-MH)measurement can be used to reliably predict the tibial nail length.Methods:This was a cross sectional study involving 100 volunteers.Measurements were done and recorded by two observers on two separate occasions.Tibial nail length estimation measurement was done from highest point of tibial tuberosity to the tip of the medial malleolus(TT-MM).O-MH measurement was taken from tip of olecranon to the tip of 5th metacarpal head with wrist in neutral position and hand clenched.Statistical analysis was done to find out correlation between two measurements and influence of age,gender and body mass index on them.Results:Paired t-test showed no systematic error between the readings.Intraclass correlation coefficient showed strong agreement in inter and intra observer settings.Strong correlation was found between the TT-MM&O-MH measurements using Pearson's correlation coefficient test(r=0.966).Hierarchical regression analysis showed age,gender and BMI have no statistically significant bearings on these measurements and their correlations.Conclusion:O-MH measurement is a useful and accurate means of estimating tibial nail length preoperatively.