Detection of Omicron Caused Pneumonia from Radiology Images Using Convolution Neural Network(CNN)

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.

Clinical features and radiological manifestations of COVID-19 disease

Coronavirus disease 2019(COVID-19)was discovered after unusual cases of severe pneumonia emerged in December 2019 in Wuhan Province(China).Coronavirus is a family of single-stranded RNA viruses.Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)is transmitted from person to person.Although asymptomatic individuals can transmit the virus,symptomatic patients are more contagious.The incubation period ranges from 3-7 d and symptoms are mainly respiratory,including pneumonia or pulmonary embolism in severe cases.Elevated serum levels of interleukins(IL)-2,IL-6,IL-7 indicate the presence of cytokine release syndrome,which is associated with disease severity.The disease has three main phases:Viral infection,pulmonary involvement,and hyperinflammation.To date,no treatment has proved to be safe or effective.Chest X-ray and computed tomography(CT)are the primary imaging tests for diagnosis of SARS-CoV-2 pneumonia,follow-up,and detection of complications.The main radiological findings are ground-glass opacification and areas of consolidation.The long-term clinical course is unknown,although some patients may develop pulmonary fibrosis.Positron emission tomography-computed tomography(PETCT)is useful to assess pulmonary involvement,to define the affected areas,and to assess treatment response.The pathophysiology and clinical course of COVID-19 infection remain poorly understood.However,patterns detected on CT and PETCT may help to diagnose and guide treatment.In this mini review,we analyze the clinical manifestations and radiological findings of COVID-19 infection.

Optimal Hybrid Feature Extraction with Deep Learning for COVID-19 Classifications

Novel coronavirus 2019(COVID-19)has affected the people’s health,their lifestyle and economical status across the globe.The application of advanced Artificial Intelligence(AI)methods in combination with radiological imaging is useful in accurate detection of the disease.It also assists the physicians to take care of remote villages too.The current research paper proposes a novel automatedCOVID-19 analysismethod with the help ofOptimal Hybrid Feature Extraction(OHFE)and Optimal Deep Neural Network(ODNN)called OHFE-ODNN from chest x-ray images.The objective of the presented technique is for performing binary and multi-class classification of COVID-19 analysis from chest X-ray image.The presented OHFE-ODNN method includes a sequence of procedures such as Median Filtering(MF)-based pre-processed,feature extraction and finally,binary(COVID/Non-COVID)and multiclass(Normal,COVID,SARS)classification.Besides,in OHFE-based feature extraction,Gray Level Co-occurrence Matrix(GLCM)and Histogram of Gradients(HOG)are integrated together.The presented OHFE-ODNN model includes Squirrel Search Algorithm(SSA)for finetuning the parameters of DNN.The performance of the presented OHFEODNN technique is conducted using chest x-rays dataset.The presented OHFE-ODNN method classified the binary classes effectively with a maximumprecision of 95.82%,accuracy of 94.01%and F-score of 96.61%.Besides,multiple classes were classified proficiently by OHFE-ODNN model with a precision of 95.63%,accuracy of 95.60%and an F-score of 95.73%.

Suspected uncomplicated cecal diverticulitis diagnosed by imaging:Initial antibiotics vs laparoscopic treatment

AIM:To compare the recurrence rate following initial antibiotic management to that following laparoscopic treatment for suspected uncomplicated cecal diverticulitis. METHODS: We examined the records of 132 patients who were diagnosed with uncomplicated cecal diverticulitis and a first attack during an 8-year period. The diagnosis of uncomplicated diverticulitis was made based on imaging findings, such as inflamed diverticulum or a phlegmon with cecal wall thickening. Concurrent appendiceal dilatation from 8 to 12 mm was observed in 36 patients (27%). One hundred and two patients were treated initially with antibiotics only, whereas 30 underwent laparoscopic treatment, including partial cecectomy (n=8) or appendectomy with diverticulectomy (n=9) or appendectomy alone (n =13). We compared clinical outcomes in both groups over a median follow-up period of 46 mo. RESULTS: All patients were successfully treated with initial therapy. Of the 102 patients who initially received only antibiotic treatment, 6 (6%) had a recurrence (3 in the cecum and 3 in the ascending colon or transverse colon) during the follow-up period. Five of these pa-tients were managed with repeated antibiotic treatmentand 1 underwent ileocolic resection for perforation. Of the 30 patients treated by the laparoscopic approach, 2 (7%) had a recurrence (ascending colon) which was treated with antibiotics. CONCLUSION: Initial antibiotic management for suspected uncomplicated cecal diverticulitis showed comparable effi cacy to laparoscopic treatment in the prevention of recurrence.

Effect of training on resident inter-reader agreement with American College of Radiology Thyroid Imaging Reporting and Data System

BACKGROUND The American College of Radiology Thyroid Imaging Reporting and Data System(ACR TI-RADS)was introduced to standardize the ultrasound characterization of thyroid nodules.Studies have shown that ACR-TIRADS reduces unnecessary biopsies and improves consistency of imaging recommendations.Despite its widespread adoption,there are few studies to date assessing the inter-reader agreement amongst radiology trainees with limited ultrasound experience.We hypothesize that in PGY-4 radiology residents with no prior exposure to ACR TIRADS,a statistically significant improvement in inter-reader reliability can be achieved with a one hour training session.AIM To evaluate the inter-reader agreement of radiology residents in using ACR TIRADS before and after training.METHODS A single center retrospective cohort study evaluating 50 thyroid nodules in 40 patients of varying TI-RADS levels was performed.Reference standard TI-RADS scores were established through a consensus panel of three fellowship-trained staff radiologists with between 1 and 14 years of clinical experience each.Three PGY-4 radiology residents(trainees)were selected as blinded readers for this study.Each trainee had between 4 to 5 mo of designated ultrasound training.No trainee had received specialized TI-RADS training prior to this study.Each of the readers independently reviewed the 50 testing cases and assigned a TI-RADS score to each case before and after TI-RADS training performed 6 wk apart.Fleiss kappa was used to measure the pooled inter-reader agreement.The relative diagnostic performance of readers,pre-and post-training,when compared against the reference standard.RESULTS There were 33 females and 7 males with a mean age of 56.6±13.6 years.The mean nodule size was 19±14 mm(range from 5 to 63 mm).A statistically significant superior inter-reader agreement was found on the post-training assessment compared to the pre-training assessment for the following variables:1.“Shape”(k of 0.09[slight]pre-training vs 0.67[substantial]post-training,P<0.001),2.“Echogenic foci”(k of 0.28[fair]pre-training vs 0.45[moderate]post-training,P=0.004),3.‘TI-RADS level’(k of 0.14[slight]pre-training vs 0.36[fair]post-training,P<0.001)and 4.‘Recommendations’(k of 0.36[fair]pre-training vs 0.50[moderate]post-training,P=0.02).No significant differences between the preand post-training assessments were found for the variables'composition','echogenicity'and'margins'.There was a general trend towards improved pooled sensitivity with TI-RADS levels 1 to 4 for the post-training assessment while the pooled specificity was relatively high(76.6%-96.8%)for all TI-RADS level.CONCLUSION Statistically significant improvement in inter-reader agreement in the assigning TI-RADS level and recommendations after training is observed.Our study supports the use of dedicated ACR TI-RADS training in radiology residents.

Diagnostic value of ACR TI-RADS combined with three-dimensional shear wave elastography in ACR TI-RADS 4 and 5 thyroid nodules

Background:Three-dimensional shear wave elastography(3D-SWE)is a promising method in distinguishing benign and malignant thyroid nodules.By combining with conventional method,it may further improve the diagnostic value.The study aimed to assess the diagnostic value of American College of Radiology(ACR)thyroid imaging reporting and data system(TI-RADS)combined with 3D-SWE in ACR TI-RADS 4 and 5 thyroid nodules.Methods:All nodules were examined by conventional ultrasonography,ACR TI-RADS classification,and 3D-SWE examination.Conventional ultrasonography was used to observe the location,size,shape,margin,echogenicity,taller-than-wide sign,microcalcification,and blood flow of thyroid nodules,and then ACR TI-RADS classification was performed.The Young’s modulus values(3D-C-Emax,3D-C-Emean,and elastography standard deviation[3D-C-Esd])were measured on the reconstructed coronal plane images.According to the receiver operating characteristic(ROC)curve,the best diagnostic efficiency among 3D-C-Emax,3D-C-Emean,and 3D-C-Esd was selected and the cut-off threshold was calculated.According to the surgical pathology,they were divided into benign group and malignant group.And appropriate statistical methods such as t-test and Mann-Whitney U test were used to compare the difference between the two groups.On this basis,3D-SWE combined with conventional ACR TI-RADS was reclassified as combined ACR TI-RADS to determine benign or malignant thyroid nodules.Results:Of the 112 thyroid nodules,62 were malignant and 50 were benign.The optimal cut-off value of three-dimensional maximum Young’s modulus in coronal plane(3D-C-Emax)was 51.5 kPa and the area under the curve(AUC)was 0.798.The AUC,sensitivity,specificity,and accuracy of conventional ACR TI-RADS were 0.828,83.9%,66.0%,and 75.9%,respectively.The AUC,sensitivity,specificity,and accuracy of combined ACR TI-RADS were 0.845,90.3%,66.0%,and 79.5%,respectively.The difference between the two AUC values was statistically significant.Conclusions:Combined ACR TI-RADS has higher diagnostic efficiency than conventional ACR TI-RADS.The sensitivity and accuracy of combined ACR TI-RADS showed significant improvements.It can be used as an effective method in the diagnosis of thyroid nodules.