Role of early CT scan in diagnosis of occult scaphoid fractures- a prospective study

Background: Wrists injuries are one of the common presentations to emergency departments and orthopaedic clinics. The scaphoid bone is the most commonly injured of the carpal bones accounting for 50-80% of carpal injuries and predominantly occurs in young healthy individuals. Scaphoid fractures are the most problematic to diagnose in a clinical setting because it can take up to 6 weeks for scaphoid fractures to become conclusive on plain X-ray films. Aim of the study was to retrospective study was carried out to study the role of early CT scan in diagnosis of occult scaphoid fractures.Methods: A total of 123 patients presented with an acute wrist injury with subsequent signs of scaphoid injury in the absence of a diagnostic fracture on plain X-ray within the time period from June 2014 to May 2016 in a tertiary care centre.Results: This study shows that 31% of normal X-rays were pathological on CT scan and out of these; scaphoid fractures (74% of pathologies) represent a large number of patients with fractures that were missed by initial plain films.Conclusions: This study shows an extremely high false-negative rate for plain X-rays and advocate CT at the first attendance to fracture clinic if there is suspicion of scaphoid injury. An earlier diagnosis leads to appropriate management and reduces restrictions to the patient in terms of prolonged immobilization and repeated clinical reviews.

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The Clinical Scaphoid Fracture: Early Computed Tomography as a Practical Approach

Annals of The Royal College of Surgeons of England ◽

10.1308/003588408x300948 ◽

2008 ◽

Vol 90 (6) ◽

pp. 488-491 ◽

Cited By ~ 19

Author(s):

Q Nguyen ◽

S Chaudhry ◽

R Sloan ◽

I Bhoora ◽

C Willard

Keyword(s):

Ct Scan ◽

General Hospital ◽

Scaphoid Fracture ◽

False Negative ◽

False Negative Rate ◽

District General Hospital ◽

X Rays ◽

Fracture Clinic ◽

Non Union ◽

Scaphoid Fractures

INTRODUCTION Up to 40% of scaphoid fractures are missed at initial presentation as clinical examination and plain radiographs are poor at identifying scaphoid fractures immediately after the injury. Avoiding a delay in diagnosis is essential to prevent the risk of non-union and early wrist arthritis. We demonstrate the use of CT scanning for the early confirmation of a scaphoid fracture. PATIENTS AND METHODS We conducted a retrospective, chronological review of patients who attended an upper limb fracture clinic from January 2001 to October 2003 in a small district general hospital. We performed a CT scan on all ‘clinical scaphoid’ patients who had negative plain X-ray films. RESULTS Overall, 70% of patients had a CT scan within 1 week of injury and not from date of accident and emergency attendance; 83% of patients had a CT scan within 2 weeks of injury. Of 118 patients identified, 32% had positive findings and 22% of ‘clinical scaphoid’ patients had scaphoid fractures. The proportion of positive findings for an acute scaphoid fracture was 68%. Additional pathologies identified on CT were capitate, triquetral and radial fractures. CONCLUSIONS Our audit shows that it is practical to perform CT on suspicious scaphoid fractures in a small district general hospital. We identified an extremely high false-negative rate for plain X-rays and demonstrate that the appropriate use of CT at initial fracture clinic attendance with ‘clinical scaphoid’ leads to an earlier diagnosis and reduces the need for prolonged immobilisation and repeated clinical review.

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COVIDPEN: A Novel COVID-19 Detection Model using Chest X-Rays and CT Scans

10.1101/2020.07.08.20149161 ◽

2020 ◽

Author(s):

Amit Kumar Jaiswal ◽

Prayag Tiwari ◽

Vipin Kumar Rathi ◽

Jia Qian ◽

Hari Mohan Pandey ◽

...

Keyword(s):

False Negative ◽

Healthcare Providers ◽

False Negative Rate ◽

X Rays ◽

X Ray ◽

Detection Model ◽

Global Pandemic ◽

High False Negative Rate ◽

Proposed Model ◽

Chest X Ray

The trending global pandemic of COVID-19 is the fastest ever impact which caused people worldwide by severe acute respiratory syndrome~(SARS)-driven coronavirus. However, several countries suffer from the shortage of test kits and high false negative rate in PCR test. Enhancing the chest X-ray or CT detection rate becomes critical. The patient triage is of utmost importance and the use of machine learning can drive the diagnosis of chest X-ray or CT image by identifying COVID-19 cases. To tackle this problem, we propose~COVIDPEN~-~a transfer learning approach on Pruned EfficientNet-based model for the detection of COVID-19 cases. The proposed model is further interpolated by post-hoc analysis for the explainability of the predictions. The effectiveness of our proposed model is demonstrated on two systematic datasets of chest radiographs and computed tomography scans. Experimental results with several baseline comparisons show that our method is on par and confers clinically explicable instances, which are meant for healthcare providers.

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An Exploration into the Detection of COVID-19 from Chest X-ray Scans Using the xRGM-NET Convolutional Neural Network

Technologies ◽

10.3390/technologies9040098 ◽

2021 ◽

Vol 9 (4) ◽

pp. 98

Author(s):

Gabriel Ackall ◽

Mohammed Elmzoudi ◽

Richard Yuan ◽

Cuixian Chen

Keyword(s):

Neural Network ◽

High School Students ◽

Convolutional Neural Network ◽

Rural Areas ◽

False Negative ◽

False Negative Rate ◽

Mapping Technique ◽

X Rays ◽

School Students ◽

X Ray

COVID-19 has spread rapidly across the world since late 2019. As of December, 2021, there are over 250 million documented COVID-19 cases and over 5 million deaths worldwide, which have caused businesses, schools, and government operations to shut down. The most common method of detecting COVID-19 is the RT-PCR swab test, which suffers from a high false-negative rate and a very slow turnaround for results, often up to two weeks. Because of this, specialists often manually review X-ray images of the lungs to detect the presence of COVID-19 with up to 97% accuracy. Neural network algorithms greatly accelerate this review process, analyzing hundreds of X-rays in seconds. Using the Cohen COVID-19 X-ray Database and the NIH ChestX-ray8 Database, we trained and constructed the xRGM-NET convolutional neural network (CNN) to detect COVID-19 in X-ray scans of the lungs. To further aid medical professionals in the manual review of X-rays, we implemented the CNN activation mapping technique Score-CAM, which generates a heat map over an X-ray to illustrate which areas in the scan are most influential over the ultimate diagnosis. xRGM-NET achieved an overall classification accuracy of 97% with a sensitivity of 94% and specificity of 97%. Lightweight models like xRGM-NET can serve to improve the efficiency and accuracy of COVID-19 detection in developing countries or rural areas. In this paper, we report on our model and methods that were developed as part of a STEM enrichment summer program for high school students. We hope that our model and methods will allow other researchers to create lightweight and accurate models as more COVID-19 X-ray scans become available.

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A comparative study between abdominal plain radiography and ultrasonography in non-traumatic acute abdominal emergencies in tertiary care hospital

International Journal of Research in Medical Sciences ◽

10.18203/2320-6012.ijrms20164533 ◽

2016 ◽

Vol 5 (1) ◽

pp. 113 ◽

Cited By ~ 1

Author(s):

Chiranjeev Kumar Gathwal ◽

Monika B. Gathwal ◽

Shreya Garg ◽

Yogita Kumari ◽

Kulvinder Singh

Keyword(s):

Comparative Study ◽

Acute Abdomen ◽

Tertiary Care ◽

False Negative ◽

False Negative Rate ◽

Plain Radiography ◽

Radiographic Evaluation ◽

Care Hospital ◽

X Rays ◽

Abdominal Emergencies

Background: Acute abdomen is a loose term frequently used to describe the acute abdominal pain in a subgroup of patients who are seriously ill developing suddenly, over a period of several hours or few days.Methods: It was a prospective comparative study between abdominal plain radiography and ultrasonography in non-traumatic acute abdominal emergencies in Tertiary Care Hospital.Results: All the included patients (140) were imaged with abdominal X-rays series (AAS) and Ultrasonography (US) by different blinded radiologists without conveying results to either. Final diagnosis was made on the basis of clinical findings / laboratory or biochemical findings /radiological evaluation /therapeutic response / operative findings / histopathological examination. The entire data was collected, recorded and statistically analyzed as per objectives. GIT system was most commonly involved, in 75/140 cases (53.57%). Most common diagnoses were acute appendicitis, KUB calculus disease and acute cholecystitis seen in 32/140 (22.86%), 24/140 (17.14%) and 21 (15%) cases respectively. US supersedes Provisional clinical diagnosis and Radiographic evaluation in diagnosing acute abdominal conditions with Sensitivity, Positive Predictive Value, False positive rate, False Negative rate and Diagnostic Accuracy as 90.71, 100, 0 ,9.28 and 90.71 percent respectively.Conclusions: We concluded that Plain X rays can be used as screening modality in the diagnosis of acute abdominal emergencies; however ultrasound examination is cheaper, non-invasive, quick, reliable and highly accurate modality in diagnosing the exact cause of pain and its origin in a patient presenting with an acute abdomen and thus helps the physician or surgeon to plan the timely management.

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Finding COVID-19 from Chest X-rays using Deep Learning on a Small Dataset

10.36227/techrxiv.12083964.v2 ◽

2020 ◽

Cited By ~ 2

Author(s):

Lawrence Hall ◽

Dmitry Goldgof ◽

Rahul Paul ◽

Gregory M. Goldgof

Keyword(s):

Deep Learning ◽

Bacterial Pneumonia ◽

False Negative ◽

False Negative Rate ◽

X Rays ◽

Test Results ◽

X Ray ◽

Chest X Ray ◽

Small Dataset ◽

Children Under 5

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Finding COVID-19 from Chest X-rays using Deep Learning on a Small Dataset

10.36227/techrxiv.12083964.v1 ◽

2020 ◽

Cited By ~ 2

Author(s):

Lawrence Hall ◽

Dmitry Goldgof ◽

Rahul Paul ◽

Gregory M. Goldgof

Keyword(s):

False Negative ◽

False Negative Rate ◽

Disease Process ◽

Small Data ◽

X Rays ◽

Data Set ◽

X Ray ◽

Set Size ◽

Lack Of Information ◽

Chest X Ray

<p>Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We have obtained 122 chest X-rays of COVID-19 and over 4,000 chest X-rays of viral and bacterial pneumonia. A pre-trained deep convolutional neural network has been tuned on 102 COVID-19 cases and 102 other pneumonia cases in a 10-fold cross validation. The results were all 102 COVID-19 cases were correctly classified and there were 8 false positives resulting in an AUC of 0.997. On a test set of 20 unseen COVID-19 cases all were correctly classified and more than 95% of 4,171 other pneumonia examples were correctly classified. This study has flaws, most critically a lack of information about where in the disease process the COVID-19 cases were and the small data set size. More COVID-19 case images will enable a better answer to the question of how useful chest X-rays can be for diagnosing COVID-19 (so please send them). </p>

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STUDY OF INFLAMMATORY MARKERS IN RELATION TO RADIOLOGICAL FINDINGS IN COVID 19

10.36106/ijsr/9604567 ◽

2021 ◽

pp. 48-50

Author(s):

Kalyanisri. Koneru ◽

V M Kiran Ogirala ◽

Kommavarapu. Kalyani Madhuri ◽

Bokam. Bhanu Rekha

Keyword(s):

Hospital Stay ◽

Tertiary Care ◽

P Value ◽

Care Hospital ◽

X Rays ◽

X Ray ◽

Duration Of Hospital Stay ◽

Number Of Patients ◽

Chest X Ray ◽

Severity Of The Disease

BACKGROUND Currently, the Coronavirus disease 2019 (COVID-19) has become pandemic globally. Elevated inammatory markers are observed and are a common pathophysiological response to acute illness. Chest X-ray changes are also commonly seen in COVID -19 patients. The present study was undertaken to determine the relationship between inammatory markers to chest X-ray ndings in COVID-19 patients. METHODS This is a prospective observational study of COVID-19 patients admitted to tertiary care hospital from may 2020-November 2020. Comorbidities, inammatory markers, and Chest X ray were collected and analyzed. Correlations between radiological and inammatory markers were studied. AIMS & OBJECTIVES: Ÿ Correlation of inammatory markers to radiographic ndings and their outcome in COVID 19 patients Ÿ The outcome was studied in terms of: Ÿ Patients requiring oxygen/ NIVsupport Ÿ duration of hospital stay Ÿ Number of patients Recovered/death RESULTS: Ÿ Out of 500 patients studied, the mean age was 49.41 years, and (295)59% of patients were male,(205)41% were females. (455)91% patients discharged and (43)8.6% died. We found a positive correlation between inammatory markers and Chest X-ray ndings at the time of admission with a signicant statistical P-value. The inammatory markers CRP, ESR, D-Dimer & Sr.ferritin compared with the mode of ventilation(O2 & NIV, duration of hospital stay and outcome also showed signicant statistical P-value. CONCLUSIONS We conclude that in patients with raised inammatory markers there were increased abnormalities on Chest X-rays which required an increase in oxygen or NIVsupport. This can be a useful predictor of the severity of the disease and assessment of outcome.

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Digital x-ray mapping of nanometer-size supported bimetallic catalysts

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100104716 ◽

1988 ◽

Vol 46 ◽

pp. 530-531

Author(s):

L. T. Germinario

Keyword(s):

Background Radiation ◽

Metal Cluster ◽

Single Element ◽

Beam Diameter ◽

X Rays ◽

X Ray ◽

Major Interest ◽

Induced Changes

Understanding the role of metal cluster composition in determining catalytic selectivity and activity is of major interest in heterogeneous catalysis. The electron microscope is well established as a powerful tool for ultrastructural and compositional characterization of support and catalyst. Because the spatial resolution of x-ray microanalysis is defined by the smallest beam diameter into which the required number of electrons can be focused, the dedicated STEM with FEG is the instrument of choice. The main sources of errors in energy dispersive x-ray analysis (EDS) are: (1) beam-induced changes in specimen composition, (2) specimen drift, (3) instrumental factors which produce background radiation, and (4) basic statistical limitations which result in the detection of a finite number of x-ray photons. Digital beam techniques have been described for supported single-element metal clusters with spatial resolutions of about 10 nm. However, the detection of spurious characteristic x-rays away from catalyst particles produced images requiring several image processing steps.

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External validation for sensitivity of the Ottawa subarachnoid hemorrhage rule in a Japanese tertiary teaching hospital

Scientific Reports ◽

10.1038/s41598-021-96320-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tomoharu Suzuki ◽

David Itokazu ◽

Yasuharu Tokuda

Keyword(s):

Subarachnoid Hemorrhage ◽

Teaching Hospital ◽

Tertiary Care ◽

External Validation ◽

False Negative ◽

False Negative Rate ◽

Prediction Rule ◽

Japanese Patients ◽

Inclusion Criteria ◽

Tertiary Teaching

AbstractThe Ottawa subarachnoid hemorrhage (OSAH) rule is a validated clinical prediction rule for ruling out subarachnoid hemorrhage (SAH). Another SAH rule (Ottawa-like rule) was developed in Japan but was not well validated. We aimed to validate both rules by examining the sensitivity for ruling out SAH in Japanese patients diagnosed with SAH. We conducted a retrospective cohort study by reviewing the medical records of consecutive adult patients hospitalized with SAH at a tertiary-care teaching hospital in Japan who visited our emergency department between July 2009 and June 2019. Sensitivity and its 95% confidence interval (CI) were estimated for each rule for the diagnosis of SAH. In a total of 280 patients with SAH, 56 (20.0%) patients met the inclusion criteria and were analyzed for the OSAH rule, and a sensitivity of the OSAH rule was 56/56 (100%; 95% CI 93.6–100%). While, 126 (45%) patients met the inclusion criteria of the Ottawa-like rule, and the rule showed a sensitivity of 125/126 (99.2%; 95%CI 95.7–100%). The OSAH rule showed 100% sensitivity among our Japanese patients diagnosed with SAH. The implementation of the Ottawa-like rule should be cautious because the false-negative rate is up to 4%.

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Assessment of Lymph Node Involvement with PET-CT in Advanced Epithelial Ovarian Cancer. A FRANCOGYN Group Study

Journal of Clinical Medicine ◽

10.3390/jcm10040602 ◽

2021 ◽

Vol 10 (4) ◽

pp. 602

Author(s):

Antoine Tardieu ◽

Lobna Ouldamer ◽

François Margueritte ◽

Lauranne Rossard ◽

Aymeline Lacorre ◽

...

Keyword(s):

Lymph Node ◽

False Negative ◽

False Negative Rate ◽

Lymph Node Involvement ◽

Accuracy Rate ◽

Peritoneal Cancer ◽

Negative Rate ◽

Number Of Patients ◽

Pet Ct ◽

Node Involvement

The objective of our study is to evaluate the diagnostic performance of positron emission tomography/computed tomography (PET-CT) for the assessment of lymph node involvement in advanced epithelial ovarian, fallopian tubal or peritoneal cancer (EOC). This was a retrospective, bicentric study. We included all patients over 18 years of age with a histological diagnosis of advanced EOC who had undergone PET-CT at the time of diagnosis or prior to cytoreduction surgery with pelvic or para-aortic lymphadenectomy. We included 145 patients with primary advanced EOC. The performance of PET-CT was calculated from the data of 63 patients. The sensitivity of PET-CT for preoperative lymph node evaluation was 26.7%, specificity was 90.9%, PPV was 72.7%, and NPV was 57.7%. The accuracy rate was 60.3%, and the false-negative rate was 34.9%. In the case of primary cytoreduction (n = 16), the sensitivity of PET-CT was 50%, specificity was 87.5%, PPV was 80%, and NPV was 63.6%. The accuracy rate was 68.8%, and the false negative rate was 25%. After neoadjuvant chemotherapy (n = 47), the sensitivity of PET-CT was 18.2%, specificity was 92%, PPV was 66.7%, and NPV was 56.1%. The accuracy rate was 57.5%, and the false negative rate was 38.3%. Due to its high specificity, the performance of a preoperative PET-CT scan could contribute to the de-escalation and reduction of lymphadenectomy in the surgical management of advanced EOC in a significant number of patients free of lymph node metastases.

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