scholarly journals Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 530
Author(s):  
Christian Salvatore ◽  
Matteo Interlenghi ◽  
Caterina B. Monti ◽  
Davide Ippolito ◽  
Davide Capra ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Area Under The Curve ◽  
Community Acquired Pneumonia ◽  
Validation Dataset ◽  
X Rays ◽  
Different Types ◽  
Chest X Ray ◽  
Sensitivity Specificity

We assessed the role of artificial intelligence applied to chest X-rays (CXRs) in supporting the diagnosis of COVID-19. We trained and cross-validated a model with an ensemble of 10 convolutional neural networks with CXRs of 98 COVID-19 patients, 88 community-acquired pneumonia (CAP) patients, and 98 subjects without either COVID-19 or CAP, collected in two Italian hospitals. The system was tested on two independent cohorts, namely, 148 patients (COVID-19, CAP, or negative) collected by one of the two hospitals (independent testing I) and 820 COVID-19 patients collected by a multicenter study (independent testing II). On the training and cross-validation dataset, sensitivity, specificity, and area under the curve (AUC) were 0.91, 0.87, and 0.93 for COVID-19 versus negative subjects, 0.85, 0.82, and 0.94 for COVID-19 versus CAP. On the independent testing I, sensitivity, specificity, and AUC were 0.98, 0.88, and 0.98 for COVID-19 versus negative subjects, 0.97, 0.96, and 0.98 for COVID-19 versus CAP. On the independent testing II, the system correctly diagnosed 652 COVID-19 patients versus negative subjects (0.80 sensitivity) and correctly differentiated 674 COVID-19 versus CAP patients (0.82 sensitivity). This system appears promising for the diagnosis and differential diagnosis of COVID-19, showing its potential as a second opinion tool in conditions of the variable prevalence of different types of infectious pneumonia.

scholarly journals Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID-19 infection: a first experience from Lombardy, Italy

2020 ◽  
Author(s):  
Isabella Castiglioni ◽  
Davide Ippolito ◽  
Matteo Interlenghi ◽  
Caterina Beatrice Monti ◽  
Christian Salvatore ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Area Under The Curve ◽  
Training Dataset ◽  
List Type ◽  
X Rays ◽  
Independent Dataset ◽  
X Ray ◽  
Chest X Ray

AbstractObjectivesWe tested artificial intelligence (AI) to support the diagnosis of COVID-19 using chest X-ray (CXR). Diagnostic performance was computed for a system trained on CXRs of Italian subjects from two hospitals in Lombardy, Italy.MethodsWe used for training and internal testing an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals. We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as reference standard.ResultsAt 10-fold cross-validation, our AI model classified COVID-19 and non COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74–0.81), 0.82 specificity (95% CI 0.78–0.85) and 0.89 area under the curve (AUC) (95% CI 0.86–0.91). For the independent dataset, AI showed 0.80 sensitivity (95% CI 0.72–0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73–0.87), and 0.81 AUC (95% CI 0.73– 0.87). Radiologists’ reading obtained 0.63 sensitivity (95% CI 0.52–0.74) and 0.78 specificity (95% CI 0.61–0.90) in one centre and 0.64 sensitivity (95% CI 0.52–0.74) and 0.86 specificity (95% CI 0.71–0.95) in the other.ConclusionsThis preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of AI for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.Key pointsArtificial intelligence based on convolutional neural networks was preliminary applied to chest-X-rays of patients suspected to be infected by COVID-19.Convolutional neural networks trained on a limited dataset of 250 COVID-19 and 250 non-COVID-19 were tested on an independent dataset of 110 patients suspected for COVID-19 infection and provided a balanced performance with 0.80 sensitivity and 0.81 specificity.Training on larger multi-institutional datasets may allow this tool to increase its performance.

S-CCCapsule: Pneumonia detection in chest X-ray images using skip-connected convolutions and capsule neural network

2021 ◽  
pp. 1-25
Author(s):  
Kwabena Adu ◽  
Yongbin Yu ◽  
Jingye Cai ◽  
Victor Dela Tattrah ◽  
James Adu Ansere ◽  
...  
Keyword(s):  
Neural Network ◽  
Human Error ◽  
Medical Center ◽  
Confusion Matrix ◽  
Area Under The Curve ◽  
Dynamic Routing ◽  
Sigmoid Function ◽  
Radiological Society ◽  
Chest X Ray ◽  
Sensitivity Specificity

The squash function in capsule networks (CapsNets) dynamic routing is less capable of performing discrimination of non-informative capsules which leads to abnormal activation value distribution of capsules. In this paper, we propose vertical squash (VSquash) to improve the original squash by preventing the activation values of capsules in the primary capsule layer to shrink non-informative capsules, promote discriminative capsules and avoid high information sensitivity. Furthermore, a new neural network, (i) skip-connected convolutional capsule (S-CCCapsule), (ii) Integrated skip-connected convolutional capsules (ISCC) and (iii) Ensemble skip-connected convolutional capsules (ESCC) based on CapsNets are presented where the VSquash is applied in the dynamic routing. In order to achieve uniform distribution of coupling coefficient of probabilities between capsules, we use the Sigmoid function rather than Softmax function. Experiments on Guangzhou Women and Children’s Medical Center (GWCMC), Radiological Society of North America (RSNA) and Mendeley CXR Pneumonia datasets were performed to validate the effectiveness of our proposed methods. We found that our proposed methods produce better accuracy compared to other methods based on model evaluation metrics such as confusion matrix, sensitivity, specificity and Area under the curve (AUC). Our method for pneumonia detection performs better than practicing radiologists. It minimizes human error and reduces diagnosis time.

scholarly journals The Added Effect of Artificial Intelligence on Physicians’ Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2206
Author(s):  
Dana Li ◽  
Lea Marie Pehrson ◽  
Carsten Ammitzbøl Lauridsen ◽  
Lea Tøttrup ◽  
Marco Fraccaro ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Assessment Tool ◽  
Cochrane Library ◽  
Original Research ◽  
Quality Assessment Tool ◽  
Bias Assessment ◽  
X Ray ◽  
Chest X Ray ◽  
Sensitivity Specificity

Our systematic review investigated the additional effect of artificial intelligence-based devices on human observers when diagnosing and/or detecting thoracic pathologies using different diagnostic imaging modalities, such as chest X-ray and CT. Peer-reviewed, original research articles from EMBASE, PubMed, Cochrane library, SCOPUS, and Web of Science were retrieved. Included articles were published within the last 20 years and used a device based on artificial intelligence (AI) technology to detect or diagnose pulmonary findings. The AI-based device had to be used in an observer test where the performance of human observers with and without addition of the device was measured as sensitivity, specificity, accuracy, AUC, or time spent on image reading. A total of 38 studies were included for final assessment. The quality assessment tool for diagnostic accuracy studies (QUADAS-2) was used for bias assessment. The average sensitivity increased from 67.8% to 74.6%; specificity from 82.2% to 85.4%; accuracy from 75.4% to 81.7%; and Area Under the ROC Curve (AUC) from 0.75 to 0.80. Generally, a faster reading time was reported when radiologists were aided by AI-based devices. Our systematic review showed that performance generally improved for the physicians when assisted by AI-based devices compared to unaided interpretation.

Role of diagnostic imaging in diagnosis by Covid-19

2020 ◽  
Author(s):  
Coda Marco ◽  
Sica Federica ◽  
Finelli Mirko ◽  
Ungaro Gaetano ◽  
Sica Alfonso Marco
Keyword(s):  
Differential Diagnosis ◽  
Lung Parenchyma ◽  
Chest Ct ◽  
Initial Assessment ◽  
X Rays ◽  
Radiological Imaging ◽  
Laboratory Examinations ◽  
Clinical Conditions

The diagnosis from Covid-19 provides the set of several examinations such as: clinical examinations, laboratory examinations, radiographic examinations. Using radiological imaging, RX and chest CT, it is possible to evaluate the impairment of lung function and thanks to this aspect it is possible to define the severity and clinical conditions of the patient. In this way, it allows timely therapeutic intervention especially if the patient shows a mild condition in such a way as to avoid the onset of further complications. Chest X-rays allow both an initial assessment of patients and the possibility to perform a differential diagnosis towards other possible causes of lung parenchyma involvement. The CT scan, which highlights the peculiar characteristics of COVID pneumonia, is performed both as diagnostic confirmation and in the patient’s follow-up.

scholarly journals 1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S424-S425
Author(s):  
Dan Ding ◽  
Anna Stachel ◽  
Eduardo Iturrate ◽  
Michael Phillips
Keyword(s):  
Language Processing ◽  
Cost Effective ◽  
The United States ◽  
Surveillance Data ◽  
X Rays ◽  
Healthcare Facilities ◽  
X Ray ◽  
Chest X Ray ◽  
Sensitivity Specificity ◽  
Time Critical

Abstract Background Pneumonia (PNU) is the second most common nosocomial infection in the United States and is associated with substantial morbidity and mortality. While definitions from CDC were developed to increase the reliability of surveillance data, reduce the burden of surveillance in healthcare facilities, and enhance the utility of surveillance data for improving patient safety - the algorithm is still laborious. We propose an implementation of a refined algorithm script which combines two CDC definitions with the use of natural language processing (NLP), a tool which relies on pattern matching to determine whether a condition of interest is reported as present or absent in a report, to automate PNU surveillance. Methods Using SAS v9.4 to write a query, we used a combination of National Healthcare Safety Network’s (NHSN) PNU and ventilator-associated event (VAE) definitions that use discrete fields found in electronic medical records (EMR) and trained an NLP tool to determine whether chest x-ray report was indicative of PNU (Fig1). To validate, we assessed sensitivity/specificity of NLP tool results compared with clinicians’ interpretations. Results The NLP tool was highly accurate in classifying the presence of PNU in chest x-rays. After training the NLP tool, there were only 4% discrepancies between NLP tool and clinicians interpretations of 223 x-ray reports - sensitivity 92.2% (81.1–97.8), specificity 97.1% (93.4–99.1), PPV 90.4% (79.0–96.8), NPV 97.7% (94.1–99.4). Combining the automated use of discrete EMR fields with NLP tool significantly reduces the time spent manually reviewing EMRs. A manual review for PNU without automation requires approximately 10 minutes each day per admission. With a monthly average of 2,350 adult admissions at our hospital and 16,170 patient-days for admissions with at least 2 days, the algorithm saves approximately 2,695 review hours. Conclusion The use of discrete EMR fields with an NLP tool proves to be a timelier, cost-effective yet accurate alternative to manual PNU surveillance review. By allowing an automated algorithm to review PNU, timely reports can be sent to units about individual cases. Compared with traditional CDC surveillance definitions, an automated tool allows real-time critical review for infection and prevention activities. Disclosures All authors: No reported disclosures.

scholarly journals Development and evaluation of an artificial intelligence system for COVID-19 diagnosis

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Cheng Jin ◽  
Weixiang Chen ◽  
Yukun Cao ◽  
Zhanwei Xu ◽  
Zimeng Tan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Influenza A ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Community Acquired Pneumonia ◽  
Intelligence System ◽  
Challenging Tasks ◽  
Chest X Ray ◽  
Detailed Interpretation ◽  
Timely Treatment

Abstract Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19.

Cytomorphometric Neutrophil and Monocyte Markers May Strengthen the Diagnosis of Sepsis

2016 ◽  
Vol 33 (12) ◽  
pp. 656-662
Author(s):  
Joy Mammen ◽  
Jui Choudhuri ◽  
Joshua Paul ◽  
Thomas Isaiah Sudarsan ◽  
T. Josephine ◽  
...  
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Blood Donors ◽  
Area Under The Curve ◽  
Standard Test ◽  
Suspected Sepsis ◽  
Predictive Values ◽  
Reactive Protein ◽  
Sensitivity Specificity

Background: The diagnosis of sepsis is challenging in the absence of a gold standard test. Recent studies have explored the role of neutrophil and monocyte volume, conductivity, and scatter (VCS), derived from automated hematology analyzers, in diagnosing sepsis. We assessed the diagnostic accuracy of VCS parameters in critically ill patients with sepsis. Methodology: In this prospective study, VCS parameters, procalcitonin, and C-reactive protein (CRP) were assessed in patients with proven sepsis (cases) and 2 control groups (intensive care unit [ICU] patients without sepsis and healthy blood donors). The diagnostic property of each test was explored by calculating sensitivity, specificity, negative and positive predictive values, and area under the curve (AUC). Results: The study included 65 patients with sepsis, 58 nonseptic ICU controls, and 98 blood donors. Procalcitonin and CRP were not significantly different ( P > .06) between patients with sepsis and nonseptic patients. Mean (95% confidence interval [CI]) neutrophil volume (MNV) was significantly higher ( P < .001) in patients with sepsis (165.5; 95%CI 161.6-169.4) than in nonseptic (157.3; 95%CI 154.6-160.1) patients and donors (148.9; 95%CI 147.9-150). A similar pattern was seen with mean monocyte volume (MMoV). Neutrophil and monocyte conductivity and scatter parameters were variably associated. The AUC was highest for MMoV (0.74) and lowest for CRP (0.62). Among all parameters, MNV and MMoV had the highest specificity of 85% and 80%, respectively. Conclusion: In critically ill patients with suspected sepsis, VCS parameters may help strengthen the diagnostic probability of sepsis. Future studies may explore the role of serial monitoring of VCS to track response to antimicrobial therapy.

scholarly journals Study of risk factors among stroke patients in a tertiary hospital of Northern India

2017 ◽  
Vol 4 (2) ◽  
pp. 446
Author(s):  
Amit Kumar ◽  
Onkar Nath Rai
Keyword(s):  
Risk Factors ◽  
Tertiary Hospital ◽  
Stroke Incidence ◽  
Medical College ◽  
Northern India ◽  
Different Types ◽  
History Of ◽  
Chest X Ray ◽  
Associated Risk Factors

Background: Stroke is one of the leading causes of death and disability worldwide. The aim of the study was to find out the incidence of different types of strokes and the associated risk factors and to establish the role of different investigations in patients of stroke.Methods: The study dealt with 100 patients of stroke who were admitted to B. R. D. Medical College, Gorakhpur, India. Each patient was analyzed in detail about clinical presentation and the investigations were aimed to establish the pathologic type of stroke and estimation of risk factors.Results: Stroke incidence was more in males (Male: Female= 1.43:1). Maximum incidence of stroke was in 6th decade (32%) followed by 7th decade (30%). Among modifiable risk factors, history of hypertension was the commonest (51%) followed by smoking (36% patients) exclusively, found in males. Hemiparesis was the most common presentation (95%) followed by altered sensorium (55%). Chest X-ray was abnormal in 16% patients, abnormal ECG was found in 27% patients and abnormal lipid values were found in 54 patients.Conclusions: Apart from control of hypertension and diabetes, abnormal lipid profile remains an important modifiable risk factor for stroke.

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