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

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.

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Public Covid-19 X-ray datasets and their impact on model bias - a systematic review of a significant problem

10.1101/2021.02.15.21251775 ◽

2021 ◽

Author(s):

Beatriz Garcia Santa Cruz ◽

Matías Nicolás Bossa ◽

Jan Sölter ◽

Andreas Dominik Husch

Keyword(s):

Systematic Review ◽

Prediction Models ◽

Risk Of Bias ◽

Systematic Evaluation ◽

Bias Assessment ◽

X Ray ◽

Inappropriate Use ◽

Chest X Ray ◽

Almost All ◽

Aided Diagnosis

ABSTRACTComputer-aided-diagnosis for COVID-19 based on chest X-ray suffers from weak bias assessment and limited quality-control. Undetected bias induced by inappropriate use of datasets, and improper consideration of confounders prevents the translation of prediction models into clinical practice. This study provides a systematic evaluation of publicly available COVID-19 chest X-ray datasets, determining their potential use and evaluating potential sources of bias.Only 5 out of 256 identified datasets met at least the criteria for proper assessment of risk of bias and could be analysed in detail. Remarkably almost all of the datasets utilised in 78 papers published in peer-reviewed journals, are not among these 5 datasets, thus leading to models with high risk of bias. This raises concerns about the suitability of such models for clinical use.This systematic review highlights the limited description of datasets employed for modelling and aids researchers to select the most suitable datasets for their task.

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Sensitivity of chest X-ray for detecting lung cancer in people presenting with symptoms: a systematic review

British Journal of General Practice ◽

10.3399/bjgp19x706853 ◽

2019 ◽

Vol 69 (689) ◽

pp. e827-e835 ◽

Cited By ~ 3

Author(s):

Stephen H Bradley ◽

Sarah Abraham ◽

Matthew EJ Callister ◽

Adam Grice ◽

William T Hamilton ◽

...

Keyword(s):

Lung Cancer ◽

Systematic Review ◽

Primary Care ◽

High Risk ◽

Grey Literature ◽

Primary Objective ◽

Risk Of Bias ◽

Cochrane Library ◽

X Ray ◽

Chest X Ray

BackgroundDespite increasing use of computed tomography (CT), chest X-ray remains the first-line investigation for suspected lung cancer in primary care in the UK. No systematic review evidence exists as to the sensitivity of chest X-ray for detecting lung cancer in people presenting with symptoms.AimTo estimate the sensitivity of chest X-ray for detecting lung cancer in symptomatic people.Design and settingA systematic review was conducted to determine the sensitivity of chest X-ray for the detection of lung cancer.MethodDatabases including MEDLINE, EMBASE, and the Cochrane Library were searched; a grey literature search was also performed.ResultsA total of 21 studies met the eligibility criteria. Almost all were of poor quality. Only one study had the diagnostic accuracy of chest X-ray as its primary objective. Most articles were case studies with a high risk of bias. Several were drawn from non-representative groups, for example, specific presentations, histological subtypes, or comorbidities. Only three studies had a low risk of bias. Two primary care studies reported sensitivities of 76.8% (95% confidence interval [CI] = 64.5 to 84.2%) and 79.3% (95% CI = 67.6 to 91.0%). One secondary care study reported a sensitivity of 79.7% (95% CI = 72.7 to 86.8%).ConclusionThough there is a paucity of evidence, the highest-quality studies suggest that the sensitivity of chest X-ray for symptomatic lung cancer is only 77% to 80%. GPs should consider if further investigation is necessary in high-risk patients who have had a negative chest X-ray.

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A Systematic Review on the Use of Artificial Intelligence Techniques in the Diagnosis of COVID-19 from Chest X-Ray Images

Avicenna Journal of Medical Biochemistry ◽

10.34172/ajmb.2020.17 ◽

2020 ◽

Vol 8 (2) ◽

pp. 120-127

Author(s):

Mohammad Hosein Sadeghi ◽

Hamid Omidi ◽

Sedigheh Sina

Keyword(s):

Artificial Intelligence ◽

Systematic Review ◽

Sensitivity And Specificity ◽

Model Performance ◽

Area Under The Curve ◽

Google Scholar ◽

X Ray ◽

Source Codes ◽

Specificity And Sensitivity ◽

Chest X Ray

Background: In this study, the artificial intelligence (AI) techniques used for the detection of coronavirus disease 2019 (COVID-19) from the chest x-ray were reviewed. Methods: PubMed, arXiv, and Google Scholar were used to search for AI studies. Results: A total of 20 papers were extracted from Google Scholar, 14 from arXiv, and 5 from PubMed. In 17 papers, publicly available datasets and in 3 papers, independent datasets were used. 10 papers disclosed source codes. Nine papers were about creating a novel AI software, 8 papers reported the modification of the existing AI models, and 3 compared the performance of the existing AI software programs. All papers have used deep learning as AI technique. Most papers reported accuracy, specificity, and sensitivity of the models, and also the area under the curve (AUC) for investigation of the model performance for the prediction of COVID-19. Nine papers reported accuracy, sensitivity, and specificity. The number of datasets used in the studies ranged from 50 to 94323. The accuracy, sensitivity, and specificity of the models ranged from 0.88 to 0.98, 0.80 to 1.00, and 0.70 to 1.00, respectively. Conclusion: The studies revealed that AI can help human in fighting the new Coronavirus.

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Artificial Intelligence for COVID-19: A Systematic Review

Frontiers in Medicine ◽

10.3389/fmed.2021.704256 ◽

2021 ◽

Vol 8 ◽

Author(s):

Lian Wang ◽

Yonggang Zhang ◽

Dongguang Wang ◽

Xiang Tong ◽

Tao Liu ◽

...

Keyword(s):

Artificial Intelligence ◽

Systematic Review ◽

High Performance ◽

Assessment Tool ◽

Clinical Laboratory ◽

Area Under The Curve ◽

Drug Repurposing ◽

Development Trend ◽

Cochrane Library ◽

Respiratory Syndrome Virus

Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines; and discusses the potential limitations.Methods: We report this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase and the Cochrane Library from inception to 19 September 2020 for published studies of AI applications in COVID-19. We used PROBAST (prediction model risk of bias assessment tool) to assess the quality of literature related to the diagnosis and prognosis of COVID-19. We registered the protocol (PROSPERO CRD42020211555).Results: We included 78 studies: 46 articles discussed AI-assisted diagnosis for COVID-19 with total accuracy of 70.00 to 99.92%, sensitivity of 73.00 to 100.00%, specificity of 25 to 100.00%, and area under the curve of 0.732 to 1.000. Fourteen articles evaluated prognosis based on clinical characteristics at hospital admission, such as clinical, laboratory and radiological characteristics, reaching accuracy of 74.4 to 95.20%, sensitivity of 72.8 to 98.00%, specificity of 55 to 96.87% and AUC of 0.66 to 0.997 in predicting critical COVID-19. Nine articles used AI models to predict the epidemic of the COVID-19, such as epidemic peak, infection rate, number of infected cases, transmission laws, and development trend. Eight articles used AI to explore potential effective drugs, primarily through drug repurposing and drug development. Finally, 1 article predicted vaccine targets that have the potential to develop COVID-19 vaccines.Conclusions: In this review, we have shown that AI achieved high performance in diagnosis, prognosis evaluation, epidemic prediction and drug discovery for COVID-19. AI has the potential to enhance significantly existing medical and healthcare system efficiency during the COVID-19 pandemic.

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Biomarkers predictive of treatment response in psoriasis and psoriatic arthritis: a systematic review

Therapeutic Advances in Musculoskeletal Disease ◽

10.1177/1759720x211014010 ◽

2021 ◽

Vol 13 ◽

pp. 1759720X2110140

Author(s):

Conor Magee ◽

Hannah Jethwa ◽

Oliver M. FitzGerald ◽

Deepak R. Jadon

Keyword(s):

Systematic Review ◽

Psoriatic Arthritis ◽

Treatment Response ◽

Unmet Need ◽

Response To Treatment ◽

Cochrane Library ◽

Original Research ◽

Eligibility Criteria ◽

Psoriatic Disease ◽

Subsequent Response

Aims: The ability to predict response to treatment remains a key unmet need in psoriatic disease. We conducted a systematic review of studies relating to biomarkers associated with response to treatment in either psoriasis vulgaris (PsV) or psoriatic arthritis (PsA). Methods: A search was conducted in PubMed, Embase and the Cochrane library from their inception to 2 September 2020, and conference proceedings from four major rheumatology conferences. Original research articles studying pre-treatment biomarker levels associated with subsequent response to pharmacologic treatment in either PsV or PsA were included. Results: A total of 765 articles were retrieved and after review, 44 articles (22 relating to PsV and 22 to PsA) met the systematic review’s eligibility criteria. One study examined the response to methotrexate, one the response to tofacitinib and all the other studies to biologic disease-modifying antirheumatic drugs (DMARDs). Whilst several studies examined the HLA-C*06 allele in PsV, the results were conflicting. Interleukin (IL)-12 serum levels and polymorphisms in the IL-12B gene show promise as biomarkers of treatment response in PsV. Most, but not all, studies found that higher baseline levels of C-reactive protein (CRP) were associated with a better clinical response to treatment in patients with PsA. Conclusion: Several studies have identified biomarkers associated with subsequent response to treatment in psoriatic disease. However, due to the different types of biomarkers, treatments and outcome measures used, firm conclusions cannot be drawn. Further validation is needed before any of these biomarkers translate to clinical practice.

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Training Artificial Intelligence Neural Networks to Detect Devices on a Chest X-ray, a Pathway to COVID-19 Testing

Journal of the National Medical Association ◽

10.1016/j.jnma.2020.09.140 ◽

2020 ◽

Vol 112 (5) ◽

pp. S50

Author(s):

Zachary Eller ◽

Michelle Chen ◽

Jermaine Heath ◽

Uzma Hussain ◽

Thomas Obisean ◽

...

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

X Ray ◽

Chest X Ray

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Effects of low back pain on balance performance in elderly people: a systematic review and meta-analysis

European Review of Aging and Physical Activity ◽

10.1186/s11556-021-00263-z ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Le Ge ◽

Chuhuai Wang ◽

Haohan Zhou ◽

Qiuhua Yu ◽

Xin Li

Keyword(s):

Systematic Review ◽

Low Back Pain ◽

Back Pain ◽

Elderly People ◽

Meta Analysis ◽

Cochrane Library ◽

Original Research ◽

Low Back ◽

Centre Of Pressure ◽

Balance Performance

Abstract Background Research suggests that individuals with low back pain (LBP) may have poorer motor control compared to their healthy counterparts. However, the sample population of almost 90% of related articles are young and middle-aged people. There is still a lack of a systematic review about the balance performance of elderly people with low back pain. This study aimed to conduct a systematic review and meta-analysis to understand the effects of LBP on balance performance in elderly people. Methods This systematic review and meta-analysis included a comprehensive search of PubMed, Embase, and Cochrane Library databases for full-text articles published before January 2020. We included the articles that 1) investigated the elderly people with LBP; 2) assessed balance performance with any quantifiable clinical assessment or measurement tool and during static or dynamic activity; 3) were original research. Two independent reviewers screened the relevant articles, and disagreements were resolved by a third reviewer. Results Thirteen case-control studies comparing balance performance parameters between LBP and healthy subjects were included. The experimental group (LBP group) was associated with significantly larger area of centre of pressure movement (P < 0.001), higher velocity of centre of pressure sway in the anteroposterior and mediolateral directions (P = 0.01 and P = 0.02, respectively), longer path length in the anteroposterior direction (P < 0.001), slower walking speed (P = 0.05), and longer timed up and go test time (P = 0.004) than the control group. Conclusion The results showed that balance performance was impaired in elderly people with LBP. We should pay more attention to the balance control of elderly people with LBP.

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Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging

Health and Technology ◽

10.1007/s12553-021-00520-2 ◽

2021 ◽

Vol 11 (2) ◽

pp. 411-424 ◽

Cited By ~ 1

Author(s):

José Daniel López-Cabrera ◽

Rubén Orozco-Morales ◽

Jorge Armando Portal-Diaz ◽

Orlando Lovelle-Enríquez ◽

Marlén Pérez-Díaz

Keyword(s):

Artificial Intelligence ◽

X Ray ◽

X Ray Imaging ◽

Chest X Ray

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Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

10.21203/rs.3.rs-66836/v2 ◽

2021 ◽

Author(s):

Ali Mohammad Alqudah ◽

Shoroq Qazan ◽

Ihssan S. Masad

Keyword(s):

Artificial Intelligence ◽

Input Image ◽

Image Size ◽

X Ray ◽

Softmax Classifier ◽

Non Invasive ◽

Efficient Detection ◽

X Ray Imaging ◽

Chest X Ray

Abstract BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates.ResultsThe results showed that the best input image size in this framework was 64 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the 10-fold methodology the propose artificial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN.ConclusionsIn this study, an artificial intelligence framework has been proposed for the detection and classification of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classifiers, namely, SVM and KNN; in addition to the SoftMax classifier which is the default CNN classifier. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

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The diagnostic value of chest X-ray in coronavirus disease 2019: A comparative study of X-ray and CT

Science Progress ◽

10.1177/00368504211016204 ◽

2021 ◽

Vol 104 (3) ◽

pp. 003685042110162

Author(s):

Fengxia Zeng ◽

Yong Cai ◽

Yi Guo ◽

Weiguo Chen ◽

Min Lin ◽

...

Keyword(s):

Clinical Symptoms ◽

Diagnostic Value ◽

Rapid Identification ◽

Chest Ct ◽

Ct Scans ◽

Ground Glass ◽

X Ray ◽

Chest X Ray ◽

Laboratory Examinations ◽

Sensitivity Specificity

As the coronavirus disease 2019 (COVID-19) epidemic spreads around the world, the demand for imaging examinations increases accordingly. The value of conventional chest radiography (CCR) remains unclear. In this study, we aimed to investigate the diagnostic value of CCR in the detection of COVID-19 through a comparative analysis of CCR and CT. This study included 49 patients with 52 CT images and chest radiographs of pathogen-confirmed COVID-19 cases and COVID-19-suspected cases that were found to be negative (non-COVID-19). The performance of CCR in detecting COVID-19 was compared to CT imaging. The major signatures that allowed for differentiation between COVID-19 and non-COVID-19 cases were also evaluated. Approximately 75% (39/52) of images had positive findings on the chest x-ray examinations, while 80.7% (42/52) had positive chest CT scans. The COVID-19 group accounted for 88.4% (23/26) of positive chest X-ray examinations and 96.1% (25/26) of positive chest CT scans. The sensitivity, specificity, and accuracy of CCR for abnormal shadows were 88%, 80%, and 87%, respectively, for all patients. For the COVID-19 group, the accuracy of CCR was 92%. The primary signature on CCR was flocculent shadows in both groups. The shadows were primarily in the bi-pulmonary, which was significantly different from non-COVID-19 patients ( p = 0.008). The major CT finding of COVID-19 patients was ground-glass opacities in both lungs, while in non-COVID-19 patients, consolidations combined with ground-glass opacities were more common in one lung than both lungs ( p = 0.0001). CCR showed excellent performance in detecting abnormal shadows in patients with confirmed COVID-19. However, it has limited value in differentiating COVID-19 patients from non-COVID-19 patients. Through the typical epidemiological history, laboratory examinations, and clinical symptoms, combined with the distributive characteristics of shadows, CCR may be useful to identify patients with possible COVID-19. This will allow for the rapid identification and quarantine of patients.

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