Automated Estimation of Thoracic Rotation in Chest X-ray Radiographs: A Deep Learning Approach for Enhanced Technical Assessment.

OBJECTIVES: This study aims to develop an automated approach for estimating the vertical rotation of the thorax, which can be used to assess the technical adequacy of chest X-ray radiographs (CXRs). METHODS: Total 800 chest radiographs were used to train and establish segmentation networks for outlining the lungs and spine regions in chest X-ray images. By measuring the widths of the left and right lungs between the central line of segmented spine and the lateral sides of the segmented lungs, the quantification of thoracic vertical rotation was achieved. Additionally, a life-size, full body anthropomorphic phantom was employed to collect chest radiographic images under various specified rotation angles for assessing the accuracy of the proposed approach. RESULTS: The deep learning networks effectively segmented the anatomical structures of the lungs and spine. The proposed approach demonstrated a mean estimation error of less than 2° for thoracic rotation, surpassing existing techniques and indicating its superiority. CONCLUSIONS: The proposed approach offers a robust assessment of thoracic rotation and presents new possibilities for automated image quality control in chest X-ray examinations. ADVANCES IN KNOWLEDGE: This study presents a novel deep learning-based approach for the automated estimation of vertical thoracic rotation in chest X-ray radiographs. The proposed method enables a quantitative assessment of the technical adequacy of CXR examinations and opens up new possibilities for automated screening and quality control of radiographs.

Independent evaluation of the accuracy of 5 artificial intelligence software for detecting lung nodules on chest X-rays.

Background: The integration of artificial intelligence (AI) into medicine is growing, with some experts predicting its standalone use soon. However, skepticism remains due to limited positive outcomes from independent validations. This research evaluates AI software's effectiveness in analyzing chest X-rays (CXR) to identify lung nodules, a possible lung cancer indicator. Methods: This retrospective study analyzed 7,670,212 record pairs from radiological exams conducted between 2020 and 2022 during the Moscow Computer Vision Experiment, focusing on CXR and computed tomography (CT) scans. All images were acquired during clinical routine. The final dataset comprised 100 CXR images (50 with lung nodules, 50 without), selected consecutively and based on inclusion and exclusion criteria, to evaluate the performance of all five AI-based solutions, participating in the Moscow Computer Vision Experiment and analyzing CXR. The evaluation was performed in 3 stages. In the first stage, the probability of a nodule in the lung obtained from AI services was compared with the Ground Truth (1-there is a nodule, 0-there is no nodule). In the second stage, 3 radiologists evaluated the segmentation of nodules performed by the AI services (1-nodule correctly segmented, 0-nodule incorrectly segmented or not segmented at all). In the third stage, the same radiologists additionally evaluated the classification of the nodules (1-nodule correctly segmented and classified, 0-all other cases). The results obtained in stages 2 and 3 were compared with Ground Truth, which was common to all three stages. For each stage, diagnostic accuracy metrics were calculated for each AI service. Results: Three software solutions (Celsus, Lunit INSIGHT CXR, and qXR) demonstrated diagnostic metrics that matched or surpassed the vendor specifications, and achieved the highest area under the receiver operating characteristic curve (AUC) of 0.956 [95% confidence interval (CI): 0.918 to 0.994]. However, when evaluated by three radiologists for accurate nodule segmentation and classification, all solutions performed below the vendor-declared metrics, with the highest AUC reaching 0.812 (95% CI: 0.744 to 0.879). Meanwhile, all AI services demonstrated 100% specificity at stages 2 and 3 of the study. Conclusions: To ensure the reliability and applicability of AI-based software, it is crucial to validate performance metrics using high-quality datasets and engage radiologists in the evaluation process. Developers are recommended to improve the accuracy of the underlying models before allowing the standalone use of the software for lung nodule detection. The dataset created during the study may be accessed at https://mosmed.ai/datasets/mosmeddatargogksnalichiemiotsutstviemlegochnihuzlovtipvii/.

Expanding molecular diagnostic coverage for tuberculosis by combining computer-aided chest radiography and sputum specimen pooling: a modeling study from four high-burden countries.

Background: In 2022, fewer than half of persons with tuberculosis (TB) had access to molecular diagnostic tests for TB due to their high costs. Studies have found that the use of artificial intelligence (AI) software for chest X-ray (CXR) interpretation and sputum specimen pooling can each reduce the cost of testing. We modeled the combination of both strategies to estimate potential savings in consumables that could be used to expand access to molecular diagnostics. Methods: We obtained Xpert testing and positivity data segmented into deciles by AI probability scores for TB from the community- and healthcare facility-based active case finding conducted in Bangladesh, Nigeria, Viet Nam, and Zambia. AI scores in the model were based on CAD4TB version 7 (Zambia) and qXR (all other countries). We modeled four ordinal screening and testing approaches involving AI-aided CXR interpretation to indicate individual and pooled testing. Setting a false negative rate of 5%, for each approach we calculated additional and cumulative savings over the baseline of universal Xpert testing, as well as the theoretical expansion in diagnostic coverage. Results: In each country, the optimal screening and testing approach was to use AI to rule out testing in deciles with low AI scores and to guide pooled vs individual testing in persons with moderate and high AI scores, respectively. This approach yielded cumulative savings in Xpert tests over baseline ranging from 50.8% in Zambia to 57.5% in Nigeria and 61.5% in Bangladesh and Viet Nam. Using these savings, diagnostic coverage theoretically could be expanded by 34% to 160% across the different approaches and countries. Conclusions: Using AI software data generated during CXR interpretation to inform a differentiated pooled testing strategy may optimize TB diagnostic test use, and could extend molecular tests to more people who need them. The optimal AI thresholds and pooled testing strategy varied across countries, which suggests that bespoke screening and testing approaches may be needed for differing populations and settings. Supplementary Information: The online version contains supplementary material available at 10.1186/s44263-024-00081-2.

Urgent Direct Access to Diagnostic Services for General Practitioners: Bridging the Gap in Cancer Diagnosis.

Urgent direct access to diagnostic services for general practitioners (GPs) is a new pathway to capture any cancer diagnoses that may have been missed due to vague symptom presentations. Hence, GPs should look out for the key symptoms mentioned by NHS England that should prompt urgent direct access referrals for chest X-ray (CXR), computed tomography (CT) chest, MRI brain, ultrasound (US) abdomen and pelvis, and CT abdomen and pelvis. By implementing this approach, we can significantly reduce the time to diagnosis, while minimizing the number of visits to GP and specialist appointments prior to initiating investigations. However, the use of this pathway can only improve if access to diagnostic scans is improved. This needs to be done by ensuring all GPs in the country have access to directly request MRI brains, CT chest, abdomen, and pelvis. Further research into the impact of the urgent direct access pathway as well as investigating the number of GPs without access to these vital diagnostic services is required to fully improve and measure the progress of this referral pathway.

Correlation of fetal lung area with MRI derived pulmonary volume.

BACKGROUND: Neonatal chest-Xray (CXR)s are commonly performed as a first line investigation for the evaluation of respiratory complications. Although lung area derived from CXRs correlates well with functional assessments of the neonatal lung, it is not currently utilised in clinical practice, partly due to the lack of reference ranges for CXR-derived lung area in healthy neonates. Advanced MR techniques now enable direct evaluation of both fetal pulmonary volume and area. This study therefore aims to generate reference ranges for pulmonary volume and area in uncomplicated pregnancies, evaluate the correlation between prenatal pulmonary volume and area, as well as to assess the agreement between antenatal MRI-derived and neonatal CXR-derived pulmonary area in a cohort of fetuses that delivered shortly after the antenatal MRI investigation. METHODS: Fetal MRI datasets were retrospectively analysed from uncomplicated term pregnancies and a preterm cohort that delivered within 72 h of the fetal MRI. All examinations included T2 weighted single-shot turbo spin echo images in multiple planes. In-house pipelines were applied to correct for fetal motion using deformable slice-to-volume reconstruction. An MRI-derived lung area was manually segmented from the average intensity projection (AIP) images generated. Postnatal lung area in the preterm cohort was measured from neonatal CXRs within 24 h of delivery. Pearson correlation coefficient was used to correlate MRI-derived lung volume and area. A two-way absolute agreement was performed between the MRI-derived AIP lung area and CXR-derived lung area. RESULTS: Datasets from 180 controls and 10 preterm fetuses were suitable for analysis. Mean gestational age at MRI was 28.6 ± 4.2 weeks for controls and 28.7 ± 2.7 weeks for preterm neonates. MRI-derived lung area correlated strongly with lung volumes (p < 0.001). MRI-derived lung area had good agreement with the neonatal CXR-derived lung area in the preterm cohort [both lungs = 0.982]. CONCLUSION: MRI-derived pulmonary area correlates well with absolute pulmonary volume and there is good correlation between MRI-derived pulmonary area and postnatal CXR-derived lung area when delivery occurs within a few days of the MRI examination. This may indicate that fetal MRI derived lung area may prove to be useful reference ranges for pulmonary areas derived from CXRs obtained in the perinatal period.

Contribution to pulmonary diseases diagnostic from X-ray images using innovative deep learning models.

Pulmonary disease identification and characterization are among the most intriguing research topics of recent years since they require an accurate and prompt diagnosis. Although pulmonary radiography has helped in lung disease diagnosis, the interpretation of the radiographic image has always been a major concern for doctors and radiologists to reduce diagnosis errors. Due to their success in image classification and segmentation tasks, cutting-edge artificial intelligence techniques like machine learning (ML) and deep learning (DL) are widely encouraged to be applied in the field of diagnosing lung disorders and identifying them using medical images, particularly radiographic ones. For this end, the researchers are concurring to build systems based on these techniques in particular deep learning ones. In this paper, we proposed three deep-learning models that were trained to identify the presence of certain lung diseases using thoracic radiography. The first model, named "CovCXR-Net", identifies the COVID-19 disease (two cases: COVID-19 or normal). The second model, named "MDCXR3-Net", identifies the COVID-19 and pneumonia diseases (three cases: COVID-19, pneumonia, or normal), and the last model, named "MDCXR4-Net", is destined to identify the COVID-19, pneumonia and the pulmonary opacity diseases (4 cases: COVID-19, pneumonia, pulmonary opacity or normal). These models have proven their superiority in comparison with the state-of-the-art models and reached an accuracy of 99,09 %, 97.74 %, and 90,37 % respectively with three benchmarks.

Association between chest X-ray score and clinical outcome in COVID-19 patients: A study on modified radiographic assessment of lung edema score (mRALE) in Indonesia.

Radiological examinations such as chest X-rays (CXR) play a crucial role in the early diagnosis and determining disease severity in coronavirus disease 2019 (COVID-19). Various CXR scoring systems have been developed to quantitively assess lung abnormalities in COVID-19 patients, including CXR modified radiographic assessment of lung edema (mRALE). The aim of this study was to determine the relationship between mRALE scores and clinical outcome (mortality), as well as to identify the correlation between mRALE score and the severity of hypoxia (PaO2/FiO2 ratio). A retrospective cohort study was conducted among hospitalized COVID-19 patients at Dr. Soetomo General Academic Hospital Surabaya, Indonesia, from February to April 2022. All CXR data at initial admission were scored using the mRALE scoring system, and the clinical outcomes at the end of hospitalization were recorded. Of the total 178 COVID-19 patients, 62.9% survived after completing the treatment. Patients within non-survived had significantly higher quick sequential organ failure assessment (qSOFA) score (p<0.001), lower PaO2/FiO2 ratio (p=0.004), and higher blood urea nitrogen (p<0.001), serum creatinine (p<0.008) and serum glutamic oxaloacetic transaminase (p=0.001) levels. There was a significant relationship between mRALE score and clinical outcome (survived vs deceased) (p=0.024; contingency coefficient of 0.184); and mRALE score of ≥2.5 served as a risk factor for mortality among COVID-19 patients (relative risk of 1.624). There was a significant negative correlation between the mRALE score and PaO2/FiO2 ratio based on the Spearman correlation test (r=-0.346; p<0.001). The findings highlight that the initial mRALE score may serve as an independent predictor of mortality among hospitalized COVID-19 patients as well as proves its potential prognostic role in the management of COVID-19.

COVID-19 Hierarchical Classification Using a Deep Learning Multi-Modal.

Coronavirus disease 2019 (COVID-19), originating in China, has rapidly spread worldwide. Physicians must examine infected patients and make timely decisions to isolate them. However, completing these processes is difficult due to limited time and availability of expert radiologists, as well as limitations of the reverse-transcription polymerase chain reaction (RT-PCR) method. Deep learning, a sophisticated machine learning technique, leverages radiological imaging modalities for disease diagnosis and image classification tasks. Previous research on COVID-19 classification has encountered several limitations, including binary classification methods, single-feature modalities, small public datasets, and reliance on CT diagnostic processes. Additionally, studies have often utilized a flat structure, disregarding the hierarchical structure of pneumonia classification. This study aims to overcome these limitations by identifying pneumonia caused by COVID-19, distinguishing it from other types of pneumonia and healthy lungs using chest X-ray (CXR) images and related tabular medical data, and demonstrate the value of incorporating tabular medical data in achieving more accurate diagnoses. Resnet-based and VGG-based pre-trained convolutional neural network (CNN) models were employed to extract features, which were then combined using early fusion for the classification of eight distinct classes. We leveraged the hierarchal structure of pneumonia classification within our approach to achieve improved classification outcomes. Since an imbalanced dataset is common in this field, a variety of versions of generative adversarial networks (GANs) were used to generate synthetic data. The proposed approach tested in our private datasets of 4523 patients achieved a macro-avg F1-score of 95.9% and an F1-score of 87.5% for COVID-19 identification using a Resnet-based structure. In conclusion, in this study, we were able to create an accurate deep learning multi-modal to diagnose COVID-19 and differentiate it from other kinds of pneumonia and normal lungs, which will enhance the radiological diagnostic process.

Patch-Level Feature Selection for Thoracic Disease Classification by Chest X-ray Images Using Information Bottleneck.

Chest X-ray (CXR) examination serves as a widely employed clinical test in medical diagnostics. Many studied have tried to apply artificial intelligence (AI) programs to analyze CXR images. Despite numerous positive outcomes, assessing the applicability of AI models for comprehensive diagnostic support remains a formidable challenge. We observed that, even when AI models exhibit high accuracy on one dataset, their performance may deteriorate when tested on another. To address this issue, we propose incorporating a variational information bottleneck (VIB) at the patch level to enhance the generalizability of diagnostic support models. The VIB introduces a probabilistic model aimed at approximating the posterior distribution of latent variables given input data, thereby enhancing the model's generalization capabilities on unseen data. Unlike the conventional VIB approaches that flatten features and use a re-parameterization trick to sample a new latent feature, our method applies the trick to 2D feature maps. This design allows only important pixels to respond, and the model will select important patches in an image. Moreover, the proposed patch-level VIB seamlessly integrates with various convolutional neural networks, offering a versatile solution to improve performance. Experimental results illustrate enhanced accuracy in standard experiment settings. In addition, the method shows robust improvement when training and testing on different datasets.

Chest Radiography Pearls in Select Adult Congenital Heart Disease.

Congenital heart disease in adult patients (ACHD) includes individuals with native anatomic deformities and those who have benefited from corrective, ameliorative, or interventional heart and vascular interventions. Congenital heart disease is the most common birth defect, although with interventions most survive into adulthood. Newborns and children with complex congenital heart diseases that feature cyanosis fail to thrive, and once this is identified, heart failure can promptly undergo diagnostic evaluations and treatment. However, patients with simple congenital heart disease and subtle clinical signs and symptoms may escape diagnosis until adulthood or experience changes in their cardiac hemodynamics and physiology in settings such as pregnancy or newly diagnosed arrhythmias. The chest X-ray (CXR) is the most common X-ray among all radiological procedures. Individual features or a constellation of features on a CXR are often present in patients who have congenital heart disease. The ability to recognize these CXR features is a valuable skill for making the diagnosis of ACHD and for following these patients as they age, and can complement echocardiographic findings. When used well to diagnose ACHD, the CXR will be the sharpest arrow in the quiver.

Comparative Analysis of Machine-Learning Algorithms for Accurate Diagnosis of Lung Diseases Using Chest X-ray Images: A Study on Balanced and Unbalanced Data on Segmented and Unsegmented Images.

The study focused on the accurate diagnosis of lung diseases, considering the high number of lung disease-related deaths in the world. Chest x-ray images were used as they are a cost-effective and widely available diagnostic tool. Eight different machine learning algorithms were evaluated: Logistic Regression, Naive Bayes, k-Nearest Neighbors (kNN), Decision Tree, Random Forest, Support Vector Machine (SVM), Ridge, and Least Absolute Shrinkage and Selection Operator (LASSO). The study evaluated balanced and imbalanced datasets and looked at both segmented and unsegmented chest x-ray images. COVID-19, pneumonia, normal, and others were the four classes that were used in the investigation. Prior to attribute reduction, Decision Tree and Random Forest performed well on the balanced dataset, obtaining 74% test accuracy and 92% training accuracy. SVM functioned well as well, obtaining a 74% test accuracy. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two attribute reduction approaches that were applied. Decision Trees and Random Forests were able to attain the maximum training accuracy of 92%, while SVM was able to retain a test accuracy of 74% after attribute reduction. The findings also imply that some algorithms' performance may be enhanced by attribute reduction methods like PCA and LDA. For imbalanced data, Random Forest and SVM perform the best in terms of balanced accuracy of 80%. However, further research and experimentation may be needed to optimize the models and explore other potential algorithms or techniques.

Performance of thoracic ultrasonography compared with chest radiography for the detection of rib fractures using computed tomography as a reference standard.

OBJECTIVE: Although there is growing evidence that ultrasonography is superior to X-ray for rib fractures' detection, X-ray is still indicated as the most appropriate method. This has partially been attributed to a lack of studies using an appropriate reference modality. We aimed to compare the diagnostic accuracy of ultrasonography and X-ray in the detection of rib fractures, considering CT as the reference standard. MATERIALS AND METHODS: Within a 2.5-year period, all consecutive patients with clinically suspected rib fracture(s) following blunt chest trauma and available posteroanterior/anteroposterior X-ray and thoracic CT were prospectively studied and planned to undergo thoracic ultrasonography, by a single operator. All imaging examinations were evaluated for cortical rib fracture(s), and their location was recorded. The cartilaginous rib portions were not assessed. CTs and X-rays were evaluated retrospectively. Concomitant thoracic/extra-thoracic injuries were assessed on CT. Comparisons were performed with the Mann-Whitney U test and Fisher's exact test. RESULTS: Fifty-nine patients (32 males, 27 females; mean age, 53.1 ± 16.6 years) were included. CT, ultrasonography, and X-ray (40 posteroanterior/19 anteroposterior views) diagnosed 136/122/42 rib fractures in 56/54/27 patients, respectively. Ultrasonography and X-ray had sensitivity of 100%/40% and specificity of 89.7%/30.9% for rib fractures' detection. Ultrasound accuracy was 94.9% compared to 35.4% for X-rays (P < .001) in detecting individual rib fractures. Most fractures involved the 4th-9th ribs. Upper rib fractures were most commonly overlooked on ultrasonography. Thoracic cage/spine fractures and haemothorax represented the most common concomitant injuries. CONCLUSION: Ultrasonography appeared to be superior to X-ray for the detection of rib fractures with regard to a reference CT.

Approximating Intermediate Feature Maps of Self-Supervised Convolution Neural Network to Learn Hard Positive Representations in Chest Radiography.

Recent advances in contrastive learning have significantly improved the performance of deep learning models. In contrastive learning of medical images, dealing with positive representation is sometimes difficult because some strong augmentation techniques can disrupt contrastive learning owing to the subtle differences between other standardized CXRs compared to augmented positive pairs; therefore, additional efforts are required. In this study, we propose intermediate feature approximation (IFA) loss, which improves the performance of contrastive convolutional neural networks by focusing more on positive representations of CXRs without additional augmentations. The IFA loss encourages the feature maps of a query image and its positive pair to resemble each other by maximizing the cosine similarity between the intermediate feature outputs of the original data and the positive pairs. Therefore, we used the InfoNCE loss, which is commonly used loss to address negative representations, and the IFA loss, which addresses positive representations, together to improve the contrastive network. We evaluated the performance of the network using various downstream tasks, including classification, object detection, and a generative adversarial network (GAN) inversion task. The downstream task results demonstrated that IFA loss can improve the performance of effectively overcoming data imbalance and data scarcity; furthermore, it can serve as a perceptual loss encoder for GAN inversion. In addition, we have made our model publicly available to facilitate access and encourage further research and collaboration in the field.

Applicability of artificial intelligence-based computer-aided detection (AI-CAD) for pulmonary tuberculosis to community-based active case finding.

BACKGROUND: Artificial intelligence-based computer-aided detection (AI-CAD) for tuberculosis (TB) has become commercially available and several studies have been conducted to evaluate the performance of AI-CAD for pulmonary tuberculosis (TB) in clinical settings. However, little is known about its applicability to community-based active case-finding (ACF) for TB. METHODS: We analysed an anonymized data set obtained from a community-based ACF in Cambodia, targeting persons aged 55 years or over, persons with any TB symptoms, such as chronic cough, and persons at risk of TB, including household contacts. All of the participants in the ACF were screened by chest radiography (CXR) by Cambodian doctors, followed by Xpert test when they were eligible for sputum examination. Interpretation by an experienced chest physician and abnormality scoring by a newly developed AI-CAD were retrospectively conducted for the CXR images. With a reference of Xpert-positive TB or human interpretations, receiver operating characteristic (ROC) curves were drawn to evaluate the AI-CAD performance by area under the ROC curve (AUROC). In addition, its applicability to community-based ACFs in Cambodia was examined. RESULTS: TB scores of the AI-CAD were significantly associated with the CXR classifications as indicated by the severity of TB disease, and its AUROC as the bacteriological reference was 0.86 (95% confidence interval 0.83-0.89). Using a threshold for triage purposes, the human reading and bacteriological examination needed fell to 21% and 15%, respectively, detecting 95% of Xpert-positive TB in ACF. For screening purposes, we could detect 98% of Xpert-positive TB cases. CONCLUSIONS: AI-CAD is applicable to community-based ACF in high TB burden settings, where experienced human readers for CXR images are scarce. The use of AI-CAD in developing countries has the potential to expand CXR screening in community-based ACFs, with a substantial decrease in the workload on human readers and laboratory labour. Further studies are needed to generalize the results to other countries by increasing the sample size and comparing the AI-CAD performance with that of more human readers.

Gene Expression of Abcc2 and Its Regulation by Chicken Xenobiotic Receptor.

Membrane transporter multidrug resistance-associated protein 2 (MRP2/Abcc2) exhibits high pharmaco-toxicological relevance because it exports multiple cytotoxic compounds from cells. However, no detailed information about the gene expression and regulation of MRP2 in chickens is yet available. Here, we sought to investigate the expression distribution of Abcc2 in different tissues of chicken and then determine whether Abcc2 expression is induced by chicken xenobiotic receptor (CXR). The bioinformatics analyses showed that MRP2 transporters have three transmembrane structural domains (MSDs) and two highly conserved nucleotide structural domains (NBDs), and a close evolutionary relationship with turkeys. Tissue distribution analysis indicated that Abcc2 was highly expressed in the liver, kidney, duodenum, and jejunum. When exposed to metyrapone (an agonist of CXR) and ketoconazole (an antagonist of CXR), Abcc2 expression was upregulated and downregulated correspondingly. We further confirmed that Abcc2 gene regulation is dependent on CXR, by overexpressing and interfering with CXR, respectively. We also demonstrated the induction of Abcc2 expression and the activity of ivermectin, with CXR being a likely mediator. Animal experiments demonstrated that metyrapone and ivermectin induced Abcc2 in the liver, kidney, and duodenum of chickens. Together, our study identified the gene expression of Abcc2 and its regulation by CXR in chickens, which may provide novel targets for the reasonable usage of veterinary drugs.

Routine chest radiography after thoracostomy tube removal and during postoperative follow-up is not necessary after lung resection.

OBJECTIVES: The need for routine chest radiography following chest tube removal after elective pulmonary resection may be unnecessary in most patients. The purpose of this study was to determine the safety of eliminating routine chest radiography in these patients. METHODS: Patients who underwent elective pulmonary resection, excluding pneumonectomy, for benign or malignant indications between 2007 and 2013 were reviewed. Patients with in-hospital mortality or without routine follow-up were excluded. During this interval, our practice transitioned from ordering routine chest radiography after chest tube removal and at the first postoperative clinic visit to obtaining imaging based on symptomatology. The primary outcome was changes in management from results of chest radiography obtained routinely versus for symptoms. Characteristics and outcomes were compared using the Student t test and chi-square analyses. RESULTS: A total of 322 patients met inclusion criteria. Ninety-three patients underwent a routine same-day post-pull chest radiography, and 229 patients did not. Thirty-three patients (14.4%) in the nonroutine chest radiography cohort received imaging for symptoms, in whom 8 (24.2%) resulted in management changes. Only 3.2% of routine post-pull chest radiography resulted in management changes versus 3.5% of unplanned chest radiography with no adverse outcomes (P = .905). At outpatient postoperative follow-up, 146 patients received routine chest radiography; none resulted in a change in management. Of the 176 patients who did not have planned chest radiography at follow-up, 12 (6.8%) underwent chest radiography for symptoms. Two of these patients required readmission and chest tube reinsertion. CONCLUSIONS: Reserving imaging for patients with symptoms after chest tube removal and follow-up after elective lung resections resulted in a higher percentage of meaningful changes in clinical management.

Clinicians' Interpretation of Unreported Chest Radiographs in Biologic Prescription Workup Service: A Comprehensive Analysis.

Clinicians without a radiology specialization face difficulties when they attempt to interpret chest X-rays (CXRs), a crucial and extensively utilized diagnostic tool that plays a fundamental role in the detection of pulmonary and cardiovascular disorders. This cross-sectional study assessed the confidence and competence of clinicians, including junior specialty trainees, higher specialty trainees, and specialist nurses, in interpreting CXRs before starting biological treatment. An online survey was used to collect data from clinicians in various healthcare settings, focusing on their experience, training, confidence levels, and CXR interpretation proficiency. The survey uncovered clinicians' insufficient confidence in interpreting the pre-biological screening CXRs despite their clinical expertise. This uncertainty raises concerns about potential misinterpretations, affecting timely treatment decisions. A Kruskal-Wallis test indicated a significant difference between training levels required with a p-value of 0.001, rejecting the null hypothesis. Subsequently, a Dunn-Bonferroni test revealed that the higher specialty trainee-specialist nurse pair differed significantly, with the specialist nurse group requiring more training. This study highlighted the need for enhanced radiology education for clinicians involved in chest radiograph interpretation for pre-biological screening. Implementing a structured training program is essential to improve skills and ensure accurate interpretation of non-formally reported chest radiographs, ultimately enhancing patient outcomes and healthcare practices.

MSTAC: A Multi-Stage Automated Classification of COVID-19 Chest X-ray Images Using Stacked CNN Models.

This study introduces a Multi-Stage Automated Classification (MSTAC) system for COVID-19 chest X-ray (CXR) images, utilizing stacked Convolutional Neural Network (CNN) models. Suspected COVID-19 patients often undergo CXR imaging, making it valuable for disease classification. The study collected CXR images from public datasets and aimed to differentiate between COVID-19, non-COVID-19, and healthy cases. MSTAC employs two classification stages: the first distinguishes healthy from unhealthy cases, and the second further classifies COVID-19 and non-COVID-19 cases. Compared to a single CNN-Multiclass model, MSTAC demonstrated superior classification performance, achieving 97.30% accuracy and sensitivity. In contrast, the CNN-Multiclass model showed 94.76% accuracy and sensitivity. MSTAC's effectiveness is highlighted in its promising results over the CNN-Multiclass model, suggesting its potential to assist healthcare professionals in efficiently diagnosing COVID-19 cases. The system outperformed similar techniques, emphasizing its accuracy and efficiency in COVID-19 diagnosis. This research underscores MSTAC as a valuable tool in medical image analysis for enhanced disease classification.

Using Artificial Intelligence to Stratify Normal versus Abnormal Chest X-rays: External Validation of a Deep Learning Algorithm at East Kent Hospitals University NHS Foundation Trust.

Background: The chest radiograph (CXR) is the most frequently performed radiological examination worldwide. The increasing volume of CXRs performed in hospitals causes reporting backlogs and increased waiting times for patients, potentially compromising timely clinical intervention and patient safety. Implementing computer-aided detection (CAD) artificial intelligence (AI) algorithms capable of accurate and rapid CXR reporting could help address such limitations. A novel use for AI reporting is the classification of CXRs as 'abnormal' or 'normal'. This classification could help optimize resource allocation and aid radiologists in managing their time efficiently. Methods: qXR is a CE-marked computer-aided detection (CAD) software trained on over 4.4 million CXRs. In this retrospective cross-sectional pre-deployment study, we evaluated the performance of qXR in stratifying normal and abnormal CXRs. We analyzed 1040 CXRs from various referral sources, including general practices (GP), Accident and Emergency (A&E) departments, and inpatient (IP) and outpatient (OP) settings at East Kent Hospitals University NHS Foundation Trust. The ground truth for the CXRs was established by assessing the agreement between two senior radiologists. Results: The CAD software had a sensitivity of 99.7% and a specificity of 67.4%. The sub-group analysis showed no statistically significant difference in performance across healthcare settings, age, gender, and X-ray manufacturer. Conclusions: The study showed that qXR can accurately stratify CXRs as normal versus abnormal, potentially reducing reporting backlogs and resulting in early patient intervention, which may result in better patient outcomes.

Comparison between ultrasound and chest X-ray to confirm central venous catheter tip position.

Background: Mechanical central venous catheter (CVC) placement complications are mostly malposition or iatrogenic pneumothorax. Verification of catheter position by chest X-ray (CXR) is usually performed postoperatively. Objectives: This prospective observational study assessed the diagnostic accuracy of peri-operative ultrasound and a 'bubble test' to detect malposition and pneumothorax. Method: Sixty-one patients undergoing peri-operative CVC placement were included. An ultrasound protocol was used to directly visualise the CVC, perform the 'bubble test' and assess for the presence of pneumothorax. The time from agitated saline injection to visualisation of microbubbles in the right atrium was evaluated to determine the correct position of the CVC. The time required to perform the ultrasound assessment was compared to that of conducting the CXR. Results: Chest X-ray identified 12 (19.7%) malpositions while ultrasound identified 8 (13.1%). Ultrasound showed a sensitivity of 0.85 (95% confidence interval [CI]: 0.72 to 0.93) and a specificity of 0.5 (95% CI: 0.16 to 0.84). The positive and negative predictive values were 0.92 (95% CI: 0.80 to 0.98) and 0.33 (95% CI: 0.10 to 0.65), respectively. No pneumothorax was identified on ultrasound and CXR. The median time for ultrasound assessment was significantly shorter at 4 min (interquartile range [IQR]: 3-6 min), compared to performing a CXR that required a median time of 29 min (IQR: 18-56 min) (p < 0.0001). Conclusion: This study showed that ultrasound produced a high sensitivity and moderate specificity in detecting CVC malposition. Contribution: Ultrasound can improve efficiency when used as a rapid bedside screening test to detect CVC malposition.