For Tuberculosis, Not "To Screen or Not to Screen?" but "Who?" and "How?"

Active case finding leveraging new molecular diagnostics and chest X-rays with automated interpretation algorithms is increasingly being developed for high-risk populations to drive down tuberculosis incidence. We consider why such an approach did not deliver a decline in tuberculosis prevalence in Brazilian prison populations and what to consider next.

Evaluation of C-Reactive Protein and Computer-Aided Analysis of Chest X-rays as Tuberculosis Triage Tests at Health Facilities in Lesotho and South Africa.

BACKGROUND: To improve tuberculosis case-finding, rapid, non-sputum triage tests need to be developed according to the World Health Organization target product profile (TPP) (>90% sensitivity, >70% specificity). We prospectively evaluated and compared artificial intelligence-based, computer-aided detection software, CAD4TBv7, and C-reactive protein assay (CRP) as triage tests at health facilities in Lesotho and South Africa. METHODS: Adults (≥18 years) presenting with ≥1 of the 4 cardinal tuberculosis symptoms were consecutively recruited between February 2021 and April 2022. After informed consent, each participant underwent a digital chest X-ray for CAD4TBv7 and a CRP test. Participants provided 1 sputum sample for Xpert MTB/RIF Ultra and Xpert MTB/RIF and 1 for liquid culture. Additionally, an expert radiologist read the chest X-rays via teleradiology. For primary analysis, a composite microbiological reference standard (ie, positive culture or Xpert Ultra) was used. RESULTS: We enrolled 1392 participants, 48% were people with HIV and 24% had previously tuberculosis. The receiver operating characteristic curve for CAD4TBv7 and CRP showed an area under the curve of .87 (95% CI: .84-.91) and .80 (95% CI: .76-.84), respectively. At thresholds corresponding to 90% sensitivity, specificity was 68.2% (95% CI: 65.4-71.0%) and 38.2% (95% CI: 35.3-41.1%) for CAD4TBv7 and CRP, respectively. CAD4TBv7 detected tuberculosis as well as an expert radiologist. CAD4TBv7 almost met the TPP criteria for tuberculosis triage. CONCLUSIONS: CAD4TBv7 is accurate as a triage test for patients with tuberculosis symptoms from areas with a high tuberculosis and HIV burden. The role of CRP in tuberculosis triage requires further research. CLINICAL TRIALS REGISTRATION: Clinicaltrials.gov identifier: NCT04666311.

Chest Radiograph Screening for Detecting Subclinical Tuberculosis in Asymptomatic Household Contacts, Peru.

The World Health Organization's end TB strategy promotes the use of symptom and chest radiograph screening for tuberculosis (TB) disease. However, asymptomatic early states of TB beyond latent TB infection and active disease can go unrecognized using current screening criteria. We conducted a longitudinal cohort study enrolling household contacts initially free of TB disease and followed them for the occurrence of incident TB over 1 year. Among 1,747 screened contacts, 27 (52%) of the 52 persons in whom TB subsequently developed during follow-up had a baseline abnormal radiograph. Of contacts without TB symptoms, persons with an abnormal radiograph were at higher risk for subsequent TB than persons with an unremarkable radiograph (adjusted hazard ratio 15.62 [95% CI 7.74-31.54]). In young adults, we found a strong linear relationship between radiograph severity and time to TB diagnosis. Our findings suggest chest radiograph screening can extend to detecting early TB states, thereby enabling timely intervention.

The role of chest X-rays when screening for latent tuberculosis infection in patients with inflammatory bowel disease before starting biologic treatment.

BACKGROUND: Guidelines generally recommend a combination of immunological assays and chest X-ray imaging (CXR) when screening for latent tuberculosis infection (LTBI) prior to biologic treatment in inflammatory bowel disease (IBD). OBJECTIVE: To investigate whether CXR identify patients with suspected LTBI/TB who were not identified with QuantiFERON tests (QFT) when screening for LTBI/TB before starting biologic treatment in IBD patients. METHODS: Single-center, retrospective cohort study of patients with inflammatory bowel disease who had a QFT and a CXR prior to initiation of biologic treatment in a 5-year period (October 1st, 2017 to September 30th, 2022). RESULTS: 520 patients (56% female, mean age 40.1 years) were included. The majority had none or few risk factors for TB (as reflected by the demographic characteristics) but some risk factors for having false negative QFT results (concurrent glucocorticoid treatment and inflammatory activity). QFT results were positive in 8 patients (1.5%), inconclusive in 18 (3.5%) and negative in 494 (95.0%). Only 1 patient (0.19%) had CXR findings suspicious of LTBI. This patient also had a positive QFT and was subsequently diagnosed with active TB. All patients with negative or inconclusive QFT had CXR without any findings suggesting LTBI/TB. One patient developed active TB after having initiated biologic treatment in spite of having negative QFT and a normal CXR at screening. CONCLUSION: In a population with low risk of TB, the benefits of supplementing the QFT with a CXR are limited and are unlikely to outweigh the cost in both patient test-burden, radioactive exposure, and economic resources.

Enhancing multi-class lung disease classification in chest x-ray images: A hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach.

One of the most used diagnostic imaging techniques for identifying a variety of lung and bone-related conditions is the chest X-ray. Recent developments in deep learning have demonstrated several successful cases of illness diagnosis from chest X-rays. However, issues of stability and class imbalance still need to be resolved. Hence in this manuscript, multi-class lung disease classification in chest x-ray images using a hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach is proposed (MPNN-Hyb-MRF-VEA). Initially, the input chest X-ray images are taken from the Covid-Chest X-ray dataset. Anisotropic diffusion Kuwahara filtering (ADKF) is used to enhance the quality of these images and lower noise. To capture significant discriminative features, the Term frequency-inverse document frequency (TF-IDF) based feature extraction method is utilized in this case. The Multilayer Perceptron Neural Network (MPNN) serves as the classification model for multi-class lung disorders classification as COVID-19, pneumonia, tuberculosis (TB), and normal. A Hybrid Manta-Ray Foraging and Volcano Eruption Algorithm (Hyb-MRF-VEA) is introduced to further optimize and fine-tune the MPNN's parameters. The Python platform is used to accurately evaluate the proposed methodology. The performance of the proposed method provides 23.21%, 12.09%, and 5.66% higher accuracy compared with existing methods like NFM, SVM, and CNN respectively.

Impact of routine bedside lung ultrasound implementation on chest x-ray requests for mechanically ventilated children: a before-after uncontrolled quality improvement project.

Lung imaging techniques are crucial for managing ventilated patients in pediatric intensive care units (PICUs). Bedside chest x-ray has limitations such as low sensitivity and radiation exposure risks. Recently, lung ultrasound has emerged as a promising technology offering advantages such as real-time monitoring and radiation-free imaging. However, the integration of lung ultrasound into clinical practice raises questions about its impact on chest x-ray prescriptions. This study aims to assess whether implementing lung ultrasound reduces reliance on chest x-rays for ventilated pediatric patients in the PICU. This before-and-after uncontrolled quality improvement project was conducted from January 2022 to December 2023 in a referral PICU. The study included three phases: retrospective evaluation, learning phase, and prospective evaluation. Patients aged under 14 years, intubated, and ventilated for ≤ 30 days were included. Lung ultrasound was performed using a standardized protocol, and chest x-rays were conducted as per clinical indications. During the study period, 430 patients were admitted to the PICU, with 142 requiring mechanical ventilation. Implementation of routine bedside lung ultrasound led to a 39% reduction in chest x-ray requests (p < 0.001). Additionally, there was a significant decrease in irradiation exposure and a 27% reduction in costs associated with chest x-rays.Conclusion: Routine bedside lung ultrasound is a valuable tool in the modern PICU, it reduces the number of chest x-rays, with reduced radiation exposure and a potential cost savings. What is known: • Bedside chest x-ray is the main imaging study in ventilated pediatric patients • Chest x-ray is a valuable tool in pediatric critical care but it is associated with irradiation exposure What is new: • Implementation of bedside lung ultrasound in pediatric critical care unites reduces the chest x-rays requests and therefore patient-irradiation.

Chest x-ray has low sensitivity to detect silicosis in artificial stone benchtop industry workers.

BACKGROUND AND OBJECTIVE: Chest x-ray (CXR) remains a core component of health monitoring guidelines for workers at risk of exposure to crystalline silica. There has however been a lack of evidence regarding the sensitivity of CXR to detect silicosis in artificial stone benchtop industry workers. METHODS: Paired CXR and high-resolution computed tomography (HRCT) images were acquired from 110 artificial stone benchtop industry workers. Blinded to the clinical diagnosis, each CXR and HRCT was independently read by two thoracic radiologists from a panel of seven, in accordance with International Labour Office (ILO) methodology for CXR and International Classification of HRCT for Occupational and Environmental Respiratory Diseases. Accuracy of screening positive (ILO major category 1, 2 or 3) and negative (ILO major category 0) CXRs were compared with identification of radiological features of silicosis on HRCT. RESULTS: CXR was positive for silicosis in 27/110 (24.5%) workers and HRCT in 40/110 (36.4%). Of the 83 with a negative CXR (ILO category 0), 15 (18.1%) had silicosis on HRCT. All 11 workers with ILO category 2 or 3 CXRs had silicosis on HRCT. In 99 workers ILO category 0 or 1 CXRs, the sensitivity of screening positive CXR compared to silicosis identified by HRCT was 48% (95%CI 29-68) and specificity 97% (90-100). CONCLUSION: Compared to HRCT, sensitivity of CXR was low but specificity was high. Reliance on CXR for health monitoring would provide false reassurance for many workers, delay management and underestimate the prevalence of silicosis in the artificial stone benchtop industry.

Detection of COVID-19 using edge devices by a light-weight convolutional neural network from chest X-ray images.

Deep learning is a highly significant technology in clinical treatment and diagnostics nowadays. Convolutional Neural Network (CNN) is a new idea in deep learning that is being used in the area of computer vision. The COVID-19 detection is the subject of our medical study. Researchers attempted to increase the detection accuracy but at the cost of high model complexity. In this paper, we desire to achieve better accuracy with little training space and time so that this model easily deployed in edge devices. In this paper, a new CNN design is proposed that has three stages: pre-processing, which removes the black padding on the side initially; convolution, which employs filter banks; and feature extraction, which makes use of deep convolutional layers with skip connections. In order to train the model, chest X-ray images are partitioned into three sets: learning(0.7), validation(0.1), and testing(0.2). The models are then evaluated using the test and training data. The LMNet, CoroNet, CVDNet, and Deep GRU-CNN models are the other four models used in the same experiment. The propose model achieved 99.47% & 98.91% accuracy on training and testing respectively. Additionally, it achieved 97.54%, 98.19%, 99.49%, and 97.86% scores for precision, recall, specificity, and f1-score respectively. The proposed model obtained nearly equivalent accuracy and other similar metrics when compared with other models but greatly reduced the model complexity. Moreover, it is found that proposed model is less prone to over fitting as compared to other models.

Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is underdiagnosed with the current gold standard measure pulmonary function test (PFT). A more sensitive and simple option for early detection and severity evaluation of COPD could benefit practitioners and patients. METHODS: In this multicenter retrospective study, frontal chest X-ray (CXR) images and related clinical information of 1055 participants were collected and processed. Different deep learning algorithms and transfer learning models were trained to classify COPD based on clinical data and CXR images from 666 subjects, and validated in internal test set based on 284 participants. External test including 105 participants was also performed to verify the generalization ability of the learning algorithms in diagnosing COPD. Meanwhile, the model was further used to evaluate disease severity of COPD by predicting different grads. RESULTS: The Ensemble model showed an AUC of 0.969 in distinguishing COPD by simultaneously extracting fusion features of clinical parameters and CXR images in internal test, better than models that used clinical parameters (AUC = 0.963) or images (AUC = 0.946) only. For the external test set, the AUC slightly declined to 0.934 in predicting COPD based on clinical parameters and CXR images. When applying the Ensemble model to determine disease severity of COPD, the AUC reached 0.894 for three-classification and 0.852 for five-classification respectively. CONCLUSION: The present study used DL algorithms to screen COPD and predict disease severity based on CXR imaging and clinical parameters. The models showed good performance and the approach might be an effective case-finding tool with low radiation dose for COPD diagnosis and staging.

Artificial intelligence-based model for predicting pulmonary arterial hypertension on chest x-ray images.

BACKGROUND: Pulmonary arterial hypertension is a serious medical condition. However, the condition is often misdiagnosed or a rather long delay occurs from symptom onset to diagnosis, associated with decreased 5-year survival. In this study, we developed and tested a deep-learning algorithm to detect pulmonary arterial hypertension using chest X-ray (CXR) images. METHODS: From the image archive of Chiba University Hospital, 259 CXR images from 145 patients with pulmonary arterial hypertension and 260 CXR images from 260 control patients were identified; of which 418 were used for training and 101 were used for testing. Using the testing dataset for each image, the algorithm outputted a numerical value from 0 to 1 (the probability of the pulmonary arterial hypertension score). The training process employed a binary cross-entropy loss function with stochastic gradient descent optimization (learning rate parameter, α = 0.01). In addition, using the same testing dataset, the algorithm's ability to identify pulmonary arterial hypertension was compared with that of experienced doctors. RESULTS: The area under the curve (AUC) of the receiver operating characteristic curve for the detection ability of the algorithm was 0.988. Using an AUC threshold of 0.69, the sensitivity and specificity of the algorithm were 0.933 and 0.982, respectively. The AUC of the algorithm's detection ability was superior to that of the doctors. CONCLUSION: The CXR image-derived deep-learning algorithm had superior pulmonary arterial hypertension detection capability compared with that of experienced doctors.

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.

Pneumonia Prevalence Upon Chest Radiography According to Vaccination Status Among Patients Under 50 Years of Age With Coronavirus Disease 2019.

BACKGROUND: Coronavirus disease 2019 (COVID-19) vaccination is effective in preventing the disease transmission and progression. However, the relatively mild disease course of the omicron variant and the decrease in antibodies over time after vaccination raise questions about the effectiveness of vaccination, especially in young people. We compared the prevalence of pneumonia and chest X-ray severity score according to vaccination status among patients < 50 years old with COVID-19. METHODS: From January 17 to March 17, 2022, 579 patients with COVID-19, who were < 50 years old and had a known vaccination history in our institution, were all included in this study. All patients underwent initial chest radiography, and follow-up chest radiographs were obtained every two days until discharge. Pneumonia was scored from the radiographs using the Brixia scoring system. The scores of the six lung zones were added for a total score ranging from 0 to 18. Patients were divided into four groups according to 10-year age intervals. Differences between groups were analyzed using the χ² or Fisher's exact tests for categorical variables and the Kruskal-Wallis test or analysis of variance for continuous variables. RESULTS: Among patients aged 12-19 years, the prevalence of pneumonia did not differ depending on vaccination status (non-vaccinated vs. vaccinated, 1/47 [2.1%] vs. 1/18 [5.6%]; P = 0.577). Among patients in their 20s, the prevalence of pneumonia was significantly higher among non-vaccinated patients than among vaccinated patients (8/28, 28.6% vs. 7/138, 5.1%, P < 0.001), similar to patients in their 40s (32/52 [61.5%] vs. 18/138 [13.0%]; P < 0.001). The chest X-ray severity score was also significantly higher in non-vaccinated patients than that in vaccinated patients in their 20s to their 40s (P < 0.001), but not among patients aged 12-19 years (P = 0.678). CONCLUSION: In patients aged 20-49 years, vaccinated patients had a significantly lower prevalence of pneumonia and chest X-ray severity score than non-vaccinated patients.

BarlowTwins-CXR: enhancing chest X-ray abnormality localization in heterogeneous data with cross-domain self-supervised learning.

BACKGROUND: Chest X-ray imaging based abnormality localization, essential in diagnosing various diseases, faces significant clinical challenges due to complex interpretations and the growing workload of radiologists. While recent advances in deep learning offer promising solutions, there is still a critical issue of domain inconsistency in cross-domain transfer learning, which hampers the efficiency and accuracy of diagnostic processes. This study aims to address the domain inconsistency problem and improve autonomic abnormality localization performance of heterogeneous chest X-ray image analysis, particularly in detecting abnormalities, by developing a self-supervised learning strategy called "BarlwoTwins-CXR". METHODS: We utilized two publicly available datasets: the NIH Chest X-ray Dataset and the VinDr-CXR. The BarlowTwins-CXR approach was conducted in a two-stage training process. Initially, self-supervised pre-training was performed using an adjusted Barlow Twins algorithm on the NIH dataset with a Resnet50 backbone pre-trained on ImageNet. This was followed by supervised fine-tuning on the VinDr-CXR dataset using Faster R-CNN with Feature Pyramid Network (FPN). The study employed mean Average Precision (mAP) at an Intersection over Union (IoU) of 50% and Area Under the Curve (AUC) for performance evaluation. RESULTS: Our experiments showed a significant improvement in model performance with BarlowTwins-CXR. The approach achieved a 3% increase in mAP50 accuracy compared to traditional ImageNet pre-trained models. In addition, the Ablation CAM method revealed enhanced precision in localizing chest abnormalities. The study involved 112,120 images from the NIH dataset and 18,000 images from the VinDr-CXR dataset, indicating robust training and testing samples. CONCLUSION: BarlowTwins-CXR significantly enhances the efficiency and accuracy of chest X-ray image-based abnormality localization, outperforming traditional transfer learning methods and effectively overcoming domain inconsistency in cross-domain scenarios. Our experiment results demonstrate the potential of using self-supervised learning to improve the generalizability of models in medical settings with limited amounts of heterogeneous data. This approach can be instrumental in aiding radiologists, particularly in high-workload environments, offering a promising direction for future AI-driven healthcare solutions.

Methods of measurement for pneumothorax in pediatric patients: a systematic review.

Accurate measurement of pneumothorax (PTX) size is necessary to guide clinical decision making; however, there is no consensus as to which method should be used in pediatric patients. This systematic review seeks to identify and evaluate the methods used to measure PTX size with CXR in pediatric patients. A systematic review of the literature through 2021 following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was conducted using the following databases: Ovid/MEDLINE, Scopus, Cochrane Database of Controlled Trials, Cochrane Database of Systematic Reviews, and Google Scholar. Original research articles that included pediatric patients (< 18 years old) and outlined the PTX measurement method were included. 45 studies were identified and grouped by method (Kircher and Swartzel, Rhea, Light, Collins, Other) and societal guideline used. The most used method was Collins (n = 16; 35.6%). Only four (8.9%) studies compared validated methods. All found the Collins method to be accurate. Seven (15.6%) studies used a standard classification guideline and 3 (6.7%) compared guidelines and found significant disagreement between them. Pediatric-specific measurement guidelines for PTX are needed to establish consistency and uniformity in both research and clinical practice. Until there is a better method, the Collins method is preferred.

X-ray interpretation in emergency department in the Kingdom of Saudi Arabia. Do we need the radiologist?

INTRODUCTION: Chest x-rays are widely used for diagnosing chest pathology worldwide. Pediatricians frequently interpret chest radiographs in the emergency department, guiding patient management. This study aims to assess the competency of non-radiologists in interpreting emergency chest x-rays and compare it with trainees of different levels to determine the necessity of radiologist input. METHODOLOGY: A cross-sectional online survey was conducted in Saudi Arabia from September to October 2023, involving 385 participants, including pediatricians and medical interns from various regions. Carefully selected questions addressed a range of x-ray abnormalities in pediatric emergencies, assessing fundamental understanding of x-ray interpretation, such as inspiratory vs. expiratory and AP or PA films. RESULTS: The study included 385 participants, primarily Saudi nationals in the eastern region, with an equal gender distribution and ages ranging from 20 to 29 years. Approximately 29.09% demonstrated fair knowledge, with 28% being Junior Pediatrics Residents, 18% Pediatric Consultants, and 15% Senior Pediatrics Residents. Fair knowledge was significantly associated with individuals aged 20-29 years, residents of the western region, and Junior Pediatrics Residents. Clinical knowledge varied among different groups, with 59% correctly identifying atypical pneumonia and 65% recognizing asymmetrical hyperinflation. However, rates for other conditions differed, with low identification of potential foreign body aspiration and film type. Accuracy in identifying tension pneumothorax and hyperlucency varied among clinicians. Pleural effusion films had a 65% identification rate for the diagnosis, but only 28% accurately described the X-ray and selected the correct answer for lung opacity. CONCLUSION: The study concluded that 29.9% of the participating physicians exhibited fair knowledge of common pediatric emergency radiological films. Junior pediatric residents showed the best knowledge, and Tetralogy of Fallot, asymmetrical hyperinflation, and pleural effusion had the highest recognition rates. In conclusion, there is still a need for radiologists in the pediatric emergency department to ensure optimal functioning.

Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times.

Medical professionals in thoracic medicine routinely analyze chest X-ray images, often comparing pairs of images taken at different times to detect lesions or anomalies in patients. This research aims to design a computer-aided diagnosis system that enhances the efficiency of thoracic physicians in comparing and diagnosing X-ray images, ultimately reducing misjudgments. The proposed system encompasses four key components: segmentation, alignment, comparison, and classification of lung X-ray images. Utilizing a public NIH Chest X-ray14 dataset and a local dataset gathered by the Chiayi Christian Hospital in Taiwan, the efficacy of both the traditional methods and deep-learning methods were compared. Experimental results indicate that, in both the segmentation and alignment stages, the deep-learning method outperforms the traditional method, achieving higher average IoU, detection rates, and significantly reduced processing time. In the comparison stage, we designed nonlinear transfer functions to highlight the differences between pre- and post-images through heat maps. In the classification stage, single-input and dual-input network architectures were proposed. The inclusion of difference information in single-input networks enhances AUC by approximately 1%, and dual-input networks achieve a 1.2-1.4% AUC increase, underscoring the importance of difference images in lung disease identification and classification based on chest X-ray images. While the proposed system is still in its early stages and far from clinical application, the results demonstrate potential steps forward in the development of a comprehensive computer-aided diagnostic system for comparative analysis of chest X-ray images.

DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features.

Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results in millions of deaths every year. Chest X-ray images pose a challenge for classification due to their visual similarity, leading to confusion among radiologists. To imitate those issues, we created an automated system with a large data hub that contains 17 datasets of chest X-ray images for a total of 71,096, and we aim to classify ten different disease classes. For combining various resources, our large datasets contain noise and annotations, class imbalances, data redundancy, etc. We conducted several image pre-processing techniques to eliminate noise and artifacts from images, such as resizing, de-annotation, CLAHE, and filtering. The elastic deformation augmentation technique also generates a balanced dataset. Then, we developed DeepChestGNN, a novel medical image classification model utilizing a deep convolutional neural network (DCNN) to extract 100 significant deep features indicative of various lung diseases. This model, incorporating Batch Normalization, MaxPooling, and Dropout layers, achieved a remarkable 99.74% accuracy in extensive trials. By combining graph neural networks (GNNs) with feedforward layers, the architecture is very flexible when it comes to working with graph data for accurate lung disease classification. This study highlights the significant impact of combining advanced research with clinical application potential in diagnosing lung diseases, providing an optimal framework for precise and efficient disease identification and classification.

A prospective observational study of pH testing to confirm ongoing nasogastric tube position.

AIMS AND OBJECTIVES: To measure the reliability of pH testing to confirm ongoing nasogastric tube (NGT) position and to document associated complications. BACKGROUND: Confirming NGT position is essential, as use of an incorrectly positioned tube can cause harm. Substantial evidence examines initial confirmation of NGT position, yet limited evidence exists considers NGT displacement which is identified via ongoing NGT position tests. In the NHS, pH testing is recommended to confirm ongoing NGT position; however, there may be an association with excess X-rays and missed enteral nutrition and/or medications. DESIGN: Prospective observational study using STROBE checklist. METHODS: Data collected from medical records of 136 patients with NGTs in a London NHS Trust included pH tests, test results and complications related to ongoing pH tests which failed to confirm the tube was positioned in the stomach, that is, X-rays, and disruptions to enteral nutrition and medication. Cohen's Kappa determined pH test reliability. RESULTS: Of 1381 pH tests conducted to confirm NGT position, five (0.3%) correctly identified an NGT displacement, and one (0.07%) failed to identify displacement before use. The reliability of ongoing pH tests using Cohen's Kappa was minimal (0.29). Ongoing pH tests that failed to confirm a correctly positioned NGT led to 31 (22.8%) patients having X-rays, 24 (17.6%) missing >10% of prescribed enteral nutrition and 25 (18.4%) missing a critical medication. CONCLUSION: Ongoing NGT position testing using pH tests did not prevent the use of a displaced tube, and more than one-fifth of patients required X-rays to confirm a correctly position NGT, contributing to missed medications and enteral nutrition. RELEVANCE TO CLINICAL PRACTICE: Caution should be used when confirming ongoing NGT position with a pH test. Future guidelines should balance the risk of using a displaced tube with potential delays to nutrition and/or medication. More research is needed to explore alternative methods of ongoing NGT position testing.

Artificial Intelligence-enabled Chest X-ray Classifies Osteoporosis and Identifies Mortality Risk.

A deep learning model was developed to identify osteoporosis from chest X-ray (CXR) features with high accuracy in internal and external validation. It has significant prognostic implications, identifying individuals at higher risk of all-cause mortality. This Artificial Intelligence (AI)-enabled CXR strategy may function as an early detection screening tool for osteoporosis. The aim of this study was to develop a deep learning model (DLM) to identify osteoporosis via CXR features and investigate the performance and clinical implications. This study collected 48,353 CXRs with the corresponding T score according to Dual energy X-ray Absorptiometry (DXA) from the academic medical center. Among these, 35,633 CXRs were used to identify CXR- Osteoporosis (CXR-OP). Another 12,720 CXRs were used to validate the performance, which was evaluated by the area under the receiver operating characteristic curve (AUC). Furthermore, CXR-OP was tested to assess the long-term risks of mortality, which were evaluated by Kaplan‒Meier survival analysis and the Cox proportional hazards model. The DLM utilizing CXR achieved AUCs of 0.930 and 0.892 during internal and external validation, respectively. The group that underwent DXA with CXR-OP had a higher risk of all-cause mortality (hazard ratio [HR] 2.59, 95% CI: 1.83-3.67), and those classified as CXR-OP in the group without DXA also had higher all-cause mortality (HR: 1.67, 95% CI: 1.61-1.72) in the internal validation set. The external validation set produced similar results. Our DLM uses CXRs for early detection of osteoporosis, aiding physicians to identify those at risk. It has significant prognostic implications, improving life quality and reducing mortality. AI-enabled CXR strategy may serve as a screening tool.

Deep Learning for Pneumothorax Detection on Chest Radiograph: A Diagnostic Test Accuracy Systematic Review and Meta Analysis.

BACKGROUND: Pneumothorax is a common acute presentation in healthcare settings. A chest radiograph (CXR) is often necessary to make the diagnosis, and minimizing the time between presentation and diagnosis is critical to deliver optimal treatment. Deep learning (DL) algorithms have been developed to rapidly identify pathologic findings on various imaging modalities. PURPOSE: The purpose of this systematic review and meta-analysis was to evaluate the overall performance of studies utilizing DL algorithms to detect pneumothorax on CXR. METHODS: A study protocol was created and registered a priori (PROSPERO CRD42023391375). The search strategy included studies published up until January 10, 2023. Inclusion criteria were studies that used adult patients, utilized computer-aided detection of pneumothorax on CXR, dataset was evaluated by a qualified physician, and sufficient data was present to create a 2 × 2 contingency table. Risk of bias was assessed using the QUADAS-2 tool. Bivariate random effects meta-analyses and meta-regression modeling were performed. RESULTS: Twenty-three studies were selected, including 34 011 patients and 34 075 CXRs. The pooled sensitivity and specificity were 87% (95% confidence interval, 81%, 92%) and 95% (95% confidence interval, 92%, 97%), respectively. The study design, use of an institutional/public data set and risk of bias had no significant effect on the sensitivity and specificity of pneumothorax detection. CONCLUSIONS: The relatively high sensitivity and specificity of pneumothorax detection by deep-learning showcases the vast potential for implementation in clinical settings to both augment the workflow of radiologists and assist in more rapid diagnoses and subsequent patient treatment.