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Background Typical signs of heart failure (HF), like increased cardiothoracic ratio (CTR) and pleural effusion, can be seen on X‐ray. Artificial Intelligence (AI) can help in the early and quicker diagnosis of HF. Objectives The study's goal was to demonstrate that the AI interpretation of chest X‐rays can assist the clinician in diagnosing HF. Methods Patients older than 45 years were included in the study. The study analyzed 10 100 deidentified outpatient chest X‐rays by AI algorithm. The AI‐generated report was later verified by an independent radiologist. Patients with CTR > 0.5 and pleural effusion were marked as potential HF. Flagged patients underwent confirmatory tests, and those labeled as negative also underwent further investigations to rule out HF. Results Out of 10 100, the AI algorithm detected 183 (1.8%) patients with increased CTR and pleural effusion on chest X‐rays. One hundred and six out of 183 underwent diagnostic tests. Eighty‐two (77%) out of 106 were diagnosed with HF according to current guidelines. From the remaining 9917 patients, 106 patients were randomly selected. Nine (8%) out of them were diagnosed with HF. The positive predictive value of AI for diagnosing HF is 77%, and the negative predictive value is 91%. More than half (54.9%) of newly diagnosed patients had HF with preserved ejection fraction. Conclusion HF is a risky condition with nonspecific symptoms that are difficult to diagnose, especially in the early stages. Using AI assistance for X‐ray interpretation can be helpful for early diagnosis of HF especially HF with preserved ejection fraction. Artificial Intelligence (AI) may help in the early and quicker diagnosis of heart failure (HF). Patients with cardiothoracic ratio (CTR) >0.5 and pleural effusion on chest X‐rays were marked as potential HF by AI. Potential HF patients underwent diagnostic tests such as natriuretic peptides and echocardiography. AI algorithm detected 183 (1.8%) patients with increased CTR and pleural effusion on chest X‐rays of 10,100 patients. 77% of AI‐positive patients were diagnosed with HF. Eight percent of AI‐negative patients were diagnosed with HF. The positive predictive value of AI for diagnosing HF is 77%, and the negative predictive value is 91%. The majority of newly diagnosed HF patients had non‐reduced ejection fraction.

Tuberculosis (TB) is the leading cause of avoidable deaths from an infectious disease globally and a large of number of people who develop TB each year remain undiagnosed. Active case-finding has been recommended by the World Health Organization to bridge the case-detection gap for TB in high burden countries. However, concerns remain regarding their yield and cost-effectiveness. Data from mobile chest X-ray (CXR) supported active case-finding community camps conducted in Karachi, Pakistan from July 2018 to March 2020 was retrospectively analyzed. Frequency analysis was carried out at the camp-level and outcomes of interest for the spatial analyses were mycobacterium TB positivity (MTB+) and X-ray abnormality rates. The Global Moran’s I statistic was used to test for spatial autocorrelation for MTB+ and abnormal X-rays within Union Councils (UCs) in Karachi. A total of 1161 (78.1%) camps yielded no MTB+ cases, 246 (16.5%) camps yielded 1 MTB+, 52 (3.5%) camps yielded 2 MTB+ and 27 (1.8%) yielded 3 or more MTB+. A total of 79 (5.3%) camps accounted for 193 (44.0%) of MTB+ cases detected. Statistically significant clustering for MTB positivity (Global Moran’s I: 0.09) and abnormal chest X-rays (Global Moran’s I: 0.36) rates was identified within UCs in Karachi. Clustering of UCs with high MTB positivity were identified in Karachi West district. Statistically significant spatial variation was identified in yield of bacteriologically positive TB cases and in abnormal CXR through active case-finding in Karachi. Cost-effectiveness of active case-finding programs can be improved by identifying and focusing interventions in hotspots and avoiding locations with no known TB cases reported through routine surveillance.

The utility of chest x-ray examination (CXR) in mandatory annual health examinations for occupational health is debatable in Japan. This study aimed to provide basic data to consider future policies for mandatory annual health examinations in the workplace. A nationwide descriptive survey was performed to determine the rate of detection of tuberculosis, lung cancer, and other diseases through CXR in organizations associated with National Federation of Industrial Health Association. The rate of finding on CXR conducted during annual health examinations in FY2016 was evaluated. Data regarding diagnosis based on follow-up examination findings were obtained and compared with the national statistics. In addition, CXR findings were compared with the results of low-dose lung computed tomography performed at the Hitachi Health Care Center. From 121 surveyed institutions, 88 institutions with 8,669,403 workers were included. For all ages, 1.0% of examinees required follow-up examination. Among 4,764,985 workers with diagnosis data, the tuberculosis detection rate was 1.8–5.3 per 100,000 persons. For Lung cancer, 3,688,396 workers were surveyed, and 334 positive cases were detected. The lung cancer detection rate using CXR was 9.1–24.4 per 100,000 persons. From 164 cases with information regarding the clinical stage, 72 (43.9%) had Stage I lung cancer. From 40,045 workers who underwent low-dose computed tomography multiple times, 31 lung cancer cases, all with Stage I disease, were detected (detection rate: 77.4 per 100,000 persons). Our findings suggest that CXR plays a little role in the detection of active tuberculosis. With regard to LC screening, the detection rate of LC by CXR was lower, approximately 50%, than the expected rate (41.0 per 100,000 persons) of LC morbidity based on the age–sex distribution of this study population. However, the role of CXR for LC screening cannot be mentioned based on this result, because assessment of mortality reduction is essential to evaluate the role.

Cardiovascular diseases are among the major health problems that are likely to benefit from promising developments in quantum machine learning for medical imaging. The chest X-ray (CXR), a widely used modality, can reveal cardiomegaly, even when performed primarily for a non-cardiological indication. Based on pre-trained DenseNet-121, we designed hybrid classical–quantum (CQ) transfer learning models to detect cardiomegaly in CXRs. Using Qiskit and PennyLane, we integrated a parameterized quantum circuit into a classic network implemented in PyTorch. We mined the CheXpert public repository to create a balanced dataset with 2436 posteroanterior CXRs from different patients distributed between cardiomegaly and the control. Using k-fold cross-validation, the CQ models were trained using a state vector simulator. The normalized global effective dimension allowed us to compare the trainability in the CQ models run on Qiskit. For prediction, ROC AUC scores up to 0.93 and accuracies up to 0.87 were achieved for several CQ models, rivaling the classical–classical (CC) model used as a reference. A trustworthy Grad-CAM++ heatmap with a hot zone covering the heart was visualized more often with the QC option than that with the CC option (94% vs. 61%, p < 0.001), which may boost the rate of acceptance by health professionals.

Background and Aims The International Commission on Radiological Protection emphasizes dose optimization for pediatric patients due to their higher radiosensitivity. Establishing diagnostic reference levels (DRLs) supports this goal during radiological examinations. This study aimed to establish pediatric diagnostic reference levels (PiDRLs) for anteroposterior (AP) chest X‐rays across four hospitals in the Northern Cape Province, South Africa. Methods PiDRLs were calculated for 375 pediatric patients (birth to 12 years) categorized by weight bands and age groups. Image quality was assessed using an image quality assessment checklist. DRLs were determined as the 75th percentile of the dose distribution for fixed X‐ray units. An image quality assessment was performed to establish if the dose levels consistently produced images of acceptable diagnostic quality. Results Mean PiDRLs for weight groups < 5 kg, 5– < 15 kg, 15– < 30 kg, 30– < 50 kg were 0.2 mGy, 0.2 mGy, 0.2 mGy, and 0.3 mGy, respectively. Corresponding values for age groups < 1 year, 1– < 5 years, 5– < 10 years, and 10– < 12 years were 0.2 mGy, 0.2 mGy, 0.3 mGy, and 0.3 mGy, respectively. Image quality was consistently high, with the 75th percentile slightly exceeding international benchmarks. The image quality assessment confirmed that the dose levels produced images of acceptable image quality. Conclusion PiDRLs match select international benchmarks, offering a baseline for South African optimization. This study contributes valuable regional data to support radiation safety and dose optimization in pediatric medical imaging in South Africa.

Background Computer‐aided detection (CAD) software has been introduced to automatically interpret digital chest X‐rays. This study aimed to evaluate the performance of CAD software (JF CXR‐1 v3.0, which was developed by a domestic Hi‐tech enterprise) in tuberculosis (TB) case finding in China. Methods In 2019, we conducted an internal evaluation of the performance of JF CXR‐1 v3.0 by reading standard images annotated by a panel of experts. In 2020, using the reading results of chest X‐rays by a panel of experts as the reference standard, we conducted an on‐site prospective study to evaluate the performance of JF CXR‐1 v3.0 and local radiologists in TB case finding in 13 township health centers in Zhongmu County, Henan Province. Results Internal assessment results based on 277 standard images showed that JF CXR‐1 v3.0 had a sensitivity of 85.94% (95% confidence interval [CI]: 77.42%, 94.45%) and a specificity of 74.65% (95% CI: 68.81%, 80.49%) to distinguish active TB from other imaging conditions. In the on‐site evaluation phase, images from 3705 outpatients who underwent chest X‐ray detection were read by JF CXR‐1 v3.0 and local radiologists in parallel. The imaging diagnosis of local radiologists for active TB had a sensitivity of 32.89% (95% CI: 22.33%, 43.46%) and a specificity of 99.28% (95% CI: 99.01%, 99.56%), while JF CXR‐1 v3.0 showed a significantly higher sensitivity of 92.11% (95% CI: 86.04%, 98.17%) (p < 0.05) and maintained high specificity at 94.54% (95% CI: 93.81%, 95.28%). Conclusions CAD software could play a positive role in improving the TB case finding capability of township health centers. Flow chart of the study implementation. Summary CAD software might be applied to help human readers improve overall TB diagnosis in primary health center, especially in resource‐limited areas with high TB burden. Compared with local radiologists, the diagnosis of JF CXR‐1 v3.0 for active TB had a significantly higher sensitivity and a similar specificity.

The modern scientific world continuously endeavors to battle and devise solutions for newly arising pandemics. One such pandemic which has turned the world’s accustomed routine upside down is COVID-19: it has devastated the world economy and destroyed around 45 million lives, globally. Governments and scientists have been on the front line, striving towards the diagnosis and engineering of a vaccination for the said virus. COVID-19 can be diagnosed using artificial intelligence more accurately than traditional methods using chest X-rays. This research involves an evaluation of the performance of deep learning models for COVID-19 diagnosis using chest X-ray images from a dataset containing the largest number of COVID-19 images ever used in the literature, according to the best of the authors’ knowledge. The size of the utilized dataset is about 4.25 times the maximum COVID-19 chest X-ray image dataset used in the explored literature. Further, a CNN model was developed, named the Custom-Model in this study, for evaluation against, and comparison to, the state-of-the-art deep learning models. The intention was not to develop a new high-performing deep learning model, but rather to evaluate the performance of deep learning models on a larger COVID-19 chest X-ray image dataset. Moreover, Xception- and MobilNetV2- based models were also used for evaluation purposes. The criteria for evaluation were based on accuracy, precision, recall, F1 score, ROC curves, AUC, confusion matrix, and macro and weighted averages. Among the deployed models, Xception was the top performer in terms of precision and accuracy, while the MobileNetV2-based model could detect slightly more COVID-19 cases than Xception, and showed slightly fewer false negatives, while giving far more false positives than the other models. Also, the custom CNN model exceeds the MobileNetV2 model in terms of precision. The best accuracy, precision, recall, and F1 score out of these three models were 94.2%, 99%, 95%, and 97%, respectively, as shown by the Xception model. Finally, it was found that the overall accuracy in the current evaluation was curtailed by approximately 2% compared with the average accuracy of previous work on multi-class classification, while a very high precision value was observed, which is of high scientific value.

Background A chest X-ray is a standard imaging procedure in the diagnostic work-up of patients suspected of having non-traumatic pulmonary disease. Compared to a chest X-ray, an ultra-low-dose (ULD) chest computed tomography (CT) scan provides substantially more detailed information on pulmonary conditions. To what extent this translates into an improvement in patient outcomes and health care efficiency is yet unknown. The OPTimal IMAging strategy in patients suspected of non-traumatic pulmonary disease at the emergency department: chest X-ray or ultra-low-dose chest CT (OPTIMACT) study is a multicenter, pragmatic, non-inferiority randomized controlled trial designed to evaluate replacement of chest X-ray by ULD chest CT in the diagnostic work-up of such patients, in terms of patient-related health outcomes and costs. During randomly assigned periods of 1 calendar month, either conventional chest X-ray or ULD chest CT scan was used as the imaging strategy. This paper presents in detail the statistical analysis plan of the OPTIMACT trial, developed prior to data analysis. Methods/results Functional health at 28 days is the primary clinical outcome. Functional health at 28 days is measured by the physical component summary scale of the Short Form (SF)-12 questionnaire version 1. Secondary outcomes are mental health (mental component summary scale of the SF-12), length of hospital stay, mortality within 28 days, quality-adjusted life year equivalent during the first 28 days (derived from the EuroQol five-dimension, five-level instrument), correct diagnoses at emergency department discharge as compared to the final post hoc diagnosis at day 28, number of patients in follow-up because of incidental findings on chest X-ray or ULD chest CT, and health care costs. Conclusions After this pragmatic trial we will have precise estimates of the effectiveness of replacing chest X-ray with ULD chest CT in terms of patient-related health outcomes and costs. Trial registration Netherlands National Trial Register: NTR6163 . Registered on 6 December 2016.

Although pleural thickening is a common finding on routine chest X-rays, its radiological and clinical features remain poorly characterized. Our investigation of 28,727 chest X-rays obtained from annual health examinations confirmed that pleural thickening was the most common abnormal radiological finding. In most cases (92.2%), pleural thickening involved the apex of the lung, particularly on the right side; thus, it was defined as a pulmonary apical cap. Pleural thickening was more common in males than in females and in current smokers or ex-smokers than in never smokers. The prevalence increased with age, ranging from 1.8% in teenagers to 9.8% in adults aged 60 years and older. Moreover, pleural thickening was clearly associated with greater height and lower body weight and body mass index, suggesting that a tall, thin body shape may predispose to pleural thickening. These observations allowed us to speculate about the causative mechanisms of pleural thickening that are attributable to disproportionate perfusion, ventilation, or mechanical forces in the lungs.

Advanced diagnostic methods are necessary for the prompt and reliable identification of tuberculosis (TB), which continues to be a worldwide health problem. Globally, there were projected to be 10 million new cases of tuberculosis in 2021, of which 9.8 million affected adults and 0.2 million children. About 15% of fatalities worldwide are attributable to tuberculosis (1.5 million deaths for every 10 million infections). To create a reliable model for tuberculosis (TB) identification using chest X‐ray pictures, we use deep learning approaches in this work, namely Convolutional Neural Networks (CNNs) and a combination of transfer learning and hyperparameter tuning. The dataset provides a varied selection of 3500 normal and 700 TB‐infected patients. It consists of 4200 photos that were obtained from the “Tuberculosis (TB) Chest X‐ray Database” on Kaggle. By utilizing the benefits of a trained model, the suggested methodological approach incorporates transfer learning. To maximize the performance of the suggested model, hyperparameter adjustment is also used. Using the VGG19 pre‐trained neural network, the model design is based on the concepts of transfer learning. The architecture makes use of task‐specific layers, regularization methods, and deliberate layer freezing to enable sophisticated categorization. Training and assessment stages demonstrate encouraging outcomes, with an accuracy of almost 98% attained on a different test dataset. A more thorough examination highlights the need for caution when interpreting high accuracy, nevertheless, by highlighting possible difficulties. Optimized System for Tuberculosis Classification.