Weakly supervised pneumonia localization in chest X-rays using generative adversarial networks.

Introduction: Chest x-ray (CXR) abnormalities after cardiopulmonary resuscitation are common. Mechanisms by which these abnormalities develop are not fully elucidated, but aspiration of secretions and regurgitated gastric contents during prehospital airway management may be an important modifiable cause. Hypothesis: We hypothesized that endotracheal intubation (ETI) is associated with decreased incidence of CXR abnormalities after out-of-hospital cardiac arrest (OHCA), as compared to bag-valve-mask (BVM) or supraglottic airway (SGA) use. Methods: We conducted a retrospective review including resuscitated OHCA patients treated at a single academic medical center from 2010-2015. We included patients that had an initial CXR obtained within 66 hours of arrival. We excluded patients with tracheostomy, patients without positive pressure ventilation on emergency department (ED) arrival, or missing initial airway management data. We classified patients by airway management at the time of ED arrival into three groups: BVM, SGA, and ETI. Board certified thoracic imaging radiologists determined if there was any CXR abnormality, and if the observed abnormality was likely due to aspiration. The incidence of any abnormality and aspiration were compared between groups. A multivariable logistic regression model was used to adjust for baseline clinical characteristics. Results: Of the 766 subjects included in the study, 22 (3%) had BVM, 68 (9%) had SGA, and 676 (88%) had ETI. Most 58% were male, 34% had initial rhythm VF/VT, and 61% had a witnessed arrest. Any abnormality on CXR was identified in 543 (71%) cases, and likely aspiration was observed in 205 (27%) cases. Incidence of CXR abnormality was not significantly different between groups: BVM group 18/22 (82%), reference; SGA group 52/68 (76%), OR 0.75, 95% CI 0.13-4.31; ETI group 473/676 (70%), OR 0.81, 95% CI 0.16-4.01. Incidence of aspiration on CXR was also not different between groups: BVM group 6/22 (27%), reference; SGA group 19/68 (28%), OR 1.04, 95% CI 0.18-6.22; ETI group 180/676 (27%), OR 1.26, 95% CI 0.25-6.32. Conclusion: Prehospital airway management strategy for resuscitated OHCA patients was not associated with a significant difference in the incidence of any abnormality or aspiration on CXR.

To improve the detection of lung abnormalities in chest X-rays by accurately suppressing overlapping bone structures in the lung area. According to literature on missed lung cancer in chest X-rays, such structures are a significant cause of chest-related diagnostic errors. This study presents a deep-learning-based bone suppression method where a residual U-Net model is trained for chest X-rays using data set generated from the single-energy material decomposition (SEMD) technique on CT. Synthetic projection images and soft-tissue selective images were obtained from the CT data set via the SEMD, which were then used as the input and label data of the U-Net network. The trained network was tested on synthetic chest X-rays and two real chest radiographs. Bone-suppressed images of the real chest radiographs obtained by the proposed method were similar to the results from the American Association of Physicists in Medicine lung CT data; pulmonary nodules in the soft-tissue selective images appeared more clearly than in the synthetic projection images. The peak signal-to-noise ratio and structural similarity values measured between the output and the corresponding label images were approximately 17.85 and 0.90, respectively. The proposed method effectively yielded bone-suppressed chest X-ray images, indicating its clinical usefulness, and it can improve the detection of lung abnormalities in chest X-rays. The idea of using SEMD to obtain large amounts of paired images for deep-learning-based bone suppression algorithms is novel.

Recognition of pulmonary involvement in extra-pulmonary tuberculosis (TB) may be an important public health issue, as smear-negative pulmonary TB is responsible for about 17% of new infections. Pulmonary TB can be present despite a normal chest x-ray (CXR), even in human immunodeficiency virus (HIV)-negative patients. In this retrospective clinical audit, we reviewed a case series of HIV-negative patients with extra-pulmonary tuberculosis to identify the proportion with concurrent pulmonary TB despite an unremarkable CXR. Clinical notes, microbiology results and CXR reports were reviewed from consecutive patients treated at Auckland City Hospital for extra-pulmonary TB from January 2007 to July 2010. Of the sample of 103 patients with extra-pulmonary TB, the majority of patients were born in an Asian country (n=70; 68%). The commonest presentation of extra-pulmonary TB was lymphadenopathy (n=51; 50%), followed by pleural (n=24; 23%) and bone (n=6; 6%) disease. Extra-pulmonary TB was diagnosed by biopsy or excision of the extra-pulmonary site in the majority (n=74; 72%), and by sputum testing alone in 26 (25%). The majority had CXR abnormalities (n=76; 74%). In the group with a normal CXR (n=27), 55% (n=15) had sputum cultures performed. In total, 18% (n=5) of patients with extra-pulmonary TB and a normal CXR had pulmonary TB, of whom two were smear positive. In patients with extra-pulmonary TB, sputum testing should be considered to detect concurrent pulmonary TB even if a CXR is normal, especially in immunosuppressed or symptomatic patients. This may aid diagnosis and determine infectivity and consequent public health action.

Cloud removal (CR) methods have been widely studied and discussed to address the issue of cloud occlusion in Earth observation tasks. Existing CR methods heavily rely on image similarity metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) to evaluate the quality of CR results. However, due to factors including rapid changes in landforms and viewpoint differences between cloudy and reference images, image similarity metrics could be ineffective, even misleading. To address these challenges, this study investigates CR by evaluating whether CR algorithms effectively produce information beneficial for downstream tasks. We introduce CUHKCR-EXT, the first very-high-resolution CR dataset explicitly designed for post-CR downstream task performance assessment. Furthermore, we propose DFCFormer, a dynamic filter-based transformer that generates adaptive kernels conditioned on cloud characteristics, enabling more precise recovery across diverse cloud types within a unified framework. In addition, we design a feature alignment loss that enforces consistency between cloud-removed and reference features at the semantic level, which guides the model to retain landform-relevant information crucial for downstream analysis. Using scene classification as a representative downstream task, we conduct extensive experiments and evaluate performance using both image similarity and information loss metrics. The results demonstrate that the proposed method achieves strong performance across all evaluated metrics. More importantly, the improvements lie not only in image similarity but also in the preservation of task-relevant semantics, which enhances the effective quality of output images for downstream applications rather than merely their visual fidelity. The project code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wzy6055/DFCFormer</uri>.

Lung ultrasound characteristics of community‐acquired pneumonia in hospitalized children

To determine predictors associated with positive chest x-ray finding in patients presenting with non-traumatic chest pain in the Emergency Department (ED). Health records, including the final radiology reports of all patients who presented with non-traumatic chest pain and had a chest x-ray performed in an urban Canadian tertiary care ED over four consecutive months were reviewed. Demographic and clinical variables were also extracted. Chest x-ray findings were categorized as normal (either normal or no significant change from previous x-rays) or abnormal. Descriptive statistics were used to describe the data. Multivariable logistic regression was used to determine the association between various predictors and chest x-ray finding (positive/negative). The 330 study patients had the following characteristics: mean age 58±20 years; female 41% (n=134). Patients' chief complaints were only chest pain 75% (n=248), chest pain with shortness of breath 12% (n=41), chest pain with palpitation 4% (n=14), chest pain with other complaints 9% (n=28). Chest x-rays were reported as normal or no acute changes in 81% (n=266) of patients, and abnormal in 19% (n=64) of patients. The most common abnormal chest x-ray diagnoses were congestive heart failure (n=28; 8%) and pneumonia (n=17; 5%). Those with abnormal chest x-ray findings were significantly older (71 versus 55 years; p<0.001), had chest pain with shortness of breath (36% versus 11%; p<0.001), had significant past medical history (39% versus 14%; p<0.001), and were also tachypnoic (31% versus 12%; p<0.001). This study found that patients with non-traumatic chest pain are likely to have a normal chest x-ray if they were young, not tachypnoeic or short of breath, and had no significant past medical history. A larger study is required to confirm these findings.

There is uncertainty in the NHS about which individuals should be offered pre-employment screening by chest X-ray and whether this procedure is of value in the detection of tuberculosis. To provide evidence for practice, pre-employment chest X-ray and tuberculin skin test status were examined retrospectively for employees of a health district. Cases were those with an abnormal chest X-ray; referents were a sample of the remainder with a normal chest X-ray. The majority of the population had positive tuberculin skin tests and there was no difference between cases (58 positive out of 68) and referents (170 positive out of 212). Most of the X-ray abnormalities were trivial; four findings were thought significant, but would have caused no problems if undetected and none of these findings was related to tuberculosis. We conclude that in new employees in Hampstead health district, X-ray abnormalities are rare and not predicted by testing tuberculin skin reactivity; neither procedure is justified routinely as a means of screening for tuberculosis. The situation in districts with a high incidence of tuberculosis needs to be investigated.

The aim of this study was to analyse the use of the chest radiograph (CXR) as the first-line investigation in primary care patients with suspected lung cancer. Of 16,945 primary care referral CXRs (June 2018 to May 2019), 1,488 were referred for suspected lung cancer. CXRs were coded as follows: CX1, normal but a CT scan is recommended to exclude malignancy; CX2, alternative diagnosis; or CX3, suspicious for cancer. Kaplan-Meier survival analysis was undertaken by stratifying patients according to their CX code. In the study period, there were 101 lung cancer diagnoses via a primary care CXR pathway. Only 10% of patients with a normal CXR (CX1) underwent subsequent CT and there was a significant delay in lung cancer diagnosis in these patients (p < 0.001). Lung cancer was diagnosed at an advanced stage in 50% of CX1 patients, 38% of CX2 patients and 57% of CX3 patients (p = 0.26). There was no survival difference between CX codes (p = 0.42). Chest radiography in the investigation of patients with suspected lung cancer may be harmful. This strategy may falsely reassure in the case of a normal CXR and prioritises resources to advanced disease. • Half of all lung cancer diagnoses in a 1-year period are first investigated with a chest X-ray. • A normal chest X-ray report leads to a significant delay in the diagnosis of lung cancer. • The majority of patients with a normal or abnormal chest X-ray have advanced disease at diagnosis and there is no difference in survival outcomes based on the chest X-ray findings.

Background: Chest X-rays are rapidly gaining prominence as a prevalent diagnostic tool, as recognized by the World Health Organization (WHO). However, interpreting chest X-rays can be demanding and time-consuming, even for experienced radiologists, leading to potential misinterpretations and delays in treatment. Method: The purpose of this research is the development of a RadAI model. The RadAI model can accurately detect four types of lung abnormalities in chest X-rays and generate a report on each identified abnormality. Moreover, deep learning algorithms, particularly convolutional neural networks (CNNs), have demonstrated remarkable potential in automating medical image analysis, including chest X-rays. This work addresses the challenge of chest X-ray interpretation by fine tuning the following three advanced deep learning models: Feature-selective and Spatial Receptive Fields Network (FSRFNet50), ResNext50, and ResNet50. These models are compared based on accuracy, precision, recall, and F1-score. Results: The outstanding performance of RadAI shows its potential to assist radiologists to interpret the detected chest abnormalities accurately. Conclusions: RadAI is beneficial in enhancing the accuracy and efficiency of chest X-ray interpretation, ultimately supporting the timely and reliable diagnosis of lung abnormalities.

BackgroundScreening for latent tuberculosis (LTB) using chest x-ray (CXR) and interferon-gamma release assay (IGRA) is standard in patients starting on biologic and immunosuppressive therapies. Our hypothesis is that the CXR...

e19713 Background: The aim of this study was to determine the utility of thoracic high resolution CT scan (HRCT) in the characterization of lung infiltrates for the early diagnosis of invasive pulmonary aspergillosis (IPA) in febrile neutropenic patients. Methods: We included hematology patients (acute leukemia/aplastic anemia) with febrile neutropenia in this study. We took chest x-rays at admission and at onset of fever or development of respiratory signs/symptoms. We did HRCT scan of the chest when fever had lasted for more than 4 days of antibiotic therapy or if the patient had signs/symptoms suggestive of pulmonary involvement or when chest X- ray showed abnormalities. Patterns of parenchymal infiltrates and the presence of nodular lesions, halo sign, cavitation, air crescent sign and fungal ball were noted. IPA was classified as proven, probable or suspected as per EORTC case definitions [excluding radiological criteria]. Calculation of sensitivity and specificity of HRCT in diagnosing IPA was based on these groups. Results: We studied 76 febrile episodes. Chest x-ray abnormalities were seen in 49 (61%) patients.HRCT scan of the chest showed abnormalities in 63 (84%) patients. Normal chest x-ray with abnormal CT was seen in 19(24%) patients. Typical CT findings included nodules in 24 (43%), nodules with halo sign in 19 (34%), segmental consolidation in 34 (61%), cavitation/air crescent sign in 9 (16%), fungal ball in 4(7%), pleural effusion in 11(20%) and hilar/paratracheal lymphadenopathy in 7 (13%). Multiple abnormalities were seen in 36 (47) % of cases. There were 32 cases of suspected IPA and 1 probable IPA. The overall sensitivity and specificity of HRCT for diagnosing IPA was 96% and 65% respectively. The sensitivity and specificity of the halo sign was 39% and 81% and that of the air crescent sign was 27% and 94%. Some of the larger nodules and consolidations in patients with suspected IPA showed a central hypodensity on non-contrast CT (‘hypodense sign’). These radiological findings were useful in the early institution of anti-fungal therapy in these patients. Conclusions: High resolution CT scan is superior to conventional chest x-ray for the early diagnosis of IPA in neutropenic patients.

Assessment of the prevalence of pulmonary involvement in cases with extrapulmonary tuberculosis

Performance of routine Chest X-rays (CXRs) in asymptomatic individuals to assess hyperbaric exposure risk is controversial. The radiation risk may overshadow the low yield in many settings. However, the yield may be higher in certain settings, such as tuberculosis-endemic countries. We evaluated the utility of routine CXR in diving and submarine medical examinations in South Africa. Records of 2,777 CXRs during 3,568 fitness examinations of 894 divers and submariners spanning 31 years were reviewed to determine the incidence of CXR abnormality. Associated factors were evaluated using odds ratios and a binomial logistic regression model, with a Kaplan-Meier plot to describe the duration of service until first abnormal CXR. An abnormal CXR was reported in 1.1% per person year of service, yielding a cumulative incidence of 6.5% (58/894) of the study participants. Only four individuals had a clinical indication for the CXR in their medical history. A range of potential pathologies were seen, of which 15.5% were declared disqualifying and the rest (84.5%) were treated, or further investigation showed that the person could be declared fit. In South Africa, a routine CXR has a role to play in detecting abnormalities that are incompatible with pressure exposures. The highest number of abnormalities were found during the initial examinations and in individuals with long service records. Only four individuals had a clinical indication for their CXR during the 31-year span of our study. Similar studies should be performed to make recommendations in other countries and settings.

Although the range of immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is variable, cytokine storm is observed in a subset of symptomatic individuals. To further understand the disease pathogenesis and, consequently, to develop an additional tool for clinicians to evaluate patients for presumptive intervention, we sought to compare plasma cytokine levels between a range of donor and patient samples grouped by a COVID-19 Severity Score (CSS) based on the need for hospitalization and oxygen requirement. Here we utilize a mutual information algorithm that classifies the information gain for CSS prediction provided by cytokine expression levels and clinical variables. Using this methodology, we found that a small number of clinical and cytokine expression variables are predictive of presenting COVID-19 disease severity, raising questions about the mechanism by which COVID-19 creates severe illness. The variables that were the most predictive of CSS included clinical variables such as age and abnormal chest x-ray as well as cytokines such as macrophage colony-stimulating factor, interferon-inducible protein 10, and interleukin-1 receptor antagonist. Our results suggest that SARS-CoV-2 infection causes a plethora of changes in cytokine profiles and that particularly in severely ill patients, these changes are consistent with the presence of macrophage activation syndrome and could furthermore be used as a biomarker to predict disease severity.

The purpose of the study was to investigate to what extent marine engineers who are exposed to mineral oil mist in the engine rooms of ships have lung function disturbances and chest X-ray abnormalities. A previous study examined a cohort of 492 seamen from which we received 169 full-scale (40 x 40 cm) chest radiographs. Among these films were 68 from marine engineers and 101 from seamen who had never worked as engineers (controls). Spirometric data were obtained from 44 engineers and from 71 of the controls. The chest X-rays were classified independently by two certified B-readers, using the ILO classification. Only 7% of the seamen had parenchymal or pleural changes. The only statistically significant difference between the two groups for the chest X-ray findings was for all comments made by the ILO readers which were termed "any symbol" (9.3% for the engineers and 0.6% for the controls; RR = 14.6 (95% CI 1.1-75.5)) and the FEV% (FEV1/FVC x 100) for the spirometric values (82.8 (SD = 10.8) for the engineers and 86.6 (SD = 8.0) for the controls; P = 0.03). The results of the study indicate a slight lung function impairment and an increase in chest X-ray abnormalities, other than small opacities, among the marine engineers.