Search Results (58)

Background: Secukinumab is a fully human monoclonal antibody that targets interleukin (IL)-17A and is used to treat axial spondyloarthritis (axSpA) and psoriatic arthritis (PsA). Treating axSpA and PsA can be challenging in patients with comorbidities. In this multicenter retrospective study, we aimed to evaluate the efficacy and safety of secukinumab treatment in patients with axSpA and PsA who had a history of tuberculosis, multiple sclerosis (MS), or congestive heart failure (CHF). Methods: The study included 44 patients with a diagnosis of axSpA and PsA and a history of tuberculosis, MS, or CHF who received secukinumab treatment at 13 centers in our country. Erythrocyte sedimentation rate, C-reactive protein (CRP), Bath Ankylosing Spondylitis Disease Activity Index, Ankylosing Spondylitis Disease Activity Score CRP, visual analog scale, and Disease Activity Score-28 CRP markers at months 0, 3, and 12 of secukinumab treatment were analyzed. Alongside this, tuberculosis, MS, and CHF were evaluated at follow-up using clinical assessments and imaging methods such as chest radiographs, brain magnetic resonance, and echocardiography. Results: A statistically significant improvement in inflammatory markers and disease activity scores was observed in patients treated with secukinumab. There was no reactivation in patients with a history of tuberculosis. In most MS patients, the disease was stable, while clinical and radiological improvement was observed in one patient. No worsening of CHF stage was observed in patients with a history of CHF. Conclusions: With regular clinical monitoring, secukinumab may be an effective and safe treatment option for axSpA and PsA patients with a history of tuberculosis, MS, or CHF. Full article

Background: Examining chest radiograph images (CXR) is an intricate and time-consuming process, sometimes requiring the identification of many anomalies at the same time. Lung segmentation is key to overcoming this challenge through different deep learning (DL) techniques. Many researchers are working to improve the performance and efficiency of lung segmentation models. This article presents a DL-based approach to accurately identify the lung mask region in CXR images to assist radiologists in recognizing early signs of high-risk lung diseases. Methods: This paper proposes a novel technique, Lightweight Residual U-Net, combining the strengths of the convolutional block attention module (CBAM), the Atrous Spatial Pyramid Pooling (ASPP) block, and the attention module, which consists of only 3.24 million trainable parameters. Furthermore, the proposed model has been trained using both the RELU and LeakyReLU activation functions, with LeakyReLU yielding superior performance. The study indicates that the Dice loss function is more effective in achieving better results. Results: The proposed model is evaluated on three benchmark datasets: JSRT, SZ, and MC, achieving a Dice score of 98.72%, 97.49%, and 99.08%, respectively, outperforming the state-of-the-art models. Conclusions: Using the capabilities of DL and cutting-edge attention processes, the proposed model improves current efforts to enhance lung segmentation for the early identification of many serious lung diseases. Full article

This study introduces the Pixel-Level Interpretability (PLI) model, a novel framework designed to address critical limitations in medical imaging diagnostics by enhancing model transparency and diagnostic accuracy. The primary objective is to evaluate PLI’s performance against Gradient-Weighted Class Activation Mapping (Grad-CAM) and achieve fine-grained interpretability and improved localization precision. The methodology leverages the VGG19 convolutional neural network architecture and utilizes three publicly available COVID-19 chest radiograph datasets, consisting of over 1000 labeled images, which were preprocessed through resizing, normalization, and augmentation to ensure robustness and generalizability. The experiments focused on key performance metrics, including interpretability, structural similarity (SSIM), diagnostic precision, mean squared error (MSE), and computational efficiency. The results demonstrate that PLI significantly outperforms Grad-CAM in all measured dimensions. PLI produced detailed pixel-level heatmaps with higher SSIM scores, reduced MSE, and faster inference times, showcasing its ability to provide granular insights into localized diagnostic features while maintaining computational efficiency. In contrast, Grad-CAM’s explanations often lack the granularity required for clinical reliability. By integrating fuzzy logic to enhance visual and numerical explanations, PLI can deliver interpretable outputs that align with clinical expectations, enabling practitioners to make informed decisions with higher confidence. This work establishes PLI as a robust tool for bridging gaps in AI model transparency and clinical usability. By addressing the challenges of interpretability and accuracy simultaneously, PLI contributes to advancing the integration of AI in healthcare and sets a foundation for broader applications in other high-stake domains. Full article

Lung cancer, the second most common type of cancer worldwide, presents significant health challenges. Detecting this disease early is essential for improving patient outcomes and simplifying treatment. In this study, we propose a hybrid framework that combines deep learning (DL) with quantum computing to enhance the accuracy of lung cancer detection using chest radiographs (CXR) and computerized tomography (CT) images. Our system utilizes pre-trained models for feature extraction and quantum circuits for classification, achieving state-of-the-art performance in various metrics. Not only does our system achieve an overall accuracy of 92.12%, it also excels in other crucial performance measures, such as sensitivity (94%), specificity (90%), F1-score (93%), and precision (92%). These results demonstrate that our hybrid approach can more accurately identify lung cancer signatures compared to traditional methods. Moreover, the incorporation of quantum computing enhances processing speed and scalability, making our system a promising tool for early lung cancer screening and diagnosis. By leveraging the strengths of quantum computing, our approach surpasses traditional methods in terms of speed, accuracy, and efficiency. This study highlights the potential of hybrid computational technologies to transform early cancer detection, paving the way for wider clinical applications and improved patient care outcomes. Full article

Objectives: The aim of this study was to examine the impact of the pentraxin 3 (PTX3) serum level and angiotensin-converting enzyme (ACE) gene insertion/deletion (I/D) polymorphism on the severity of radiographic pulmonary infiltrates and the clinical outcomes of COVID-19. Methods: The severity of COVID-19 pulmonary infiltrates was evaluated within a week of admission by analyzing chest X-rays (CXR) using the modified Brixia (MBrixa) scoring system. The insertion (I)/deletion (D) polymorphism of the ACE gene and the serum levels of PTX3 were determined for all patients included in the study. Results: This study included 80 patients. Using a cut-off serum level of PTX3 ≥ 2.765 ng/mL, the ROC analysis (AUC 0.871, 95% CI 0.787–0.954, p < 0.001) showed a sensitivity of 85.7% and specificity of 78.8% in predicting severe MBrixa scores. Compared to ACE I/I polymorphism, D/D polymorphism significantly increased the risk of severe CXR infiltrates, OR 7.7 (95% CI: 1.9–30.1), and p = 0.002. Significant independent predictors of severe CXR infiltrates include hypertension (OR 7.71), PTX3 (OR 1.20), and ACE D/D polymorphism (OR 18.72). Hypertension (OR 6.91), PTX3 (OR 1.47), and ACE I/I polymorphism (OR 0.09) are significant predictors of poor outcomes. Conclusion: PTX3 and ACE D/D polymorphism are significant predictors of the severity of COVID-19 pneumonia. PTX3 is a significant predictor of death. Full article

Interstitial lung disease (ILD) is characterized by progressive pathological changes that require timely and accurate diagnosis. The early detection and progression assessment of ILD are important for effective management. This study introduces a novel quantitative evaluation method utilizing chest radiographs to analyze pixel-wise changes in ILD. Using a weakly supervised learning framework, the approach incorporates the contrastive unpaired translation model and a newly developed ILD extent scoring algorithm for more precise and objective quantification of disease changes than conventional visual assessments. The ILD extent score calculated through this method demonstrated a classification accuracy of 92.98% between ILD and normal classes. Additionally, using an ILD follow-up dataset for interval change analysis, this method assessed disease progression with an accuracy of 85.29%. These findings validate the reliability of the ILD extent score as a tool for ILD monitoring. The results of this study suggest that the proposed quantitative method may improve the monitoring and management of ILD. Full article

Deep learning has recently demonstrated the ability to predict long-term patient risk and its stratification when trained on imaging data such as chest radiographs. However, existing methods formulate estimating patient risk as a binary classification, typically ignoring or limiting the use of temporal information, and not accounting for the loss of patient follow-up, which reduces the fidelity of estimation and limits the prediction to a certain time horizon. In this paper, we demonstrate that deep survival and time-to-event prediction models can outperform binary classifiers at predicting mortality and risk of adverse health events. In our study, deep survival models were trained to predict risk scores from chest radiographs and patient demographic information in the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial (25,433 patient data points used in this paper) for 2-, 5-, and 10-year time horizons. Binary classification models that predict mortality at these time horizons were built as baselines. Compared to the considered alternative, deep survival models improve the Brier score (5-year: 0.0455 [95% CI, 0.0427–0.0482] vs. 0.0555 [95% CI, (0.0535–0.0575)], p < 0.05) and expected calibration error (ECE) (5-year: 0.0110 [95% CI, 0.0080–0.0141] vs. 0.0747 [95% CI, 0.0718–0.0776], p < 0.05) for those fixed time horizons and are able to generate predictions for any time horizon, without the need to retrain the models. Our study suggests that deep survival analysis tools can outperform binary classification in terms of both discriminative performance and calibration, offering a potentially plausible solution for forecasting risk in clinical practice. Full article

Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. The early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings poses challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we develop a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures the localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieves the highest performance with accuracy, sensitivity, specificity, precision, F1-score, and combined AUC of 0.93 ± 0.04, 0.94 ± 0.04, 0.93 ± 0.08, 0.95 ± 0.05, 0.94 ± 0.04, and 0.97, respectively, in a five-fold cross-validation study with pulmonary sarcoidosis (n = 126) and LCa (n = 93) cases. A detailed ablation study showing the impact of CNN + ViT compared to CNN or ViT alone, and CT + radiomics input, compared to CT or radiomics alone, is also presented in this work. Overall, the AI model developed in this work offers promising potential for triaging the pulmonary sarcoidosis patients for timely diagnosis and treatment from chest CT. Full article

Background: Interstitial lung disease (ILD) is a complication in patients with systemic sclerosis (SSc). Accurate strategies to identify its presence in early phases are essential. We conducted the study aiming to determine the validity of ultrasound (US) in detecting subclinical ILD in SSc, and to ascertain its potential in determining the disease progression. Methods: 133 patients without respiratory symptoms and 133 healthy controls were included. Borg scale, Rodnan skin score (RSS), auscultation, chest radiographs, and respiratory function tests (RFT) were performed. A rheumatologist performed the lung US. High-resolution CT (HRCT) was also performed. The patients were followed every 12 weeks for 48 weeks. Results: A total of 79 of 133 patients (59.4%) showed US signs of ILD in contrast to healthy controls (4.8%) (p = 0.0001). Anti-centromere antibodies (p = 0.005) and RSS (p = 0.004) showed an association with ILD. A positive correlation was demonstrated between the US and HRCT findings (p = 0.001). The sensitivity and specificity of US in detecting ILD were 91.2% and 88.6%, respectively. In the follow-up, a total of 30 patients out of 79 (37.9%) who demonstrated US signs of ILD at baseline, showed changes in the ILD score by US. Conclusions: US showed a high prevalence of subclinical ILD in SSc patients. It proved to be a valid, reliable, and feasible tool to detect ILD in SSc and to monitor disease progression. Full article

This research addresses the respiratory distress syndrome (RDS) in preterm newborns caused by insufficient surfactant synthesis, which can lead to serious complications, including pneumothorax, pulmonary hypertension, and pulmonary hemorrhage, increasing the risk of a fatal outcome. By analyzing chest radiographs and blood gases, we specifically focus on the significant contributions of these parameters to the diagnosis and analysis of the recovery of patients with RDS. The study involved 32 preterm newborns, and the analysis of gas parameters before and after the administration of surfactants and inhalation corticosteroid therapy revealed statistically significant changes in values of parameters such as FiO₂, pH, pCO₂, HCO₃, and BE (Sig. < 0.05), while the pO₂ parameter showed a potential change (Sig. = 0.061). Parallel to this, the research emphasizes the development of a lung segmentation algorithm implemented in the MATLAB programming environment. The key steps of the algorithm include preprocessing, segmentation, and visualization for a more detailed understanding of the recovery dynamics after RDS. These algorithms have achieved promising results, with a global accuracy of 0.93 ± 0.06, precision of 0.81 ± 0.16, and an F-score of 0.82 ± 0.14. These results highlight the potential application of algorithms in the analysis and monitoring of recovery in newborns with RDS, also underscoring the need for further development of software solutions in medicine, particularly in neonatology, to enhance the diagnosis and treatment of preterm newborns with respiratory distress syndrome. Full article

In this study, we present an innovative approach that harnesses deep neural networks to simulate respiratory lung motion and extract local functional information from single-phase chest X-rays, thus providing valuable auxiliary data for early diagnosis of lung cancer. A novel radiograph motion simulation (RMS) network was developed by combining a U-Net and a long short-term memory (LSTM) network for image generation and sequential prediction. By utilizing a spatial transformer network to deform input images, our proposed network ensures accurate image generation. We conducted both qualitative and quantitative assessments to evaluate the effectiveness and accuracy of our proposed network. The simulated respiratory motion closely aligns with pulmonary biomechanics and reveals enhanced details of pulmonary diseases. The proposed network demonstrates precise prediction of respiratory motion in the test cases, achieving remarkable average Dice scores exceeding 0.96 across all phases. The maximum variation in lung length prediction was observed during the end-exhale phase, with average deviation of 4.76 mm (±6.64) for the left lung and 4.77 mm (±7.00) for the right lung. This research validates the feasibility of generating patient-specific respiratory motion profiles from single-phase chest radiographs. Full article

Background: Influenza virus infection leads to acute pulmonary injury and acute respiratory distress syndrome (ARDS). The Radiographic Assessment of Lung Edema (RALE) score has been proposed as a reliable tool for the evaluation of the opacity of chest X-rays (CXRs). This study aimed to examine the RALE scores and outcomes in patients with influenza-associated ARDS. Methods: Patients who were newly diagnosed with influenza-associated ARDS from December 2015 to March 2016 were enrolled. Two independent reviewers scored the CXRs obtained on the day of ICU admission and on days 2 and 7 after intensive care unit (ICU) admission. Results: During the study, 47 patients had influenza-associated ARDS. Five died within 7 days of ICU admission. Of the remaining 42, non-survivors (N = 12) had higher Sequential Organ Failure Assessment scores (SOFA) at ICU admission and higher day 7 RALE scores than survivors (N = 30). The day 7 RALE score independently related to late in-hospital mortality (aOR = 1.121, 95% CI: 1.014–1.240, p = 0.025). Conclusions: The RALE score for the evaluation of opacity on CXRs is a highly reproducible tool. Moreover, RALE score on day 7 was an independent predictor of late in-hospital mortality in patients with influenza-associated ARDS. Full article

Introduction: Unilateral pulmonary edema (UPE) is a potential complication after mitral valve surgery (MVS), and its cause is not yet fully understood. Definitions are inconsistent, and previous studies have reported wide variance in the incidence of UPE. This research aims at the evaluation of the Radiographic Assessment of Lung Edema (RALE) score concerning assessment of UPE after MVS in order to provide an accurate and consistent definition of this pathology. Methods and Results: Postoperative chest X-ray images of 676 patients after MVS (minimally invasive MVS, n = 434; conventional MVS, n = 242) were retrospectively analyzed concerning presence of UPE. UPE was diagnosed only after exclusion of other pathologies up until the eighth postoperative day. RALE values were calculated for each patient. ROC analysis was performed to assess diagnostic performance. UPE was diagnosed in 18 patients (2.8%). UPE occurred significantly more often in the MI-MVS group (p = 0.045; MI-MVS n = 15; C-MVS n = 3). Postoperative RALE values for the right hemithorax (Q1 + Q2) > 12 and the right-to-left RALE difference ((Q1 + Q2) − (Q3 + Q4)) > 13 provide a sensitivity of up to 100% and 94.4% and a specificity of up to 88.4% and 94.2% for UPE detection. Conclusion: The RALE score is a practical tool for assessment of chest X-ray images after MVS with regard to UPE and provides a clear definition of UPE. In addition, it enables objective comparability when assessing of the postoperative course. The given score thresholds provide a sensitivity and specificity of up to 94%. Further, UPE after MVS seems to be a rather rare pathology with an incidence of 2.6%. Full article

The prediction of corticosteroid responses in coronavirus disease 2019 (COVID-19) patients is crucial in clinical practice, and exploring the role of artificial intelligence (AI)-assisted analysis of chest radiographs (CXR) is warranted. This retrospective case–control study involving mild-to-moderate COVID-19 patients treated with corticosteroids was conducted from 4 September 2021, to 30 August 2022. The primary endpoint of the study was corticosteroid responsiveness, defined as the advancement of two or more of the eight-categories-ordinal scale. Serial abnormality scores for consolidation and pleural effusion on CXR were obtained using a commercial AI-based software based on days from the onset of symptoms. Amongst the 258 participants included in the analysis, 147 (57%) were male. Multivariable logistic regression analysis revealed that high pleural effusion score at 6–9 days from onset of symptoms (adjusted odds ratio of (aOR): 1.022, 95% confidence interval (CI): 1.003–1.042, p = 0.020) and consolidation scores up to 9 days from onset of symptoms (0–2 days: aOR: 1.025, 95% CI: 1.006–1.045, p = 0.010; 3–5 days: aOR: 1.03 95% CI: 1.011–1.051, p = 0.002; 6–9 days: aOR; 1.052, 95% CI: 1.015–1.089, p = 0.005) were associated with an unfavorable corticosteroid response. AI-generated scores could help intervene in the use of corticosteroids in COVID-19 patients who would not benefit from them. Full article

Background: CRB-65 (Confusion; Respiratory rate ≥ 30/min; Blood pressure ≤ 90/60 mmHg; age ≥ 65 years) is a risk score for prognosticating patients with COVID-19 pneumonia. However, a significant proportion of COVID-19 patients have normal chest X-rays (CXRs). The influence of CXR abnormalities on the prognostic value of CRB-65 is unknown, limiting its wider applicability. Methods: We assessed the influence of CXR abnormalities on the prognostic value of CRB-65 in COVID-19. Results: In 589 study patients (71 years (IQR: 57–83); 57% males), 186 (32%) had normal CXRs. On ROC analysis, CRB-65 performed similarly in patients with normal vs. abnormal CXRs for predicting inpatient mortality (AUC 0.67 ± 0.05 vs. 0.69 ± 0.03). In patients with normal CXRs, a CRB-65 of 0 ruled out mortality, NIV requirement and critical illness (intubation and/or ICU admission) with negative predictive values (NPVs) of 94%, 98% and 99%, respectively. In patients with abnormal CXRs, a CRB-65 of 0 ruled out the same endpoints with NPVs of 91%, 83% and 86%, respectively. Patients with low CRB-65 scores had better inpatient survival than patients with high CRB-65 scores, irrespective of CXR abnormalities (all p < 0.05). Conclusions: CRB-65, CXR and CRP are independent predictors of mortality in COVID-19. Adding CXR findings (dichotomised to either normal or abnormal) to CRB-65 does not improve its prognostic accuracy. A low CRB-65 score of 0 may be a good rule-out test for adverse clinical outcomes in COVID-19 patients with normal or abnormal CXRs, which deserves prospective validation. Full article