Chest X-ray Research Articles

Closing Situations of Tuberculosis Cases in A Municipality in Southeast Matogrosso During the Pandemic Period

Objective: To analyze tuberculosis cases according to the outcome situation in Rondonópolis, Mato Grosso. Method: Ecological and descriptive study carried out with data from the Epidemiological Surveillance database of Rondonópolis, Mato Grosso, from March 2020 to March 2023. The population was all reported cases of tuberculosis according to the outcome situation (cure, treatment abandonment and death from tuberculosis). Death from other causes, transfer and ignored and/or blank cases were excluded. Descriptive statistics used absolute and relative frequencies with the aid of the R software. Study approved by the Research Ethics Committee (Opinion: 6,130,934). Results: A total of 233 cases of tuberculosis with outcome situation were reported (71.67% cure, 23.18% treatment abandonment and 5.15% death). The profile was male, mixed race, high school graduate, new case admission type and chest X-ray with suspicious finding. Cure was prevalent among people aged 61 to 70, while treatment abandonment and death were prevalent among people aged 31 to 40. Research Implications: The study highlights that, despite the high cure rate, treatment abandonment is a challenge. It is essential to continue studies to monitor and improve tuberculosis control measures. Originality/Value: The results highlight the need to propose specific interventions for vulnerable groups, in addition to the importance of complete notifications by health professionals.

Varied Clinical Presentations of Abdominal Tuberculosis Causing Bowl Obstruction

Background: This study investigates the clinical presentations and surgical outcomes of abdominal Koch's disease, a common cause of intestinal obstruction, and the outcomes of surgical interventions.Objective: To evaluate the clinical presentations, surgical outcomes, and postoperative progress in patients with abdominal tuberculosis causing intestinal obstruction.Materials and Methods: A study of 50 patients diagnosed with abdominal TB at DHQ Teaching Hospital from 16 September 2021 to 16 September 2022 found that 80% were emergency cases, with a male-to-female ratio of 1:1.2, and a mean age of 24 years. The majority were Afghan nationals, with a majority coming from lower-middle socioeconomic backgrounds. Common symptoms included abdominal pain, distension, constipation, vomiting, weight loss, pallor, abdominal tenderness, and a mass in the abdomen. Laboratory findings included anemia, leukocytosis and raised ESR. Imaging findings showed distended small bowel loops and multiple air-fluid levels in 80% of cases, while 20% had evidence of coexisting pulmonary Koch's on chest X-rays.Results: Abdominal Koch's is diagnosed 50% of the time, with 35% of confirmed cases having positive clinical findings. Common lesions included strictures, ileocecal masses, and intestinal perforations. Surgical procedures varied, and patients received full antitubercular therapy postoperatively. Complications occurred in 30% of cases, and hospital mortality was 4%.Conclusions: Abdominal tuberculosis, a major cause of intestinal obstruction, but it is mostly asymptomatic and commonly affecting lower-middle socioeconomic groups. Surgical intervention and antitubercular therapy often led to improvement, but postoperative complications and mortality persist.Keywords: Abdominal tuberculosis, Intestinal obstruction, Mycobacterium tuberculosis.

CNSC-55. UNCOVERING THE ROLE OF COLLAGEN EXTRACELLULAR MATRIX IN GLIOBLASTOMA BRAIN MICROENVIRONMENT TRANSFORMATION

Abstract Glioblastoma (GBM) is a highly malignant brain tumor characterized by diffuse infiltration and significant heterogeneity, without effective treatment. Mesenchymal-like (MES) glioma cells drive tumor aggression by promoting brain invasion and tumor microenvironment remodeling. Previous spatial transcriptomic analyses identified Collagen, particularly COL1A, as a key extracellular matrix (ECM) component, enriched in MES areas termed oncostreams. Depletion of COL1A1 disrupted oncostreams, influencing the tumor microenvironment to support cell survival, and migration. Reactive astrocytes and macrophages, essential components of the brain microenvironment, impact glioma progression. However, the interplay between glioma-secreted collagen and the brain microenvironment remains poorly understood. To elucidate this, we generated orthotopic mouse glioma models with high-collagen (NPD) and low-collagen (NPDshCol1A1). Our data revealed that downregulation of COL1A1 in glioma cells alters the abundance and spatial distribution of reactive astrocytes and macrophages/microglia. Immunofluorescence and flow cytometry analysis showed that tumors with low-collagen had decreased GFAP+ reactive astrocyte cells at the invasive edge and perivascular niche compared to high-collagen gliomas. Moreover, our results shows that high collagen levels may activate astrocytes through distant communication, as evidenced by the higher presence of reactive astrocytes in the contralateral cortex of NPD tumors. However, low-collagen tumors exhibited an increase in astrocytes (GFAP+) in the tumor core. Furthermore, high-collagen gliomas demonstrated increased macrophage infiltration, promoting an immunosuppressive environment. Using multiplex immunohistochemistry assays, we observed that reactive astrocytes along the invasive tumor edge were associated with glioma cells expressing elevated levels of COL1A1 and TGF-β ligand. In summary, our results show that glioma cell-expressed COL1A1 promotes the activation of peritumoral and perivascular brain-resident astrocytes and macrophages. The interactions of reactive astrocyte with glioma cells enriched in collagen form a feedback loop that enhances tumor invasion and vascular proliferation, suggesting that targeting the ECM-astrocyte interactions could disrupt these processes and offer a therapeutic avenue for GBM.

EPCO-07. INTEGRATING SPATIALLY RESOLVED MULTI-OMICS DATA TO UNCOVER DYSFUNCTIONAL METABOLISM DRIVEN NETWORKS THAT ENHANCE INFILTRATION OF DIFFUSE GLIOMAS

Abstract BACKGROUND Diffuse infiltration is an aggressive feature of high-grade gliomas with survival implications. The contribution of crosstalk between non-neoplastic and neoplastic cells to tumor infiltration remains largely understudied due to the lack of profiling techniques that retain spatial information. Spatial multi-omic profiling is a promising approach to comprehensively analyze transcript-omics, prote-omics and metabol-omics on the same tissue section while preserving information about the spatial organization of cells. Integration of these spatial studies allows for inferring the consequences of complex cell-cell communication underlying tumor infiltration. We hypothesize that the glioma edge is enriched with pro-infiltration ligands-receptors driving tumor infiltration. Strategies that disrupt these ligand-receptor networks may suppress glioma infiltration and improve clinical outcomes. METHODS We utilized a glioma tissue micro-array (TMA) to establish the neoplastic and non-neoplastic heterogeneity in the GBM infiltration edge. Each TMA slide consists of pathologist annotated tumor core and edge samples of Glioblastoma (IDH Wildtype, WHO Grade 4; n=10), Diffuse Astrocytoma (IDH Mutant, WHO Grade 3; n=3), Oligodendroglioma (IDH mutant, WHO Grade 2; n=5) and 2 non-brain control samples. We performed spatial multi-omics profiling on adjacent sections of the TMA using 10x Xenium spatial transcriptomics, imaging mass cytometry (IMC) and mass spectrometry imaging (MSI). Integration, visualization, and quantification of the spatial data was done on VisioPharm. RESULTS In GBM, we identified candidate mRNA transcripts and proteins for ligands enriched at the infiltrating edge (compared to tumor core; p<0.0001) that correlated with a poor progression free survival (PFS; r2=0.22). These candidate ligands were also significantly enriched at the edge of oligodendroglioma WHO Grade 2 and astrocytoma WHO Grade 3. CONCLUSIONS Spatial multi-omics profiling on a TMA consisting of Glioma WHO grades 2-4 identified differentially expressed and targetable pro-tumor infiltration ligands associated with lower PFS in GBM. Functional studies to uncover the role of metabolites enriched at the glioma edge for the expression of the identified pro-infiltration ligands are ongoing.

The Study of Lung Ultrasound, Chest CT and Chest X-ray in Children with COVID-19 Hospitalized in Mofid Children's Hospital During a Six-Month Period

Background: Coronavirus disease (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The primary diagnostic tool for pediatric COVID-19 patients is polymerase chain reaction (PCR). Chest imaging findings in pediatric COVID-19 cases are often normal or mild. The correlation of lung ultrasound (LUS) with chest computed tomography (CT) and chest X-ray (CXR) in pediatric COVID-19 patients has not been extensively studied. Objectives: This study aimed to assess the correlation of LUS with chest CT and CXR in detecting COVID-19 pneumonia in children. Methods: This single-center cross-sectional study included patients under 18 years of age diagnosed with COVID-19 by PCR or by abnormal chest CT findings suggestive of COVID-19, admitted to Mofid Pediatric Hospital between December 2021 and August 2022. All patients underwent LUS. Approximately half of the patients also had a chest CT, and CXR was performed on 35 patients. Lung ultrasound and CT scores were calculated, and the correlation between these scores was evaluated. The correlation between LUS and CXR was also analyzed. Results: Sixty patients were included, of whom 21 were female, with a mean age of 4.9 ± 4.0 years. A significant correlation was observed between LUS and CT scores (correlation coefficient = 0.467, P = 0.011). Lesion distribution was similar between LUS and CT. However, no significant correlation was found between LUS scores and CXR findings (P = 0.392). Sixteen out of 19 patients with normal CXRs had LUS scores ≤ 4. Notably, three patients with normal CXRs had LUS scores of 8, 14, and 14. Conclusions: Lung ultrasound was more sensitive than CXR and demonstrated a significant correlation with CT. Lung ultrasound may serve as a safe alternative to CT for detecting COVID-19 pneumonia in pediatric patients.

Respiratory Physiotherapy Interventions in Paediatric Population with Atelectasis: A Systematic Review.

Objective: This systematic review aims to assess the effectiveness of respiratory physiotherapy techniques in oxygenation, chest X-ray findings, and lung auscultation in paediatric patients aged 0 to 18 years diagnosed with atelectasis. Methods: A comprehensive search was conducted in the PubMed, PEDro, Web of Science, and Cochrane Library databases. Results: Eight randomised clinical trials were included, involving 430 children ranging from 35 weeks of gestational age to 14 years. These trials evaluated various respiratory physiotherapy techniques and their effects on oxygenation and chest radiograph outcomes. The methodological quality of the studies ranged from acceptable to good, according to the PEDro scale. Conclusions: Recent evidence indicates that respiratory physiotherapy is effective and safe in the paediatric population with atelectasis. Both manual and instrumental techniques demonstrated efficacy, with instrumental techniques showing superior outcomes in many cases.

Pneumonia detection on chest X-rays from Xception-based transfer learning and logistic regression.

Pneumonia is a dangerous disease that kills millions of children and elderly patients worldwide every year. The detection of pneumonia from a chest x-ray is perpetrated by expert radiologists. The chest x-ray is cheaper and is most often used to diagnose pneumonia. However, chest x-ray-based diagnosis requires expert radiologists which is time-consuming and laborious. Moreover, COVID-19 and pneumonia have similar symptoms which leads to false positives. Machine learning-based solutions have been proposed for the automatic prediction of pneumonia from chest X-rays, however, such approaches lack robustness and high accuracy due to data imbalance and generalization errors. This study focuses on elevating the performance of machine learning models by dealing with data imbalanced problems using data augmentation. Contrary to traditional machine learning models that required hand-crafted features, this study uses transfer learning for automatic feature extraction using Xception and VGG-16 to train classifiers like support vector machine, logistic regression, K nearest neighbor, stochastic gradient descent, extra tree classifier, and gradient boosting machine. Experiments involve the use of hand-crafted features, as well as, transfer learning-based feature extraction for pneumonia detection. Performance comparison using Xception and VGG-16 features suggest that transfer learning-based features tend to show better performance than hand-crafted features and an accuracy of 99.23% can be obtained for pneumonia using chest X-rays.

Radiological predictors of PCP in HIV-positive adults in South Africa: A matched case-control study

Background: Definition of chest X-ray (CXR) features associated with laboratory-confirmed pneumocystis pneumonia (PCP) among HIV-positive adults is needed to improve diagnosis in high-burden settings.Objectives: Our primary objective was to identify CXR features associated with confirmed PCP diagnosis and severe PCP (defined by hypoxia, intensive care unit referral/admission, and/or in-hospital death). We also explored the performance of logistic regression models, incorporating selected clinical and CXR predictors, for PCP diagnosis and severe PCP.Method: We conducted a case-control study involving HIV-positive adults with laboratory-confirmed PCP and a matched cohort with non-PCP respiratory presentations at regional hospitals in Cape Town, South Africa (2012–2020).Results: Records from 104 adults (52 PCP cases and 52 non-PCP controls) were included. Diffuse versus patchy ground-glass opacification was associated with increased odds of PCP diagnosis (adjusted odds ratio [aOR]: 6.2, 95% confidence interval [CI]: 1.6–28.9, P = 0.01) and severe PCP (aOR: 4.5, 95% CI: 1.6–14.4, P = 0.008). Consolidation was associated with severe PCP (aOR: 3.3, 95% CI: 1.2–11.0, P = 0.03) as was increasing ground-glass zone involvement (aOR: 2.1 for each one-unit increase in involved zone; 95% CI: 1.4–3.2, P = 0.0004). Models incorporating hypoxia (hypoxia model) or tachypnoea (respiratory rate model) with diffuse ground-glass opacities, absence of pleural effusion or reticular/reticulonodular changes on CXR performed well in predicting PCP (area under the receiver operating characteristic curve 0.828 [hypoxia model] and 0.857 [respiratory rate model]).Conclusion: CXR evaluation alongside bedside clinical information offers good accuracy for discriminating definite PCP from other HIV-associated respiratory diseases.

Latent TB screening without chest x-ray: a single centre experience.

Following a previous audit of latent tuberculosis (TB) screening prior to biologic treatment in our centre, which showed that chest x-ray was not useful in screening for latent TB, we removed chest x-ray from our screening protocol. We report the results of a review of latent TB screening in 761 patients prior to commencing biologic agents for psoriasis or hidradenitis suppurativa, finding that rates of latent TB infection in this cohort (3.3%) are similar to background rates of LTBI in the Republic of Ireland.

Manifestations of toxic pulmonary edema during respiratory support in an experiment

BACKGROUND: Currently, respiratory support is successfully used to treat pulmonary edema of non-toxic origin. The manifestations of pulmonary edema of non-toxic origin and toxic pulmonary edema have similar features, so respiratory support can be effective in treating the latter. AIM: To describe the manifestations of toxic pulmonary edema in rabbits and demonstrate the effectiveness of artificial pulmonary ventilation with support of positive end-expiratory pressure (PEEP) in case of severe intoxication with thermal destruction products of fluoroplastic-4. MATERIALS AND METHODS: Three rabbits were used in the study: rabbit N 1 (control), rabbit N 2 (intoxication) and rabbit N 3 (treatment). Rabbits N 2 and 3 were subjected to severe intoxication with thermal destruction products of fluoroplastic-4 (1,5 HLC50, 15 min). For treatment, rabbit N 3 (treatment), an hour after exposure, underwent mechanical ventilation with PEEP (pressure-controlled mode; oxygen fraction — 0,3; starting PEEP — 5 cm H2O, tidal volume — 20–25 ml). At various times, chest radiography was performed, oxygenation index, hemoglobin saturation (SaO2), and partial pressure of carbon dioxide in exhaled air (PetCO2) were determined. Posthumously, pathological changes in lung tissue, pulmonary coefficient were determined, and histological examination was performed. RESULTS: Exposure of rabbit N 2 (intoxication) to the thermal destruction products of fluoroplastic-4 led to the sequential formation of the interstitial and alveolar phases of toxic pulmonary edema, which contributed to its death 13 hours after exposure. As SaO2 decreased and PetCO2 increased (3 and 5 hours after exposure), in rabbit N 3 (treatment), during respiratory support, PEEP was increased twice by 2 cm H2O (maintaining a given respiratory volume), which led to the normalization of the studied parameters. On the 7th day after exposure, the condition of rabbit N 3 (treatment) did not differ from the condition of rabbit N 1 (control); no pathological changes in the respiratory system were detected. CONCLUSION: Carrying out mechanical ventilation with PEEP, started an hour after exposure (with a stepwise increase in PEEP as the condition worsens), is effective for correcting toxic pulmonary edema in rabbits caused by severe intoxication with the thermal destruction products of fluoroplastic-4.

Prolonged Fever After the Treatment of Ulcerative Colitis in an Adolescent.

A 17-year-old female was diagnosed with inflammatory bowel disease and started on infliximab. A few weeks after starting infliximab, she developed a recurrence of daily fevers associated with an intermittent dry cough, which worsened over the course of a month. A chest radiograph, abdominal ultrasound, and computed tomography scan of the chest and abdomen revealed a heterogeneous spleen with multiple hyperechoic areas, tiny splenic micronodules, and diffuse micronodularity throughout the lungs. She was transferred to a tertiary care hospital because of hypotension, new oxygen requirements, and ongoing fever. Her bloodwork on presentation to tertiary care revealed pancytopenia and elevated inflammatory markers; she had splenomegaly on MRI. As her clinical picture evolved, she continued to have persistent fevers and anorexia despite ongoing management. Infectious diseases, rheumatology, and gastroenterology were consulted to guide the evaluation and management of this patient's complex clinical course.

Enhanced Tuberculosis Detection in Chest X-Rays Using Optimal Feature Selection with Hybrid Binary Particle Swarm Optimization (HBPSO) and a Dual Classifier Framework Combining LSTM and Spiking Neural Networks

Tuberculosis (TB) remains a significant global health challenge, particularly in developing countries, where rapid and accurate diagnosis is crucial for effective treatment. Recent advancements in machine learning and image processing have shown promise in enhancing the detection of TB from chest X-ray images. This research introduces a novel approach that integrates a hybrid feature selection method with a dual classifier framework to improve the accuracy of tuberculosis detection. Feature extraction is conducted using Multi-Scale Local Binary Patterns (MSLBP), Speeded-Up Robust Features (SURF) with Bag of Words (BoW), and Cartoon Texture Features, combining them into a comprehensive feature vector. The Hybrid Binary Particle Swarm Optimization (HBPSO) is employed for optimal feature selection, reducing redundancy and improving classification efficiency. Classification is performed using a dual framework involving a Long Short-Term Memory (LSTM) network and a Spiking Neural Network (SNN), which work in tandem to provide robust predictions. Majority voting is utilized to finalize predictions, ensuring high accuracy and adaptability across varying TB presentations. Extensive evaluation on publicly available datasets shows that this hybrid approach significantly outperforms existing methods, achieving a highest accuracy of 99.73%, along with superior precision, recall, and F1 scores.

Bilateral chylothorax following papillary thyroid carcinoma with cervical lymph node dissection: Case report and comprehensive review of the literature.

This case analysis and literature review aim to identify the causes of bilateral chylothorax following thyroid cancer surgery, a rare yet serious complication. We report 2 East Asian women who developed bilateral chylothorax after undergoing total thyroidectomy with neck lymph node dissection. Both patients presented with dyspnea and significant pleural effusion postoperatively. Both patients were diagnosed with bilateral chylothorax based on clinical examination and imaging studies, including chest ultrasonography and X-rays. In both cases, conservative management was initially implemented, involving chest tube drainage, total parenteral nutrition, and octreotide therapy. Surgical intervention was considered if conservative measures failed to control the chylous output. Both patients showed gradual improvement with conservative treatment, ultimately resulting in successful resolution of pleural effusion and discharge from the hospital without complications. For patients with bilateral chylothorax, conservative treatment should be the initial approach for small effusions. For moderate to large effusions, placement of a chest drainage tube is recommended, and surgical intervention should be considered if chyle volume exceeds 10 mL/(kg/d) for 48 to 72 hours or persists for more than 11 days following conservative treatment.

Navigating the Spectrum: Assessing the Concordance of ML-Based AI Findings with Radiology in Chest X-Rays in Clinical Settings.

Background: The integration of artificial intelligence (AI) into radiology aims to improve diagnostic accuracy and efficiency, particularly in settings with limited access to expert radiologists and in times of personnel shortage. However, challenges such as insufficient validation in actual real-world settings or automation bias should be addressed before implementing AI software in clinical routine. Methods: This cross-sectional study in a maximum care hospital assesses the concordance between diagnoses made by a commercial AI-based software and conventional radiological methods augmented by AI for four major thoracic pathologies in chest X-ray: fracture, pleural effusion, pulmonary nodule and pneumonia. Chest radiographs of 1506 patients (median age 66 years, 56.5% men) consecutively obtained between January and August 2023 were re-evaluated by the AI software InferRead DR Chest®. Results: Overall, AI software detected thoracic pathologies more often than radiologists (18.5% vs. 11.1%). In detail, it detected fractures, pneumonia, and nodules more frequently than radiologists, while radiologists identified pleural effusions more often. Reliability was highest for pleural effusions (0.63, 95%-CI 0.58-0.69), indicating good agreement, and lowest for fractures (0.39, 95%-CI 0.32-0.45), indicating moderate agreement. Conclusions: The tested software shows a high detection rate, particularly for fractures, pneumonia, and nodules, but hereby produces a nonnegligible number of false positives. Thus, AI-based software shows promise in enhancing diagnostic accuracy; however, cautious interpretation and human oversight remain crucial.

Development and Validation of Deep Learning-Based Infectivity Prediction in Pulmonary Tuberculosis Through Chest Radiography: Retrospective Study.

Pulmonary tuberculosis (PTB) poses a global health challenge owing to the time-intensive nature of traditional diagnostic tests such as smear and culture tests, which can require hours to weeks to yield results. This study aimed to use artificial intelligence (AI)-based chest radiography (CXR) to evaluate the infectivity of patients with PTB more quickly and accurately compared with traditional methods such as smear and culture tests. We used DenseNet121 and visualization techniques such as gradient-weighted class activation mapping and local interpretable model-agnostic explanations to demonstrate the decision-making process of the model. We analyzed 36,142 CXR images of 4492 patients with PTB obtained from Severance Hospital, focusing specifically on the lung region through segmentation and cropping with TransUNet. We used data from 2004 to 2020 to train the model, data from 2021 for testing, and data from 2022 to 2023 for internal validation. In addition, we used 1978 CXR images of 299 patients with PTB obtained from Yongin Severance Hospital for external validation. In the internal validation, the model achieved an accuracy of 73.27%, an area under the receiver operating characteristic curve of 0.79, and an area under the precision-recall curve of 0.77. In the external validation, it exhibited an accuracy of 70.29%, an area under the receiver operating characteristic curve of 0.77, and an area under the precision-recall curve of 0.8. In addition, gradient-weighted class activation mapping and local interpretable model-agnostic explanations provided insights into the decision-making process of the AI model. This proposed AI tool offers a rapid and accurate alternative for evaluating PTB infectivity through CXR, with significant implications for enhancing screening efficiency by evaluating infectivity before sputum test results in clinical settings, compared with traditional smear and culture tests.

Collaboration between clinicians and vision-language models in radiology report generation.

Automated radiology report generation has the potential to improve patient care and reduce the workload of radiologists. However, the path toward real-world adoption has been stymied by the challenge of evaluating the clinical quality of artificial intelligence (AI)-generated reports. We build a state-of-the-art report generation system for chest radiographs, called Flamingo-CXR, and perform an expert evaluation of AI-generated reports by engaging a panel of board-certified radiologists. We observe a wide distribution of preferences across the panel and across clinical settings, with 56.1% of Flamingo-CXR intensive care reports evaluated to be preferable or equivalent to clinician reports, by half or more of the panel, rising to 77.7% for in/outpatient X-rays overall and to 94% for the subset of cases with no pertinent abnormal findings. Errors were observed in human-written reports and Flamingo-CXR reports, with 24.8% of in/outpatient cases containing clinically significant errors in both report types, 22.8% in Flamingo-CXR reports only and 14.0% in human reports only. For reports that contain errors we develop an assistive setting, a demonstration of clinician-AI collaboration for radiology report composition, indicating new possibilities for potential clinical utility.

A survey of bridging bone on chest radiography shows a greater than expected prevalence of marginal syndesmophytes.

The recognition of thin marginal spinal syndesmophytes is important, in part due to their association with non-traumatic or mildly traumatic vertebral fractures. To determine a lower limit on the prevalence of marginal spinal syndesmophytes using chest radiographs. We conducted a retrospective analysis of 500 chest radiographs, assessing the prevalence of thin marginal syndesmophytes, bridging or near-bridging osteophytes, and flowing paravertebral ossifications in the thoracic intervertebral discs among individuals aged 16 years and older in a North American city. Among the 500 participants, we observed that thin vertical marginal syndesmophytes were present in 17 (3.4%) cases, bridging or near-bridging osteophytes were present in 126 (25.2%) cases, and flowing paravertebral ossifications were present in 37 (7.4%) cases. Out of the 17 participants with thin marginal syndesmophytes, 10 exhibited a bamboo-like spine appearance, defined as the presence of ≥4 contiguous levels of bridging marginal syndesmophytes. Analysis of syndesmophyte distribution per vertebral level indicated a higher frequency of involvement in the mid to lower thoracic spine, maximal at T9/10. The presence of thin marginal syndesmophytes in the thoracic spine on routine chest radiographs is substantially more prevalent than would be anticipated based on the existing literature. The feasibility of reliably identifying these syndesmophytes in the spine and the impact of this on morbidity should be further investigated due to their association with advanced ankylosing spondylitis and their susceptibility to fractures.

Bullectomy with video-assisted thoracic surgery and minimally invasive repair of pectus excavatum simultaneously

IntroductionPectus excavatum (PE) is the most common chest wall deformity. Surgical treatment with minimally invasive repair (MIRPE) is the most preferred surgical procedure. In the presence of additional pulmonary pathologies, simultaneous surgical procedures have been considered, but there are few cases reported in the literature.Case presentationMIRPE was planned for a 15-year-old male patient due to PE. The day before the surgery, the patient developed sudden onset of shortness of breath. Pneumothorax was observed on the right side in the chest radiograph. Thereupon, the surgery was brought forward. Video-assisted thoracoscopic surgery (VATS) bullectomy was performed in the left lateral decubitus position. MIRPE was then performed through the same incision in the semi-lateral decubitus position. The patient was planned to be discharged on the 7th day, but on the day of discharge, a pneumothorax on the left was observed on the chest x-ray. VATS bullectomy was performed in the left semi-lateral decubitus position. The patient was discharged on postoperative day five.ConclusionSince the long-term results of simultaneous surgical procedures are unknown. With MIRPE, results such as less pain, shorter hospital stays, and earlier involvement in social life are achieved. Keeping in mind that these advantages may be lost with additional procedures, we think that simultaneous procedures should be planned.

NOVEL DEVICE FOR ENHANCING TUBERCULOSIS DIAGNOSIS FOR FASTER, MORE ACCURATE SCREENING RESULTS

Tuberculosis (TB) continues to be a leading global cause of illness and death, highlighting an urgent need for rapid, precise diagnostic solutions. Conventional methods, such as chest X-rays and sputum smear microscopy, often lack adequate sensitivity and specificity, resulting in treatment delays and increased transmission. This research introduces a novel device leveraging deep learning to enhance TB diagnosis, utilizing a convolutional neural network (CNN) model designed to automatically detect TB-related abnormalities in chest X-ray images. The device, powered by a large annotated dataset, incorporates transfer learning to fine-tune pre-trained models, maximizing diagnostic performance. Key evaluation metrics—accuracy, precision, recall, and F1-score—demonstrate the model’s efficacy compared to traditional diagnostics. Preliminary findings show that the AI-driven approach significantly enhances diagnostic accuracy, reducing false negatives and facilitating faster screenings. These results suggest that embedding AI technology in clinical workflows can enable earlier TB detection, optimize healthcare resources, and improve patient outcomes. Future directions include testing the device across diverse clinical environments and exploring integration with mobile health technologies to broaden access to rapid TB diagnostics.

Optimizing pulmonary chest x-ray classification with stacked feature ensemble and swin transformer integration

This research presents an integrated framework designed to automate the classification of pulmonary chest x-ray images. Leveraging convolutional neural networks (CNNs) with a focus on transformer architectures, the aim is to improve both the accuracy and efficiency of pulmonary chest x-ray image analysis. A central aspect of this approach involves utilizing pre-trained networks such as VGG16, ResNet50, and MobileNetV2 to create a feature ensemble. A notable innovation is the adoption of a stacked ensemble technique, which combines outputs from multiple pre-trained models to generate a comprehensive feature representation. In the feature ensemble approach, each image undergoes individual processing through the three pre-trained networks, and pooled images are extracted just before the flatten layer of each model. Consequently, three pooled images in 2D grayscale format are obtained for each original image. These pooled images serve as samples for creating 3D images resembling RGB images through stacking, intended for classifier input in subsequent analysis stages. By incorporating stacked pooling layers to facilitate feature ensemble, a broader range of features is utilized while effectively managing complexities associated with processing the augmented feature pool. Moreover, the study incorporates the Swin Transformer architecture, known for effectively capturing both local and global features. The Swin Transformer architecture is further optimized using the artificial hummingbird algorithm (AHA). By fine-tuning hyperparameters such as patch size, multi-layer perceptron (MLP) ratio, and channel numbers, the AHA optimization technique aims to maximize classification accuracy. The proposed integrated framework, featuring the AHA-optimized Swin Transformer classifier utilizing stacked features, is evaluated using three diverse chest x-ray datasets-VinDr-CXR, PediCXR, and MIMIC-CXR. The observed accuracies of 98.874%, 98.528%, and 98.958% respectively, underscore the robustness and generalizability of the developed model across various clinical scenarios and imaging conditions.