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Background: Spontaneous pneumothorax is a common clinical condition due to several etiologies. Appropriate radiologic assessment helps avoid re-imaging and guide individualized management. Objective: To describe the etiologic factors of spontaneous pneumothorax in children from a radiological perspective. Materials and methods: Thirty-nine children with spontaneous pneumothorax were evaluated according to their demographic characteristics, the presence of underlying lung diseases, and imaging findings. Results: Twenty-one patients without underlying lung disease were assessed as primary spontaneous pneumothorax; eight of these 21 patients (38.9%) had subpleural air cysts in the apices/upper lobes of the lung on chest computed tomography (CT). In the remaining 18 patients with secondary spontaneous pneumothorax, the most common causes were respiratory diseases (33.3%), infectious lung diseases (33.3%), interstitial lung diseases (27.7%), and connective tissue diseases (5.5%). The mean age of children with secondary spontaneous pneumothorax was lower than that of children with primary spontaneous pneumothorax (P = 0.002). Recurrences occurred in 11 patients (52.3%) with primary spontaneous pneumothorax and three patients (16.6%) with secondary spontaneous pneumothorax. Bilateral pneumothorax was observed in three of the 18 patients with secondary spontaneous pneumothorax. Conclusion: Identifying the etiologic factors of spontaneous pneumothorax may help clinicians plan how to reduce the risk of recurrence. Radiologists should keep in mind the possible underlying diseases and assess imaging methods in children with spontaneous pneumothorax for subpleural air cysts and be suspicious about an underlying disease in cases of bilateral pneumothorax and in younger patients. [ABSTRACT FROM AUTHOR]

Purpose: Many large radiographic datasets of lung nodules are available, but the small and hard-to-detect nodules are rarely validated by computed tomography. Such difficult nodules are crucial for training nodule detection methods. This lack of difficult nodules for training can be addressed by artificial nodule synthesis algorithms, which can create artificially embedded nodules. This study aimed to develop and evaluate a novel cost function for training networks to detect such lesions. Embedding artificial lesions in healthy medical images is effective when positive cases are insufficient for network training. Although this approach provides both positive (lesion-embedded) images and the corresponding negative (lesion-free) images, no known methods effectively use these pairs for training. This paper presents a novel cost function for segmentation-based detection networks when positive–negative pairs are available. Methods: Based on the classic U-Net, new terms were added to the original Dice loss for reducing false positives and the contrastive learning of diseased regions in the image pairs. The experimental network was trained and evaluated, respectively, on 131,072 fully synthesized pairs of images simulating lung cancer and real chest X-ray images from the Japanese Society of Radiological Technology dataset. Results: The proposed method outperformed RetinaNet and a single-shot multibox detector. The sensitivities were 0.688 and 0.507 when the number of false positives per image was 0.2, respectively, with and without fine-tuning under the leave-one-case-out setting. Conclusion: To our knowledge, this is the first study in which a method for detecting pulmonary nodules in chest X-ray images was evaluated on a real clinical dataset after being trained on fully synthesized images. The synthesized dataset is available at https://zenodo.org/records/10648433. [ABSTRACT FROM AUTHOR]

Introduction: Artificial intelligence (AI)-based systems using chest images are potentially reliable for diagnosing osteoporosis. Methods: We performed a systematic review and meta-analysis to assess the diagnostic accuracy of chest X-ray and computed tomography (CT) scans using AI for osteoporosis in accordance with the diagnostic test accuracy guidelines. We included any type of study investigating the diagnostic accuracy of index test for osteoporosis. We searched MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, and IEEE Xplore Digital Library on November 8, 2023. The main outcome measures were the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for osteoporosis and osteopenia. We described forest plots for sensitivity, specificity, and AUC. The summary points were estimated from the bivariate random-effects models. We summarized the overall quality of evidence using the Grades of Recommendation, Assessment, Development, and Evaluation approach. Results: Nine studies with 11,369 participants were included in this review. The pooled sensitivity, specificity, and AUC of chest X-rays for the diagnosis of osteoporosis were 0.83 (95% confidence interval [CI] 0.75, 0.89), 0.76 (95% CI 0.71, 0.80), and 0.86 (95% CI 0.83, 0.89), respectively (certainty of the evidence, low). The pooled sensitivity and specificity of chest CT for the diagnosis of osteoporosis and osteopenia were 0.83 (95% CI 0.69, 0.92) and 0.70 (95% CI 0.61, 0.77), respectively (certainty of the evidence, low and very low). Conclusions: This review suggests that chest X-ray with AI has a high sensitivity for the diagnosis of osteoporosis, highlighting its potential for opportunistic screening. However, the risk of bias of patient selection in most studies were high. More research with adequate participants' selection criteria for screening tool will be needed in the future. [ABSTRACT FROM AUTHOR]

The opportunistic use of radiological examinations for disease detection can potentially enable timely management. We assessed if an index created by an AI software to quantify chest radiography (CXR) findings associated with heart failure (HF) could distinguish between patients who would develop HF or not within a year of the examination. Our multicenter retrospective study included patients who underwent CXR without an HF diagnosis. We included 1117 patients (age 67.6 ± 13 years; m:f 487:630) that underwent CXR. A total of 413 patients had the CXR image taken within one year of their HF diagnosis. The rest (n = 704) were patients without an HF diagnosis after the examination date. All CXR images were processed with the model (qXR-HF, Qure.AI) to obtain information on cardiac silhouette, pleural effusion, and the index. We calculated the accuracy, sensitivity, specificity, and area under the curve (AUC) of the index to distinguish patients who developed HF within a year of the CXR and those who did not. We report an AUC of 0.798 (95%CI 0.77–0.82), accuracy of 0.73, sensitivity of 0.81, and specificity of 0.68 for the overall AI performance. AI AUCs by lead time to diagnosis (<3 months: 0.85; 4–6 months: 0.82; 7–9 months: 0.75; 10–12 months: 0.71), accuracy (0.68–0.72), and specificity (0.68) remained stable. Our results support the ongoing investigation efforts for opportunistic screening in radiology. [ABSTRACT FROM AUTHOR]

Despite periodic changes in the clinical definition of ARDS, imaging of the lung remains a central component of its diagnostic identification. Several imaging modalities are available to the clinician to establish a diagnosis of the syndrome, monitor its clinical course, or assess the impact of treatment and management strategies. Each imaging modality provides unique insight into ARDS from structural and/or functional perspectives. This review will highlight several methods for lung imaging in ARDS, emphasizing basic operational and physical principles for the respiratory therapist. Advantages and disadvantages of each modality will be discussed in the context of their utility for clinical management and decision-making. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this study is to evaluate the performance of our deep learning algorithm in calculating cardiothoracic ratio (CTR) and thus in the assessment of cardiomegaly or pericardial effusion occurrences on chest radiography (CXR). Methods: From a database of 8000 CXRs, 13 folders with a comparable number of images were created. Then, 1020 images were chosen randomly, in proportion to the number of images in each folder. Afterward, CTR was calculated using RadiAnt Digital Imaging and Communications in Medicine (DICOM) Viewer software (2023.1). Next, heart and lung anatomical areas were marked in 3D Slicer. From these data, we trained an AI model which segmented heart and lung anatomy and determined the CTR value. Results: Our model achieved an Intersection over Union metric of 88.28% for the augmented training subset and 83.06% for the validation subset. F1-score for subsets were accordingly 90.22% and 90.67%. In the comparative analysis of artificial intelligence (AI) vs. humans, significantly lower transverse thoracic diameter (TTD) (p < 0.001), transverse cardiac diameter (TCD) (p < 0.001), and CTR (p < 0.001) values obtained using the neural network were observed. Conclusions: Results confirm that there is a significant correlation between the measurements made by human observers and the neural network. After validation in clinical conditions, our method may be used as a screening test or advisory tool when a specialist is not available, especially on Intensive Care Units (ICUs) or Emergency Departments (ERs) where time plays a key role. [ABSTRACT FROM AUTHOR]

To determine if infants with exomphalos had abnormal antenatal lung growth as indicated by lower chest radiographic thoracic areas (CRTA) on day one compared to controls and whether the CRTA could predict the development of bronchopulmonary dysplasia (BPD). Infants with exomphalos cared for between January 2004 and January 2023 were included. The controls were term, newborn infants ventilated for absent respiratory drive at birth, without lung disease and had no supplemental oxygen requirement by 6 h of age. The radiographs were imported as digital image files by Sectra PACS software (Sectra AB, Linköping, Sweden). Free-hand tracing of the perimeter of the thoracic area was undertaken and the CRTA calculated by the software. Sixty-four infants with exomphalos and 130 controls were included. Infants with exomphalos had a lower median (IQR) CRTA (1,983 [1,657–2,471] mm2) compared to controls (2,547 [2,153–2,932] mm2, p<0.001). Following multivariable regression analysis, infants with exomphalos had lower CRTAs compared to controls (p=0.001) after adjusting for differences in gestational age and male sex. In the exomphalos group, the CRTAs were lower in those who developed BPD (n=14, 1,530 [1,307–1,941] mm2) compared to those who did not (2,168 [1,865–2,672], p<0.001). Following multivariable regression analysis, the CRTA was associated with BPD development (p=0.021) after adjusting for male sex and gestational age. Lower CRTAs on day one in the exomphalos infants compared to the controls predicted BPD development. [ABSTRACT FROM AUTHOR]

To determine the reliability of the cardiothoracic ratio (CTR) as a simple method to assess the cardiac size and function in patients with non-ischemic cardiomyopathy (NICM). In a sample of 91 patients (66 patients with diagnosed non-ischemic cardiomyopathy and 25 controls) we calculated the CTR on a posteroanterior chest radiograph and ventricular and atrial size based on accepted cardiovascular magnetic resonance (CMR) imaging values. Left and right ventricular ejection fraction was also calculated. The CTR and cardiac chamber size were compared between patients with NICM and healthy individuals. The distinction between normal and increased cardiac chamber size was made using published normal CMR reference values stratified by age and gender. CTR values were higher in the NICM group (50.7±5.5 % Vs. 45.3±4.7 %, p<0.001). Likewise, LVEDVi, LV indexed mass, LA indexed volume, LA indexed area, and RA indexed area were higher, and LVEF and RVEF were lower in patients with non-ischemic cardiomyopathy (p < 0.05). In patients with non-ischemic cardiomyopathy, the greatest correlation between CTR and CMR values was with LVEDVi (ρ=0.4, p < 0.001), LA indexed volume (ρ=0.5, p < 0.001), LA indexed area (ρ=0.5, p < 0.001) and RA indexed area (ρ=0.4, p < 0.001). However, the correlation strength was only moderate. Despite patients with NICM had higher CTR values than the control group, a substantial proportion of these patients showed normal CTRs (<50 %). This fact limits the usefulness of CTR to reliably predict NICM. Correlation between CTR and heart chamber dilation on CMR was only weak to moderate. [ABSTRACT FROM AUTHOR]

Case Presentation: An 8-month-old infant presented to a general emergency department with chief complaints of rhinorrhea, decreased activity, and fever. A point-of-care lung ultrasound (LUS) was performed at bedside with potential early findings of pneumonia. Based on these findings on LUS, a chest radiograph (CXR) was ordered and performed with no acute findings. He was discharged without antibiotics based on these findings; unfortunately, he returned two days later with worsening symptoms requiring chest tube placement, mechanical ventilation, and prolonged hospitalization for complicated bacterial pneumonia. Discussion: Pneumonia is a major cause of pediatric morbidity and mortality worldwide. Despite evidence supporting the utilization of LUS for the diagnosis of pediatric pneumonia, CXR remains the default imaging for clinical decision-making in most settings. In this case, earlier antibiotics and higher reliance on LUS for clinical decision-making may have prevented the morbidity associated with this hospitalization. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to evaluate the role of chest radiographs and electrocardiograms in predicting the hemodynamics of congenital heart disease (CHD). Methods: This retrospective study included 50 patients with a diagnosis of CHD who had undergone any form of cardiac intervention, either surgical or nonsurgical between September 2019 and September 2020. Chest radiographs and electrocardiograms were evaluated and compared with the diagnostic gold standard echocardiography. Results: Chest radiographs had the highest sensitivity, specificity, and accuracy, with all being 100%, in detecting situs and cardiac position. There was a very good agreement between chest radiographs and echocardiography in the detection of both situs and cardiac position (κ = 1.00, P < 0.001), while moderate agreement was observed for the detection of cardiomegaly, position of the aortic knuckle, main pulmonary artery dilation, and right pulmonary artery dilation. Electrocardiograms had a high sensitivity (100.00%), but modest specificity and accuracy for the detection of left ventricle pressure overload. For the detection of left atrial enlargement and left ventricle volume overload, electrocardiograms had high specificity (94.12% and 94.29%, respectively) but low sensitivity and modest accuracy. There was a moderate agreement between electrocardiograms and echocardiography in the detection of right ventricle pressure overload (κ = 0.43, P = 0.002) and left ventricle volume overload (κ = 0.46, P < 0.001). Conclusions: The study findings indicate that chest radiographs and electrocardiograms alone are not adequate for the assessment of hemodynamics of CHD and reinstates the recommendation that in addition to routine chest radiographs and electrocardiograms, echocardiography should be performed.

Background: Artificial intelligence (AI) has been proven useful for the assessment of tubes and lines on chest radiographs of general patients. However, validation on intensive care unit (ICU) patients remains imperative. Methods: This retrospective case-control study evaluated the performance of deep learning (DL) models for tubes and lines classification on both an external public dataset and a local dataset comprising 303 films randomly sampled from the ICU database. The endotracheal tubes (ETTs), central venous catheters (CVCs), and nasogastric tubes (NGTs) were classified into “Normal,” “Abnormal,” or “Borderline” positions by DL models with and without rule-based modification. Their performance was evaluated using an experienced radiologist as the standard reference. Results: The algorithm showed decreased performance on the local ICU dataset, compared to that of the external dataset, decreasing from the Area Under the Curve of Receiver (AUC) of 0.967 (95 % CI 0.965–0.973) to the AUC of 0.70 (95 % CI 0.68–0.77). Significant improvement in the ETT classification task was observed after modifications were made to the model to allow the use of the spatial relationship between line tips and reference anatomy with the improvement of the AUC, increasing from 0.71 (95 % CI 0.70 – 0.75) to 0.86 (95 % CI 0.83 – 0.94) Conclusions: The externally trained model exhibited limited generalizability on the local ICU dataset. Therefore, evaluating the performance of externally trained AI before integrating it into critical care routine is crucial. Rule-based algorithm may be used in combination with DL to improve results.

Insertion of a nasogastric tube (NGT) is generally considered safe; however, it is not without risk, and in cases of misplacement, complications and even death may occur. In this article, we reported a case of NGT misplacement in a 75‐year‐old male, which resulted in aspiration pneumonia. We also reviewed published cases of NGT misplacement. Clinicians should pay enough attention to the confirmation of the proper placement of an NGT. A systematic approach for NGT insertion and confirmation is required to prevent misplacement. [ABSTRACT FROM AUTHOR]

Objectives: Chest wall deformities are a series of abnormalities that extend from the sternum to the vertebral column and often cause aesthetic and psychological problems. Most chest wall deformities are caused by cartilaginous malformations such as pectus excavatum and pectus carinatum. The aim of this study was to provide a detailed description of chest wall abnormalities in young male athletes with no existing complaints. Methods: A comprehensive health assessment was performed on 1600 young men at the Erzincan Police Vocational Training Centre in March 2023. The evaluation included chest radiographs, pulmonary function tests, electrocardiography, transthoracic echocardiography, haemogram, biochemical test findings and comorbidities. Haller index scale was used to grade the severity of pectus deformity in individuals with pectus excavatum. Results: Pectus excavatum deformity was detected in 16 individuals (1%). Pectus carinatum was detected in only one individual (0.06%). Only one of the patients with chest wall deformity had an abnormal pulmonary function test, especially in the form of a minor obstructive pattern. In addition, 11 individuals in this group had associated electrocardiographic abnormalities. These abnormalities did not cause significant clinical findings. Conclusion: Our study showed that the prevalence of chest wall deformities in physically active young men is comparable to the prevalence of chest wall deformities reported for the general population in the available literature. Furthermore, this study demonstrated a higher prevalence of electrocardiographic abnormalities in subjects with chest wall deformities. [ABSTRACT FROM AUTHOR]

Prompt and correct detection of pulmonary tuberculosis (PTB) is critical in preventing its spread. We aimed to develop a deep learning–based algorithm for detecting PTB on chest X-ray (CXRs) in the emergency department. This retrospective study included 3498 CXRs acquired from the National Taiwan University Hospital (NTUH). The images were chronologically split into a training dataset, NTUH-1519 (images acquired during the years 2015 to 2019; n = 2144), and a testing dataset, NTUH-20 (images acquired during the year 2020; n = 1354). Public databases, including the NIH ChestX-ray14 dataset (model training; 112,120 images), Montgomery County (model testing; 138 images), and Shenzhen (model testing; 662 images), were also used in model development. EfficientNetV2 was the basic architecture of the algorithm. Images from ChestX-ray14 were employed for pseudo-labelling to perform semi-supervised learning. The algorithm demonstrated excellent performance in detecting PTB (area under the receiver operating characteristic curve [AUC] 0.878, 95% confidence interval [CI] 0.854–0.900) in NTUH-20. The algorithm showed significantly better performance in posterior-anterior (PA) CXR (AUC 0.940, 95% CI 0.912–0.965, p-value < 0.001) compared with anterior–posterior (AUC 0.782, 95% CI 0.644–0.897) or portable anterior–posterior (AUC 0.869, 95% CI 0.814–0.918) CXR. The algorithm accurately detected cases of bacteriologically confirmed PTB (AUC 0.854, 95% CI 0.823–0.883). Finally, the algorithm tested favourably in Montgomery County (AUC 0.838, 95% CI 0.765–0.904) and Shenzhen (AUC 0.806, 95% CI 0.771–0.839). A deep learning–based algorithm could detect PTB on CXR with excellent performance, which may help shorten the interval between detection and airborne isolation for patients with PTB. [ABSTRACT FROM AUTHOR]

This retrospective study evaluated a commercial deep learning (DL) software for chest radiographs and explored its performance in different scenarios. A total of 477 patients (284 male, 193 female, mean age 61.4 (44.7–78.1) years) were included. For the reference standard, two radiologists performed independent readings on seven diseases, thus reporting 226 findings in 167 patients. An autonomous DL reading was performed separately and evaluated against the gold standard regarding accuracy, sensitivity and specificity using ROC analysis. The overall average AUC was 0.84 (95%-CI 0.76–0.92) with an optimized DL sensitivity of 85% and specificity of 75.4%. The best results were seen in pleural effusion with an AUC of 0.92 (0.885–0.955) and sensitivity and specificity of each 86.4%. The data also showed a significant influence of sex, age, and comorbidity on the level of agreement between gold standard and DL reading. About 40% of cases could be ruled out correctly when screening for only one specific disease with a sensitivity above 95% in the exploratory analysis. For the combined reading of all abnormalities at once, only marginal workload reduction could be achieved due to insufficient specificity. DL applications like this one bear the prospect of autonomous comprehensive reporting on chest radiographs but for now require human supervision. Radiologists need to consider possible bias in certain patient groups, e.g., elderly and women. By adjusting their threshold values, commercial DL applications could already be deployed for a variety of tasks, e.g., ruling out certain conditions in screening scenarios and offering high potential for workload reduction. [ABSTRACT FROM AUTHOR]

Pericardial fat (PF)—the thoracic visceral fat surrounding the heart—promotes the development of coronary artery disease by inducing inflammation of the coronary arteries. To evaluate PF, we generated pericardial fat count images (PFCIs) from chest radiographs (CXRs) using a dedicated deep-learning model. We reviewed data of 269 consecutive patients who underwent coronary computed tomography (CT). We excluded patients with metal implants, pleural effusion, history of thoracic surgery, or malignancy. Thus, the data of 191 patients were used. We generated PFCIs from the projection of three-dimensional CT images, wherein fat accumulation was represented by a high pixel value. Three different deep-learning models, including CycleGAN were combined in the proposed method to generate PFCIs from CXRs. A single CycleGAN-based model was used to generate PFCIs from CXRs for comparison with the proposed method. To evaluate the image quality of the generated PFCIs, structural similarity index measure (SSIM), mean squared error (MSE), and mean absolute error (MAE) of (i) the PFCI generated using the proposed method and (ii) the PFCI generated using the single model were compared. The mean SSIM, MSE, and MAE were 8.56 × 10−1, 1.28 × 10−2, and 3.57 × 10−2, respectively, for the proposed model, and 7.62 × 10−1, 1.98 × 10−2, and 5.04 × 10−2, respectively, for the single CycleGAN-based model. PFCIs generated from CXRs with the proposed model showed better performance than those generated with the single model. The evaluation of PF without CT may be possible using the proposed method. [ABSTRACT FROM AUTHOR]

This study examined physicians' participation and performance in the examinations administered by the Asian Intensive Reader of Pneumoconiosis (AIR Pneumo) program from 2008 to 2020 and compared radiograph readings of physicians who passed with those who failed the examinations. Demography of the participants, participation trends, pass/fail rates, and proficiency scores were summarized; differences in reading the radiographs for pneumoconiosis of physicians who passed the examinations and those who failed were evaluated. By December 2020, 555 physicians from 20 countries had taken certification examinations; the number of participants increased in recent years. Reported background specialty training and work experience varied widely. Passing rate and mean proficiency score for participants who passed were 83.4% and 77.6 ± 9.4 in certification, and 76.8% and 88.1 ± 4.5 in recertification examinations. Compared with physicians who passed the examinations, physicians who failed tended to classify test radiographs as positive for pneumoconiosis and read a higher profusion; they likely missed large opacities and pleural plaques and had a lower accuracy in recognizing the shape of small opacities. Findings suggest that physicians who failed the examination tend to over-diagnose radiographs as positive for pneumoconiosis with higher profusion and have difficulty in correctly identifying small opacity shape. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to evaluate the performance of the commercially available artificial intelligence-based software CXR-AID for the automatic detection of pulmonary nodules on the chest radiographs of patients suspected of having lung cancer. Materials and methods: This retrospective study included 399 patients with clinically suspected lung cancer who underwent CT and chest radiography within 1 month between June 2020 and May 2022. The candidate areas on chest radiographs identified by CXR-AID were categorized into target (properly detected areas) and non-target (improperly detected areas) areas. The non-target areas were further divided into non-target normal areas (false positives for normal structures) and non-target abnormal areas. The visibility score, characteristics and location of the nodules, presence of overlapping structures, and background lung score and presence of pulmonary disease were manually evaluated and compared between the nodules detected or undetected by CXR-AID. The probability indices calculated by CXR-AID were compared between the target and non-target areas. Results: Among the 450 nodules detected in 399 patients, 331 nodules detected in 313 patients were visible on chest radiographs during manual evaluation. CXR-AID detected 264 of these 331 nodules with a sensitivity of 0.80. The detection sensitivity increased significantly with the visibility score. No significant correlation was observed between the background lung score and sensitivity. The non-target area per image was 0.85, and the probability index of the non-target area was lower than that of the target area. The non-target normal area per image was 0.24. Larger and more solid nodules exhibited higher sensitivities, while nodules with overlapping structures demonstrated lower detection sensitivities. Conclusion: The nodule detection sensitivity of CXR-AID on chest radiographs was 0.80, and the non-target and non-target normal areas per image were 0.85 and 0.24, respectively. Larger, solid nodules without overlapping structures were detected more readily by CXR-AID. [ABSTRACT FROM AUTHOR]

Objective: Although lung cancer screening trials have showed the efficacy of computed tomography to decrease mortality compared with chest radiography, the two are widely taken as different kinds of clinical practices. Artificial intelligence can improve outcomes by detecting lung tumors in chest radiographs. Currently, artificial intelligence is used as an aid for physicians to interpret radiograms, but with the future evolution of artificial intelligence, it may become a modality that replaces physicians. Therefore, in this study, we investigated the current situation of lung cancer diagnosis by artificial intelligence. Methods: In total, we recruited 174 consecutive patients with malignant pulmonary tumors who underwent surgery after chest radiography that was checked by artificial intelligence before surgery. Artificial intelligence diagnoses were performed using the medical image analysis software EIRL X-ray Lung Nodule version 1.12, (LPIXEL Inc., Tokyo, Japan). Results: The artificial intelligence determined pulmonary tumors in 90 cases (51.7% for all patients and 57.7% excluding 18 patients with adenocarcinoma in situ). There was no significant difference in the detection rate by the artificial intelligence among histological types. All eighteen cases of adenocarcinoma in situ were not detected by either the artificial intelligence or the physicians. In a univariate analysis, the artificial intelligence could detect cases with larger histopathological tumor size (p < 0.0001), larger histopathological invasion size (p < 0.0001), and higher maximum standardized uptake values of positron emission tomography-computed tomography (p < 0.0001). In a multivariate analysis, detection by AI was significantly higher in cases with a large histopathological invasive size (p = 0.006). In 156 cases excluding adenocarcinoma in situ, we examined the rate of artificial intelligence detection based on the tumor site. Tumors in the lower lung field area were less frequently detected (p = 0.019) and tumors in the middle lung field area were more frequently detected (p = 0.014) compared with tumors in the upper lung field area. Conclusions: Our study showed that using artificial intelligence, the diagnosis of tumor-associated findings and the diagnosis of areas that overlap with anatomical structures is not satisfactory. While the current standing of artificial intelligence diagnostics is to assist physicians in making diagnoses, there is the possibility that artificial intelligence can substitute for humans in the future. However, artificial intelligence should be used in the future as an enhancement, to aid physicians in the role of a radiologist in the workflow. [ABSTRACT FROM AUTHOR]

OBJECTIVES: We aimed to develop a computer-aided detection (CAD) system to localize and detect the malposition of endotracheal tubes (ETTs) on portable supine chest radiographs (CXRs). DESIGN: This was a retrospective diagnostic study. DeepLabv3+ with ResNeSt50 backbone and DenseNet121 served as the model architecture for segmentation and classification tasks, respectively. SETTING: Multicenter study. PATIENTS: For the training dataset, images meeting the following inclusion criteria were included: 1) patient age greater than or equal to 20 years; 2) portable supine CXR; 3) examination in emergency departments or ICUs; and 4) examination between 2015 and 2019 at National Taiwan University Hospital (NTUH) (NTUH-1519 dataset: 5,767 images). The derived CAD system was tested on images from chronologically (examination during 2020 at NTUH, NTUH-20 dataset: 955 images) or geographically (examination between 2015 and 2020 at NTUH Yunlin Branch [YB], NTUH-YB dataset: 656 images) different datasets. All CXRs were annotated with pixel-level labels of ETT and with image-level labels of ETT presence and malposition. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: For the segmentation model, the Dice coefficients indicated that ETT would be delineated accurately (NTUH-20: 0.854; 95% CI, 0.824-0.881 and NTUH-YB: 0.839; 95% CI, 0.820-0.857). For the classification model, the presence of ETT could be accurately detected with high accuracy (area under the receiver operating characteristic curve [AUC]: NTUH-20, 1.000; 95% CI, 0.999-1.000 and NTUH-YB: 0.994; 95% CI, 0.984-1.000). Furthermore, among those images with ETT, ETT malposition could be detected with high accuracy (AUC: NTUH-20, 0.847; 95% CI, 0.671-0.980 and NTUH-YB, 0.734; 95% CI, 0.630-0.833), especially for endobronchial intubation (AUC: NTUH-20, 0.991; 95% CI, 0.969-1.000 and NTUH-YB, 0.966; 95% CI, 0.933-0.991). CONCLUSIONS: The derived CAD system could localize ETT and detect ETT malposition with excellent performance, especially for endobronchial intubation, and with favorable potential for external generalizability. [ABSTRACT FROM AUTHOR]

According to WHO, lung cancer is one of the major causes of cancer deaths worldwide. Lung nodule detection has substantially improved by using image processing techniques and Computer Aided Diagnosis (CAD) systems. A novel method is presented for processing Postero Anterior chest radiographs for extracting a candidate nodule based on their local properties using circular windowbased local contrast thresholding (CWLCT). The algorithm is tested on different subtlety levels of chest radiographs and compared with square window-based Bernsen local contrast thresholding. For the first test 50 chest radiograph images with different subtlety levels are used. The experimental results of candidate nodule extraction using local contrast thresholding with a circular window show that for 90% of images true positive nodules are extracted accurately with minute error in the size and 4 false positive nodules/image. In the second test, the proposed method is compared with square window-based Bernsen local contrast thresholding and showed a reduction of 3.8 % in nodule size error. Utilizing CWLCT not only lowers the number of false positive nodules per picture but also assists in precisely segmenting nodules from chest radiograph images. [ABSTRACT FROM AUTHOR]

The generalizability of artificial intelligence (AI) models is a major issue in the field of AI applications. Therefore, we aimed to overcome the generalizability problem of an AI model developed for a particular center for pneumothorax detection using a small dataset for external validation. Chest radiographs of patients diagnosed with pneumothorax (n = 648) and those without pneumothorax (n = 650) who visited the Ankara University Faculty of Medicine (AUFM; center 1) were obtained. A deep learning-based pneumothorax detection algorithm (PDA-Alpha) was developed using the AUFM dataset. For implementation at the Health Sciences University (HSU; center 2), PDA-Beta was developed through external validation of PDA-Alpha using 50 radiographs with pneumothorax obtained from HSU. Both PDA algorithms were assessed using the HSU test dataset (n = 200) containing 50 pneumothorax and 150 non-pneumothorax radiographs. We compared the results generated by the algorithms with those of physicians to demonstrate the reliability of the results. The areas under the curve for PDA-Alpha and PDA-Beta were 0.993 (95% confidence interval (CI): 0.985–1.000) and 0.986 (95% CI: 0.962–1.000), respectively. Both algorithms successfully detected the presence of pneumothorax on 49/50 radiographs; however, PDA-Alpha had seven false-positive predictions, whereas PDA-Beta had one. The positive predictive value increased from 0.525 to 0.886 after external validation (p = 0.041). The physicians' sensitivity and specificity for detecting pneumothorax were 0.585 and 0.988, respectively. The performance scores of the algorithms were increased with a small dataset; however, further studies are required to determine the optimal amount of external validation data to fully address the generalizability issue. [ABSTRACT FROM AUTHOR]

Simple Summary: Dynamic chest radiographs offer a distinct advantage over traditional chest radiographs by integrating motion and functional data, elevating their significance in clinical diagnostics. This study introduces a pioneering technique employing deep neural networks to simulate respiratory lung motion and extract local functional details from single-phase chest X-rays, thereby enhancing lung cancer clinical diagnostic capabilities. Our research establishes the viability of generating patient-specific respiratory motion profiles from single-phase chest radiographs. The evaluation of results from the network developed here underscores its substantial accuracy and fidelity, affirming its robustness in providing valuable supplementary insights into pulmonary function. 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. [ABSTRACT FROM AUTHOR]

Purpose: To develop two deep learning-based systems for diagnosing and localizing pneumothorax on portable supine chest X-rays (SCXRs). Methods: For this retrospective study, images meeting the following inclusion criteria were included: (1) patient age ≥ 20 years; (2) portable SCXR; (3) imaging obtained in the emergency department or intensive care unit. Included images were temporally split into training (1571 images, between January 2015 and December 2019) and testing (1071 images, between January 2020 to December 2020) datasets. All images were annotated using pixel-level labels. Object detection and image segmentation were adopted to develop separate systems. For the detection-based system, EfficientNet-B2, DneseNet-121, and Inception-v3 were the architecture for the classification model; Deformable DETR, TOOD, and VFNet were the architecture for the localization model. Both classification and localization models of the segmentation-based system shared the UNet architecture. Results: In diagnosing pneumothorax, performance was excellent for both detection-based (Area under receiver operating characteristics curve [AUC]: 0.940, 95% confidence interval [CI]: 0.907–0.967) and segmentation-based (AUC: 0.979, 95% CI: 0.963–0.991) systems. For images with both predicted and ground-truth pneumothorax, lesion localization was highly accurate (detection-based Dice coefficient: 0.758, 95% CI: 0.707–0.806; segmentation-based Dice coefficient: 0.681, 95% CI: 0.642–0.721). The performance of the two deep learning-based systems declined as pneumothorax size diminished. Nonetheless, both systems were similar or better than human readers in diagnosis or localization performance across all sizes of pneumothorax. Conclusions: Both deep learning-based systems excelled when tested in a temporally different dataset with differing patient or image characteristics, showing favourable potential for external generalizability. [ABSTRACT FROM AUTHOR]

Purpose: Chest x-ray (CXR) is the standard imaging used to evaluate children in acute respiratory distress and failure. Our objective was to compare the lung-imaging techniques of CXR and lung ultrasound (LUS) in the evaluation of children with acute respiratory failure (ARF) to quantify agreement and to determine which technique identified a higher frequency of pulmonary abnormalities. Methods: This was a secondary analysis of a prospective observational study evaluating the sensitivity and specificity of LUS in children with ARF from 12/2018 to 02/2020 completed at the University of Wisconsin-Madison (USA). Children > 37.0 weeks corrected gestational age and ≤ 18 years of age admitted to the PICU with ARF were evaluated with LUS. We compared CXR and LUS completed within 6 h of each other. Kappa statistics (k) adjusted for maximum attainable agreement (k/kmax) were used to quantify agreement between imaging techniques and descriptive statistics were used to describe the frequency of abnormalities. Results: Eighty-eight children had LUS completed, 32 with concomitant imaging completed within 6 h are included. There was fair agreement between LUS and CXR derived diagnoses with 58% agreement (k/kmax = 0.36). Evaluation of imaging patterns included: normal, 57% agreement (k = 0.032); interstitial pattern, 47% agreement (k = 0.003); and consolidation, 65% agreement (k = 0.29). CXR identified more imaging abnormalities than LUS. Conclusions: There is fair agreement between CXR and LUS-derived diagnoses in children with ARF. Given this, clinicians should consider the benefits and limitations of specific imaging modalities when evaluating children with ARF. Additional studies are necessary to further define the role of LUS in pediatric ARF given the small sample size of our study. [ABSTRACT FROM AUTHOR]

Public chest X-ray (CXR) data sets are commonly compressed to a lower bit depth to reduce their size, potentially hiding subtle diagnostic features. In contrast, radiologists apply a windowing operation to the uncompressed image to enhance such subtle features. While it has been shown that windowing improves classification performance on computed tomography (CT) images, the impact of such an operation on CXR classification performance remains unclear. In this study, we show that windowing strongly improves the CXR classification performance of machine learning models and propose WindowNet, a model that learns multiple optimal window settings. Our model achieved an average AUC score of 0.812 compared with the 0.759 score of a commonly used architecture without windowing capabilities on the MIMIC data set. [ABSTRACT FROM AUTHOR]

Currently, the detection of pathology of lung cavities and their digitalization is one of the urgent problems of the healthcare industry in Kazakhstan. In this paper, the method of fractal analysis was considered to solve the task set. Diagnosis of lung pathology based on fractal analysis is an actively developing area of medical research. Conducted experiments on a set of clinical data confirm the effectiveness of the proposed methodology. The results obtained show that fractal analysis can be a useful tool for early detection of lung pathologies. It allows you to detect even minor changes in the structure and texture of lung tissues, which may not be obvious during visual analysis. The article deals with images of pathology of the pulmonary cavity, taken from an open data source. Based on the analysis of fractal objects, they were pre-assembled. Software algorithms for the operation of the information system for screening diagnostics have been developed. Based on the information contained in the fractal image of the lungs, mathematical models have been developed to create a diagnostic rule. A reference set of information features has been created that allows you to create algorithms for diagnosing the lungs: healthy and with pathologies of tuberculosis. [ABSTRACT FROM AUTHOR]

Background: There have been no reports on diagnostic performance of deep learning-based automated detection (DLAD) for thoracic diseases in real-world outpatient clinic. Purpose: To validate DLAD for use at an outpatient clinic and analyze the interpretation time for chest radiographs. Material and Methods: This is a retrospective single-center study. From 18 January 2021 to 18 February 2021, 205 chest radiographs with DLAD and paired chest CT from 205 individuals (107 men and 98 women; mean ± SD age: 63 ± 8 years) from an outpatient clinic were analyzed for external validation and observer performance. Two radiologists independently reviewed the chest radiographs by referring to the paired chest CT and made reference standards. Two pulmonologists and two thoracic radiologists participated in observer performance tests, and the total amount of time taken during the test was measured. Results: The performance of DLAD (area under the receiver operating characteristic curve [AUC] = 0.920) was significantly higher than that of pulmonologists (AUC = 0.756) and radiologists (AUC = 0.782) without assistance of DLAD. With help of DLAD, the AUCs were significantly higher for both groups (pulmonologists AUC = 0.853; radiologists AUC = 0.854). A greater than 50% decrease in mean interpretation time was observed in the pulmonologist group with assistance of DLAD compared to mean reading time without aid of DLAD (from 67 s per case to 30 s per case). No significant difference was observed in the radiologist group (from 61 s per case to 61 s per case). Conclusion: DLAD demonstrated good performance in interpreting chest radiographs of patients at an outpatient clinic, and was especially helpful for pulmonologists in improving performance. [ABSTRACT FROM AUTHOR]

Tuberculosis is a severe and contagious lung disease that kills more than one million people annually, representing a real public health problem worldwide. Meanwhile, researchers and industry have highlighted the potential of analyzing and classifying chest radiographs (CXR) with artificial intelligence (AI). However, although high-performance industry solutions to aid in diagnosing have become a reality, vulnerable populations and underdeveloped countries have struggled to access them. In this sense, accessible and low-cost alternatives are essential to reach the needs of those populations that rely on public actions and services. Therefore, we propose a method to assist in the screening, diagnosing, and classifying of tuberculosis cases with a low-cost and high-efficiency computational approach. Our method uses a novel feature extraction approach based on binary patterns of phase congruence (BPPC) to describe CXR images that are represented in a feature vector of 1062 values. The extraction strategy maintains and recovers descriptive textural information of the TB, captured in 6 different directions of the CXR image, being invariant in the illumination and contrast of the CXR. The vector is then used to build a feed-forward neural network (FFNN) model capable of identifying and classifying TB cases. Our model experimented with three classification scenarios with CXR images from the TBX11K public dataset, and our results achieved exceptional performance, with greater than 99% accuracy, outperforming models from related work.

Objective: To compare and correlate the value of the Haller index calculated with chest x-ray with the values calculated with a Computed Tomography (CT) scan. Study Design: Cross-sectional study. Place and Duration of Study: Department of Radiology, Combined Military Hospital, Peshawar Pakistan, from Jan to Jun 2018. Methodology: A total of 50 male patients aged ≥16 years presenting with pectus excavatum were included. Haller index was calculated after CT scan and X-ray. Results: The mean age of the patients was 20.20±2.34 years. The mean Haller Index for CT scan was 2.35±0.39, and the mean Haller Index for X-ray was 2.01±0.45. A strong and positive correlation existed between the Haller index for CT scans and X-rays (r=0.83). Conclusion: X-rays can be used instead of CT scans to measure the Haller index and plan the surgical intervention with similar accuracy.

PurposeTo assess the potential of a transfer learning strategy leveraging radiologist supervision to enhance convolutional neural network-based (CNN) localization of pneumonia on radiographs and to further assess the prognostic value of CNN severity quantification on patients evaluated for COVID-19 pneumonia, for whom severity on the presenting radiograph is a known predictor of mortality and intubation.Materials and methodsWe obtained an initial CNN previously trained to localize pneumonia along with 25,684 radiographs used for its training. We additionally curated 1466 radiographs from patients who had a computed tomography (CT) performed on the same day. Regional likelihoods of pneumonia were then annotated by cardiothoracic radiologists, referencing these CTs. Combining data, a preexisting CNN was fine-tuned using transfer learning. Whole-image and regional performance of the updated CNN was assessed using receiver-operating characteristic area under the curve and Dice. Finally, the value of CNN measurements was assessed with survival analysis on 203 patients with COVID-19 and compared against modified radiographic assessment of lung edema (mRALE) score.ResultsPneumonia detection area under the curve improved on both internal (0.756 to 0.841) and external (0.864 to 0.876) validation data. Dice overlap also improved, particularly in the lung bases (R: 0.121 to 0.433, L: 0.111 to 0.486). There was strong correlation between radiologist mRALE score and CNN fractional area of involvement (ρ=0.85). Survival analysis showed similar, strong prognostic ability of the CNN and mRALE for mortality, likelihood of intubation, and duration of hospitalization among patients with COVID-19.ConclusionsRadiologist-supervised transfer learning can enhance the ability of CNNs to localize and quantify the severity of disease. Closed-loop systems incorporating radiologists may be beneficial for continued improvement of artificial intelligence algorithms.

Summary: Background & Aims: Malnutrition in the elderly, frequently and significantly affects both physical functioning and cognition, as well as incurs direct and indirect costs to society. Guidelines recommend rapid nutritional intervention and initiation of nutritional therapy within 24–48 hours of admission. Height and weight information is essential for proper nutritional assessment; however, it is difficult to obtain individual the height and weight of bedridden elderly patients directly. This study aimed to illustrate the potential of a convolutional neural network model to assess the height and weight based on chest radiographs. Methods: We retrospectively evaluated radiographs obtained over 15 years of follow-up. Overall, 6,453 radiographs from male patients, and 7,879 from female patients were included in the analysis. A convolutional neural network was used to predict the height and weight of the patients (Juzen NST). A ResNet152 classifier was trained using Fastai (V1.0) running on PyTorch to predict the height and weight. Training was performed for four epochs using validation without augmentation. Results: The correlation coefficients between the predicted and measured values using the height prediction model for males and females were R=0.855 and R=0.81, respectively. The correlation coefficients between the values predicted by the weight prediction model and measured values were R=0.793 and R=0.86, respectively. Conclusion: Our chest radiographic prediction model has a high correlation with actual height and weight and can be combined with information regarding clinical nutrition factors for rapid assessment of risk for malnutrition. By training the prediction model using chest radiographs from each hospital, it can be optimized for the most common ethnic groups in the area. Importantly, our model is an illustration of the potential of automated imaging artificial intelligence for proper nutrition prediction models in older adults.

This study attempts to detect and differentiate Multi Drug Resistant (MDR) - Tuberculosis (TB) and Drug Sensitive (DS)-TB Chest Radiographs (CXR) using local texture descriptors and Ensemble Learning method. Studies report that CXR images contain likelihood information of the drug resistance which can be utilized computationally. Initially, CXR images are subjected to lung fields segmentation using Reaction Diffusion Level Set method. Further, Local Directional Texture Pattern (LDTP) features are extracted from the segmented lungs to characterize the localized textural variations. Extreme Gradient Boosting (XGBoost) classifier is employed to differentiate DS-TB and MDR-TB images. The obtained results demonstrate the ability of extracted LDTP features to characterize nonspecific textural inhomogeneities in images by operating on its principal directions. XGBoost algorithm provides maximum accuracy of 93% and true positive rate of 94.6% in detecting MDR-TB. As the proposed study differentiates the MDR-TB condition using CXR images, its computerized diagnostics could be used in the early screening and followup of TB ridden patients for public health infection control in any setting.

Lung hydatid cysts are easy to diagnose and can be treated with a simple surgical procedure. In lung hydatid cysts, conventional radiographic examinations are generally sufficient for the diagnosis of the disease. A 23-year-old female patient was admitted with complaints of left-sided pain, cough, and clear fluid with a bitter taste in the mouth that came with coughing. A giant cystic lesion was seen on chest radiography and thorax computed tomography (CT). In the external center radiological imaging of the patient examined through the system, it was seen that the cystic lesion was smaller in size and well-circumscribed in the chest radiograph taken 4 years ago. It was determined that the lesion was not noticed at that time, the existing lesion grew over the years and the diagnosis was delayed. Based on the findings, a ruptured lung hydatid cyst was considered. The patient underwent cystotomy and capitonnage through a left thoracotomy. The correct interpretation of radiological imaging methods used in the diagnosis of hydatid cysts is of great importance to prevent serious complications. [ABSTRACT FROM AUTHOR]

The opportunistic use of radiological examinations for disease detection can potentially enable timely management. We assessed if an index created by an AI software to quantify chest radiography (CXR) findings associated with heart failure (HF) could distinguish between patients who would develop HF or not within a year of the examination. Our multicenter retrospective study included patients who underwent CXR without an HF diagnosis. We included 1117 patients (age 67.6 ± 13 years; m:f 487:630) that underwent CXR. A total of 413 patients had the CXR image taken within one year of their HF diagnosis. The rest (n = 704) were patients without an HF diagnosis after the examination date. All CXR images were processed with the model (qXR-HF, Qure.AI) to obtain information on cardiac silhouette, pleural effusion, and the index. We calculated the accuracy, sensitivity, specificity, and area under the curve (AUC) of the index to distinguish patients who developed HF within a year of the CXR and those who did not. We report an AUC of 0.798 (95%CI 0.77–0.82), accuracy of 0.73, sensitivity of 0.81, and specificity of 0.68 for the overall AI performance. AI AUCs by lead time to diagnosis (

The air crescent (AC) is a common radiological sign. Even if its commonest aetiology remains pulmonary aspergillosis, various other causes have been described. In this study, we report four rare causes of ACs seen on chest radiographs that haven’t been described in the literature. Teaching point: The differential diagnosis of an air crescent sign on chest radiographs includes oesophageal bezoar, interstitial lung emphysema, central bronchial stenosis and perforated emphysematous cholecystitis.

Abstract Background Multisystem inflammatory syndrome in children (MIS-C) is a relatively new and rare complication of COVID-19. This complication seems to develop after the infection rather than during the acute phase of COVID-19. This report aims to describe a case of MIS-C in an 8-year-old Thai boy who presented with unilateral lung consolidation. Unilateral whiteout lung is not a common pediatric chest radiograph finding in MIS-C, but this is attributed to severe acute respiratory failure. Case presentation An 8-year-old boy presented with persistent fever for seven days, right cervical lymphadenopathy, and dyspnea for 12 h. The clinical and biochemical findings were compatible with MIS-C. Radiographic features included total opacity of the right lung and CT chest found consolidation and ground-glass opacities of the right lung. He was treated with intravenous immunoglobulin and methylprednisolone, and he dramatically responded to the treatment. He was discharged home in good condition after 8 days of treatment. Conclusion Unilateral whiteout lung is not a common pediatric chest radiographic finding in MIS-C, but when it is encountered, a timely and accurate diagnosis is required to avoid delays and incorrect treatment. We describe a pediatric patient with unilateral lung consolidation from the inflammatory process.

BackgroundPneumoconiosis is the most important occupational disease all over the world, with high prevalence and mortality. At present, the monitoring of workers exposed to dust and the diagnosis of pneumoconiosis rely on manual interpretation of chest radiographs, which is subjective and low efficiency. With the development of artificial intelligence technology, a more objective and efficient computer aided system for pneumoconiosis diagnosis can be realized. Therefore, the present study reported a novel deep learning (DL) artificial intelligence (AI) system for detecting pneumoconiosis in digital frontal chest radiographs, based on which we aimed to provide references for radiologists.MethodsWe annotated 49,872 chest radiographs from patients with pneumoconiosis and workers exposed to dust using a self-developed tool. Next, we used the labeled images to train a convolutional neural network (CNN) algorithm developed for pneumoconiosis screening. Finally, the performance of the trained pneumoconiosis screening model was validated using a validation set containing 495 chest radiographs.ResultsApproximately, 51% (25,435/49,872) of the chest radiographs were labeled as normal. Pneumoconiosis was detected in 49% (24,437/49,872) of the labeled radiographs, among which category-1, category-2, and category-3 pneumoconiosis accounted for 53.1% (12,967/24,437), 20.4% (4,987/24,437), and 26.5% (6,483/24,437) of the patients, respectively. The CNN DL algorithm was trained using these data. The validation set of 495 digital radiography chest radiographs included 261 cases of pneumoconiosis and 234 cases of non-pneumoconiosis. As a result, the accuracy of the AI system for pneumoconiosis identification was 95%, the area under the curve was 94.7%, and the sensitivity was 100%.ConclusionDL algorithm based on CNN helped screen pneumoconiosis in the chest radiographs with high performance; thus, it could be suitable for diagnosing pneumoconiosis automatically and improve the efficiency of radiologists.

OBJECTIVES Epithelioid Hemangioendothelioma is an infrequent vascular neoplasm of intermediate malignant potential oddly affecting children. It is primarily noted in soft tissues, the stomach, breast, spleen, brain, and liver. No definite risk factor is identified in children. However, following WWTR1-CAMTA1 and YAP1-TFE3 gene fusions are frequently seen in these tumours. We present a case of very rare childhood Epithelioid Hemangioendothelioma of the liver in a ten-year-old boy. A 10-year-old boy presented with abdominal pain, mild weight loss, and abnormal liver function tests. Radiologically, hepatic, pulmonary, and bony lesions are noted. The biopsy report showed Epithelioid Hemangioendothelioma confirmed by an Immunohistochemical panel. Due to the lack of facilities in our centre, palliative treatment was given to the patient. Surgical resection, liver transplant, and radiofrequency ablation were impossible due to widespread hepatic and pulmonary disease. The patient was resistant to any medical treatment. The patient died at the seventh-month follow-up. HEHE is a scarcely seen tumour with no definite management protocol. Surgical resection is the preferred treatment for resectable tumours. In non-resectable extensive bifocal tumours, like in our case, the preferred treatment is radio-frequency ablation and hepatic transplant. The overall survival is trivial due to the non-compliant nature of the disease.

Objective: To determine the diagnostic accuracy of lung ultrasound (LUS) in the diagnosis of pneumonia in children using chest x-ray as the gold standard. Study Design: Comparative cross-sectional study. Place and Duration of Study: Department of Radiology, Combined Military Hospital, Sialkot Pakistan, from Feb to Aug 2021 Methodology: All patients underwent clinical examination, chest radiograph (anterior-posterior view), lung ultrasound and blood sample analysis in the first 24 hours after admission. A pre-specified hospital radiologist carried out a lung ultrasound. The examination was performed with the patient sitting upright or supine. The presence of pneumonia on x-ray and USG were recorded. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy of lung ultrasound in the diagnosis of pneumonia by taking chest x-ray as the gold standard was calculated in all the study participants. Results: In this study, 262 patients with clinical features of pneumonia were enrolled. The mean age of the study participants was 3.27±2.344 years. Sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), accuracy and frequency of lung ultrasonography (US) vs chest radiography (CR) in diagnosing pneumonia was 95.28%, 87.5%, 99.59%, 36.84%, 95.04% and 96.95% respectively. Conclusion: Lung ultrasound emerged as a sensitive diagnostic tool to diagnose pneumonia in children. Sensitivity, specificity, positive predictive value, and negative predictive value all favoured the use of this modality to diagnose pneumonia in paediatric patients.

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. [ABSTRACT FROM AUTHOR]

Background: Historically, chest radiographs (CXR) have been used to quickly diagnose pneumothorax (PTX) and hemothorax in trauma patients. Over the last 2 decades, chest ultrasound (CUS) as part of Extended Focused Assessment with Sonography in Trauma (eFAST) has also become accepted as a modality for the early diagnosis of PTX in trauma patients. Methods: We queried our institution's trauma databases for all trauma team activations from 2021 for patients with eFAST results. Demographics, injury variables, and the following were collected: initial eFAST CUS, CXR, computed tomography (CT) scan, and thoracostomy tube procedure notes. We then compared PTX detection rates on initial CXR and CUS to those on thoracic CT scans. Results: 580 patients were included in the analysis after excluding patients without a chest CT scan within 2 hours of arrival. Extended Focused Assessment with Sonography in Trauma was 68.4% sensitive and 87.5% specific for detecting a moderate-to-large PTX on chest CT, while CXR was 23.5% sensitive and 86.3% specific. Extended Focused Assessment with Sonography in Trauma was 69.8% sensitive for predicting the need for tube thoracostomy, while CXR was 40.0% sensitive. Discussion: At our institution, eFAST CUS was superior to CXR for diagnosing the presence of a PTX and predicting the need for a thoracostomy tube. However, neither test is accurate enough to diagnose a PTX nor predict if the patient will require a thoracostomy tube. Based on the specificity of both tests, a negative CXR or eFAST means there is a high probability that the patient does not have a PTX and will not need a chest tube. [ABSTRACT FROM AUTHOR]