Association between chest X-ray score and clinical outcome in COVID-19 patients: A study on modified radiographic assessment of lung edema score (mRALE) in Indonesia.

Radiological examinations such as chest X-rays (CXR) play a crucial role in the early diagnosis and determining disease severity in coronavirus disease 2019 (COVID-19). Various CXR scoring systems have been developed to quantitively assess lung abnormalities in COVID-19 patients, including CXR modified radiographic assessment of lung edema (mRALE). The aim of this study was to determine the relationship between mRALE scores and clinical outcome (mortality), as well as to identify the correlation between mRALE score and the severity of hypoxia (PaO2/FiO2 ratio). A retrospective cohort study was conducted among hospitalized COVID-19 patients at Dr. Soetomo General Academic Hospital Surabaya, Indonesia, from February to April 2022. All CXR data at initial admission were scored using the mRALE scoring system, and the clinical outcomes at the end of hospitalization were recorded. Of the total 178 COVID-19 patients, 62.9% survived after completing the treatment. Patients within non-survived had significantly higher quick sequential organ failure assessment (qSOFA) score (p<0.001), lower PaO2/FiO2 ratio (p=0.004), and higher blood urea nitrogen (p<0.001), serum creatinine (p<0.008) and serum glutamic oxaloacetic transaminase (p=0.001) levels. There was a significant relationship between mRALE score and clinical outcome (survived vs deceased) (p=0.024; contingency coefficient of 0.184); and mRALE score of ≥2.5 served as a risk factor for mortality among COVID-19 patients (relative risk of 1.624). There was a significant negative correlation between the mRALE score and PaO2/FiO2 ratio based on the Spearman correlation test (r=-0.346; p<0.001). The findings highlight that the initial mRALE score may serve as an independent predictor of mortality among hospitalized COVID-19 patients as well as proves its potential prognostic role in the management of COVID-19.

Diagnostic performance of chest CT average intensity projection (AIP) reconstruction for the assessment of pleuro-parenchymal abnormalities.

AIM: The comparison between chest x-ray (CXR) and computed tomography (CT) images is commonly required in clinical practice to assess the evolution of chest pathological manifestations. Intrinsic differences between the two techniques, however, limit reader confidence in such a comparison. CT average intensity projection (AIP) reconstruction allows obtaining "synthetic" CXR (s-CXR) images, which are thought to have the potential to increase the accuracy of comparison between CXR and CT imaging. We aim at assessing the diagnostic performance of s-CXR imaging in detecting common pleuro-parenchymal abnormalities. MATERIALS AND METHODS: 142 patients who underwent chest CT examination and CXR within 24 hours were enrolled. CT was the standard of reference. Both conventional CXR (c-CXR) and s-CXR images were retrospectively reviewed for the presence of consolidation, nodule/mass, linear opacities, reticular opacities, and pleural effusion by 3 readers in two separate sessions. Sensitivity, specificity, accuracy and their 95% confidence interval were calculated for each reader and setting and tested by McNemar test. Inter-observer agreement was tested by Cohen's K test and its 95%CI. RESULTS: Overall, s-CXR sensitivity ranged 45-67% for consolidation, 12-28% for nodule/mass, 17-33% for linear opacities, 2-61% for reticular opacities, and 33-58% for pleural effusion; specificity 65-83%, 83-94%, 94-98%, 93-100% and 79-86%; accuracy 66-68%, 74-79%, 89-91%, 61-65% and 68-72%, respectively. K values ranged 0.38-0.50, 0.05-0.25, -0.05-0.11, -0.01-0.15, and 0.40-0.66 for consolidation, nodule/mass, linear opacities, reticular opacities, and pleural effusion, respectively. CONCLUSION: S-CXR images, reconstructed with AIP technique, can be compared with conventional images in clinical practice and for educational purposes.

Chest CT-based automated vertebral fracture assessment using artificial intelligence and morphologic features.

BACKGROUND: Spinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD. PURPOSE: We present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi-parametric freeze-and-grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts. METHODS: A chest CT-based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant et al. The computational method was accomplished in the following steps: (1) computation of a voxel-level vertebral body likelihood map from chest CT using a trained DL network; (2) delineation and labelling of individual vertebrae on the likelihood map using an iterative multi-parametric FG algorithm; (3) separation of apparently fused vertebrae in CT using intensity autocorrelation; (4) computation of vertebral heights using contour analysis on the central anterior-posterior (AP) plane of a vertebral body; (5) assessment of vertebral fracture status using ratio functions of vertebral heights and optimized thresholds. The method was applied to inspiratory or total lung capacity (TLC) chest scans from the multi-site Genetic Epidemiology of COPD (COPDGene) (ClinicalTrials.gov: NCT00608764) study, and the performance was examined (n = 3231). One hundred and twenty scans randomly selected from this dataset were partitioned into training (n = 80) and validation (n = 40) datasets for the DL-based vertebral body classifier. Also, generalizability of the method to low dose CT imaging (n = 236) was evaluated. RESULTS: The vertebral segmentation module achieved a Dice score of .984 as compared to manual outlining results as reference (n = 100); the segmentation performance was consistent across images with the minimum and maximum of Dice scores among images being .980 and .989, respectively. The vertebral labelling module achieved 100% accuracy (n = 100). For low dose CT, the segmentation module produced image-level minimum and maximum Dice scores of .995 and .999, respectively, as compared to standard dose CT as the reference; vertebral labelling at low dose CT was fully consistent with standard dose CT (n = 236). The fracture assessment method achieved overall accuracy, sensitivity, and specificity of 98.3%, 94.8%, and 98.5%, respectively, for 40,050 vertebrae from 3231 COPDGene participants. For generalizability experiments, fracture assessment from low dose CT was consistent with the reference standard dose CT results across all participants. CONCLUSIONS: Our CT-based automated method for vertebral fracture assessment is accurate, and it offers a feasible alternative to manual expert reading, especially for large population-based studies, where automation is important for high efficiency. Generalizability of the method to low dose CT imaging further extends the scope of application of the method, particularly since the usage of low dose CT imaging in large population-based studies has increased to reduce cumulative radiation exposure.

Artificial intelligence in the healthcare sector: comparison of deep learning networks using chest X-ray images.

Purpose: Artificial intelligence has led to significant developments in the healthcare sector, as in other sectors and fields. In light of its significance, the present study delves into exploring deep learning, a branch of artificial intelligence. Methods: In the study, deep learning networks ResNet101, AlexNet, GoogLeNet, and Xception were considered, and it was aimed to determine the success of these networks in disease diagnosis. For this purpose, a dataset of 1,680 chest X-ray images was utilized, consisting of cases of COVID-19, viral pneumonia, and individuals without these diseases. These images were obtained by employing a rotation method to generate replicated data, wherein a split of 70 and 30% was adopted for training and validation, respectively. Results: The analysis findings revealed that the deep learning networks were successful in classifying COVID-19, Viral Pneumonia, and Normal (disease-free) images. Moreover, an examination of the success levels revealed that the ResNet101 deep learning network was more successful than the others with a 96.32% success rate. Conclusion: In the study, it was seen that deep learning can be used in disease diagnosis and can help experts in the relevant field, ultimately contributing to healthcare organizations and the practices of country managers.

Assessment of the comparative agreement between chest radiographs and CT scans in intensive care units.

PURPOSE: Chest radiographs in critically ill patients can be difficult to interpret due to technical and clinical factors. We sought to determine the agreement of chest radiographs and CT scans, and the inter-observer variation of chest radiograph interpretation, in intensive care units (ICUs). METHODS: Chest radiographs and corresponding thoracic computerised tomography (CT) scans (as reference standard) were collected from 45 ICU patients. All radiographs were analysed by 20 doctors (radiology consultants, radiology trainees, ICU consultants, ICU trainees) from 4 different centres, blinded to CT results. Specificity/sensitivity were determined for pleural effusion, lobar collapse and consolidation/atelectasis. Separately, Fleiss' kappa for multiple raters was used to determine inter-observer variation for chest radiographs. RESULTS: The median sensitivity and specificity of chest radiographs for detecting abnormalities seen on CTs scans were 43.2% and 85.9% respectively. Diagnostic sensitivity for pleural effusion was significantly higher among radiology consultants but no specialty/experience distinctions were observed for specificity. Median inter-observer kappa coefficient among assessors was 0.295 ("fair"). CONCLUSIONS: Chest radiographs commonly miss important radiological features in critically ill patients. Inter-observer agreement in chest radiograph interpretation is only "fair". Consultant radiologists are least likely to miss thoracic radiological abnormalities. The consequences of misdiagnosis by chest radiographs remain to be determined.

Core and Advanced Thoracic Imaging Curricula to Guide Residency and Fellowship Training: A Framework From the Canadian Society of Thoracic Radiology.

RATIONALE: A well-defined curriculum with goals and objectives is an inherent part of every radiology training program. MATERIALS AND METHODS: Following a needs assessment, the Canadian Society of Thoracic Radiology Education Committee developed a thoracic imaging curriculum using a mixed- method approach, complimentary to the cardiac curriculum published as a separate document. RESULTS: The Thoracic Imaging Curriculum consists of two separate yet complimentary parts: a Core Curriculum, aimed at residents in-training, with the main goal of building a strong foundational knowledge, and an Advanced Curriculum, designed to build upon the core knowledge and guide a more in-depth subspecialty training. CONCLUSION: The curricular frameworks aim to enhance the educational experience of residents and fellows and provide an educational framework for clinical supervisors and residency and fellowship program directors. SUMMARY STATEMENT: The Canadian Society of Thoracic Radiology championed the creation of Cardiovascular and Thoracic Imaging curricula encompassing clinical knowledge and technical, communication, and decision-making skills with the goal of providing direction to a strong foundational knowledge for residents and to guide specialty training for fellowship programs.

Alternativas de procesos para la búsqueda de casos de tuberculosis bajo condiciones económicas limitadas. Cuba 2022-2025 / Process Alternatives for the Tuberculosis Case Finding under Limited Economic Conditions. Cuba 2022-2025

Ejecutar procesos efectivos de búsqueda de casos de tuberculosis es crucial para acele-rar el paso hacia su eliminación. El empeoramiento de las condiciones económicas mun-diales y nacionales no nos permite aplicar extensivamente las tecnologías rápidas mo-leculares idóneas de diagnóstico. Consideramos sensato entonces aplicar algoritmos alternativos que satisfagan las necesidades nacionales presentes hasta que las condi-ciones permitan la cobertura completa de las tecnologías moleculares recomendadas. Sugerimos introducir la radiografía digital para todos los algoritmos, utilizar mejor la microscopía de fluorescencia LED y la óptica convencional ya probadas. En conclusión, es preciso que este enfoque de trabajo, que procura optimizar la efectividad y eficiencia del programa, se introduzca en la práctica cotidiana hasta que lo idóneo sea permisible

Executing effective tuberculosis case-finding processes is crucial to accelerate the path towards elimination of the disease. The worsening of global and national economic conditions do not allow us to extensively apply rapid molecular diagnostic technolo-gies. We consider it sensible and necessary to apply alternative algorithms that meet the current national needs, until conditions allow full coverage of the recommended molecular technologies. We suggest introducing digital X-rays for all algorithms, bet-ter use of LED fluorescence microscopy and conventional optics already appropriate-ly tested. In conclusion, it is necessary that this approach that seeks to optimize the effectiveness and efficiency of the Cuban program be introduced into daily practice until the ideal is permissible

Effect of the Extended Focused Assessment With Sonography for Trauma on the Screening Performance of the National Emergency X-Radiography Utilization Study Chest Decision Instrument.

STUDY OBJECTIVE: Developed to decrease unnecessary thoracic computed tomography use in adult blunt trauma patients, the National Emergency X-Radiography Utilization Study (NEXUS) Chest clinical decision instrument does not include the extended Focused Assessment with Sonography in Trauma (eFAST). We assessed whether eFAST improves the NEXUS Chest clinical decision instrument's diagnostic performance and may replace the chest radiograph (CXR) as a predictor variable. METHODS: We performed a secondary analysis of prospective data from 8 Level I trauma centers from 2011-2014. We compared performance of modified clinical decision instruments that (1) added eFAST as a predictor (eFAST-added clinical decision instrument), and (2) replaced CXR with eFAST (eFAST-replaced clinical decision instrument), in screening for blunt thoracic injuries. RESULTS: One thousand nine hundred fifty-seven patients had documented computed tomography, CXR, clinical NEXUS criteria, and adequate eFAST; 624 (31.9%) patients had blunt thoracic injuries, and 126 (6.4%) had major injuries. Compared to the NEXUS Chest clinical decision instrument, the eFAST-added clinical decision instrument demonstrated unchanged screening performance for major injury (sensitivity 0.98 [0.94 to 1.00], specificity 0.28 [0.26 to 0.30]) or any injury (sensitivity 0.97 [0.95 to 0.98], specificity 0.21 [0.19 to 0.23]). The eFAST-replaced clinical decision instrument demonstrated unchanged sensitivity for major injury (sensitivity 0.93 [0.87 to 0.97], specificity 0.31 [0.29 to 0.34]) and decreased sensitivity for any injury (0.93 [0.91 to 0.951] versus 0.97 [0.953 to 0.98]). CONCLUSION: In our secondary analysis, adding eFAST as a predictor variable did not improve the diagnostic screening performance of the original NEXUS Chest clinical decision instrument; eFAST cannot replace the CXR criterion of the NEXUS Chest clinical decision instrument.

Abdominal Imaging Findings on Computed Tomography as a Tool for COVID-19 Mortality Risk Assessment: Comparison With Chest Radiograph Severity Scores.

OBJECTIVE: To quantify the association between computed tomography abdomen and pelvis with contrast (CTAP) findings and chest radiograph (CXR) severity score, and the incremental effect of incorporating CTAP findings into predictive models of COVID-19 mortality. METHODS: This retrospective study was performed at a large quaternary care medical center. All adult patients who presented to our institution between March and June 2020 with the diagnosis of COVID-19 and had a CXR up to 48 hours before a CTAP were included. Primary outcomes were the severity of lung disease before CTAP and mortality within 14 and 30 days. Logistic regression models were constructed to quantify the association between CXR score and CTAP findings. Penalized logistic regression models and random forests were constructed to identify key predictors (demographics, CTAP findings, and CXR score) of mortality. The discriminatory performance of these models, with and without CTAP findings, was summarized using area under the characteristic (AUC) curves. RESULTS: One hundred ninety-five patients (median age, 63 years; 119 men) were included. The odds of having CTAP findings was 3.89 times greater when a CXR score was classified as severe compared with mild (P = 0.002). When CTAP findings were included in the feature set, the AUCs for 14-day mortality were 0.67 (penalized logistic regression) and 0.71 (random forests). Similar values for 30-day mortality were 0.76 and 0.75. When CTAP findings were omitted, all AUC values were attenuated. CONCLUSIONS: The CTAP findings were associated with more severe CXR score and may serve as predictors of COVID-19 mortality.

Enhanced visualization of mobile chest X-ray images in the intensive care setting using software scatter correction.

BACKGROUND: Mobile chest X-ray (CXR) scans are performed within intensive treatment units (ITU) without anti-scatter grids for confirming tube and line hardware placement. Assessment is therefore challenging due to degraded subject contrast resulting from scatter. PURPOSE: To evaluate the efficacy of a software scatter correction method (commercially named Trueview) for enhanced hardware visualization and diagnostic quality in the ITU setting. MATERIAL AND METHODS: A total of 30 CXR scans were processed using Trueview and compared with standard original equipment manufacturer (OEM) images via observer scoring study involving two radiology and four ITU doctors to compare visualization of tubes and lines. Results were analyzed to determine observer preference and likelihood of diagnostic quality. RESULTS: Reviewers were more likely to score Trueview higher than OEM for mediastinal structures, bones, retrocardiac region, tube visibility, and tube safety (P < 0.01). Visual grading characteristic analysis suggested a clinical preference for Trueview compared with OEM for mediastinal structures (area under the visual grading characteristic curve [AUCVGC] = 0.60, 95% confidence interval [CI] = 0.55-0.65), bones (AUCVGC = 0.61, 95% CI = 0.55-0.66), retrocardiac region (AUCVGC = 0.64, 95% CI = 0.59-0.69), tube visibility (AUCVGC = 0.65, 95% CI = 0.60-0.70), and tube safety (AUCVGC = 0.68, 95% CI = 0.64-0.73). Reviewers were indifferent to visualization of the lung fields (AUCVGC = 0.49, 95% CI = 0.44-0.55). Registrars (3/6 reviewers) were indifferent to the mediastinal structure regions (AUCVGC = 0.54, 95% CI = 0.47-0.62). CONCLUSION: Reviewers were more confident in identifying the placement and safety of tubes and lines when reviewing Trueview images than they were when reviewing OEM.

Routine chest CT combined with the osteoporosis self-assessment tool for Asians (OSTA): a screening tool for patients with osteoporosis.

INTRODUCTION: The osteoporosis self-assessment tool for Asians (OSTA) is a common screening tool for osteoporosis. The seventh thoracic CT (CT-T7) Hounsfield unit (HU) measured by chest CT correlates with osteoporosis. This study aimed to investigate the diagnostic value of OSTA alone, CT-T7 alone, or the combination of OSTA and CT-T7 in osteoporosis. MATERIALS AND METHODS: In this study, 1268 participants were grouped into 586 men and 682 women. We established multiple linear regression models by combining CT-T7 and OSTA. Receiver operating characteristic (ROC) curves were used to evaluate the ability to diagnose osteoporosis. RESULTS: In the male group, the mean age was 59.02 years, and 108 patients (18.4%) had osteoporosis. In the female group, the mean age was 63.23 years, and 308 patients (45.2%) had osteoporosis. By ROC curve comparison, the CT-T7 (male, AUC = 0.789, 95% CI 0.745-0.832; female, AUC = 0.835, 95% CI 0.805-0.864) in the diagnosis of osteoporosis was greater than the OSTA (male, AUC = 0.673, 95% CI 0.620-0.726; female, AUC = 0.775, 95% CI 0.741-0.810) in both the male and female groups (p < 0.001). When OSTA was combined with CT, the equation of multiple linear regression (MLR) was obtained as follows: female = 3.020-0.028*OSTA-0.004*CT-T7. In the female group, it was found that the AUC of MLR (AUC = 0.853, 95% CI 0.825-0.880) in the diagnosis of osteoporosis was larger than that of CT-T7 (p < 0.01). When the MLR was 2.65, the sensitivity and specificity were 53.9% and 90%, respectively. CONCLUSION: For a patient who has completed chest CT, CT-T7 (HU) combined with OSTA is recommended to identify the high-risk population of osteoporosis, and it has a higher diagnostic value than OSTA alone or CT-T7 alone, especially among females. For a female with MLR greater than 2.65, further DXA examination is needed.

EFFECT OF PELVIC LEAD SHIELD ON ORGAN DOSES IN POSTERO-ANTERIOR CHEST RADIOGRAPHY.

Lead shields are commonly used in X-ray imaging to protect radiosensitive organs and to minimise patient's radiation dose. However, they might also complicate or interfere with the examination, and even decrease the diagnostic value if they are positioned incorrectly. In this study, the radiation dose effect of waist half-apron lead shield was examined via Monte Carlo simulations of postero-anterior (PA) chest radiography examinations using a female anthropomorphic phantom. Relevant organs for dose determination were lungs, breasts, liver, kidneys and uterus. The organ dose reductions varied depending on shield position and organ but were negligible for properly positioned shields. The shield that had the largest effective dose reduction (9%) was partly positioned inside the field of view, which should not be done in practice. Dose reduction was practically 0% for properly positioned shields. Therefore, the use of lead shield in the pelvic region during chest PA examinations should be discontinued.

Economic Evaluation of Ultrasound-guided Central Venous Catheter Confirmation vs Chest Radiography in Critically Ill Patients: A Labor Cost Model.

INTRODUCTION: Despite evidence suggesting that point-of-care ultrasound (POCUS) is faster and non-inferior for confirming position and excluding pneumothorax after central venous catheter (CVC) placement compared to traditional radiography, millions of chest radiographs (CXR) are performed annually for this purpose. Whether the use of POCUS results in cost savings compared to CXR is less clear but could represent a relative advantage in implementation efforts. Our objective in this study was to evaluate the labor cost difference for POCUS-guided vs CXR-guided CVC position confirmation practices. METHODS: We developed a model to evaluate the per patient difference in labor cost between POCUS-guided vs CXR-guided CVC confirmation at our local urban, tertiary academic institution. We used internal cost data from our institution to populate the variables in our model. RESULTS: The estimated labor cost per patient was $18.48 using CXR compared to $14.66 for POCUS, resulting in a net direct cost savings of $3.82 (21%) per patient using POCUS for CVC confirmation. CONCLUSION: In this study comparing the labor costs of two approaches for CVC confirmation, the more efficient alternative (POCUS-guided) is not more expensive than traditional CXR. Performing an economic analysis framed in terms of labor costs and work efficiency may influence stakeholders and facilitate earlier adoption of POCUS for CVC confirmation.

Deep Convolutional Neural Network Mechanism Assessment of COVID-19 Severity.

As an epidemic, COVID-19's core test instrument still has serious flaws. To improve the present condition, all capabilities and tools available in this field are being used to combat the pandemic. Because of the contagious characteristics of the unique coronavirus (COVID-19) infection, an overwhelming comparison with patients queues up for pulmonary X-rays, overloading physicians and radiology and significantly impacting the quality of care, diagnosis, and outbreak prevention. Given the scarcity of clinical services such as intensive care and motorized ventilation systems in the aspect of this vastly transmissible ailment, it is critical to categorize patients as per their risk categories. This research describes a novel use of the deep convolutional neural network (CNN) technique to COVID-19 illness assessment seriousness. Utilizing chest X-ray images as contribution, an unsupervised DCNN model is constructed and suggested to split COVID-19 individuals into four seriousness classrooms: low, medium, serious, and crucial with an accuracy level of 96 percent. The efficiency of the DCNN model developed with the proposed methodology is demonstrated by empirical findings on a suitably huge sum of chest X-ray scans. To the evidence relating, it is the first COVID-19 disease incidence evaluation research with four different phases, to use a reasonably high number of X-ray images dataset and a DCNN with nearly all hyperparameters dynamically adjusted by the variable selection optimization task.

COVID-19 pneumonia chest radiographic severity score: variability assessment among experienced and in-training radiologists and creation of a multireader composite score database for artificial intelligence algorithm development.

OBJECTIVE: The purpose was to evaluate reader variability between experienced and in-training radiologists of COVID-19 pneumonia severity on chest radiograph (CXR), and to create a multireader database suitable for AI development. METHODS: In this study, CXRs from polymerase chain reaction positive COVID-19 patients were reviewed. Six experienced cardiothoracic radiologists and two residents classified each CXR according to severity. One radiologist performed the classification twice to assess intraobserver variability. Severity classification was assessed using a 4-class system: normal (0), mild (1), moderate (2), and severe (3). A median severity score (Rad Med) for each CXR was determined for the six radiologists for development of a multireader database (XCOMS). Kendal Tau correlation and percentage of disagreement were calculated to assess variability. RESULTS: A total of 397 patients (1208 CXRs) were included (mean age, 60 years SD ± 1), 189 men). Interobserver variability between the radiologists ranges between 0.67 and 0.78. Compared to the Rad Med score, the radiologists show good correlation between 0.79-0.88. Residents show slightly lower interobserver agreement of 0.66 with each other and between 0.69 and 0.71 with experienced radiologists. Intraobserver agreement was high with a correlation coefficient of 0.77. In 220 (18%), 707 (59%), 259 (21%) and 22 (2%) CXRs there was a 0, 1, 2 or 3 class-difference. In 594 (50%) CXRs the median scores of the residents and the radiologists were similar, in 578 (48%) and 36 (3%) CXRs there was a 1 and 2 class-difference. CONCLUSION: Experienced and in-training radiologists demonstrate good inter- and intraobserver agreement in COVID-19 pneumonia severity classification. A higher percentage of disagreement was observed in moderate cases, which may affect training of AI algorithms. ADVANCES IN KNOWLEDGE: Most AI algorithms are trained on data labeled by a single expert. This study shows that for COVID-19 X-ray severity classification there is significant variability and disagreement between radiologist and between residents.

Automated quality assessment of chest radiographs based on deep learning and linear regression cascade algorithms.

OBJECTIVES: Develop and evaluate the performance of deep learning and linear regression cascade algorithms for automated assessment of the image layout and position of chest radiographs. METHODS: This retrospective study used 10 quantitative indices to capture subjective perceptions of radiologists regarding image layout and position of chest radiographs, including the chest edges, field of view (FOV), clavicles, rotation, scapulae, and symmetry. An automated assessment system was developed using a training dataset consisting of 1025 adult posterior-anterior chest radiographs. The evaluation steps included: (i) use of a CNN framework based on ResNet - 34 to obtain measurement parameters for quantitative indices and (ii) analysis of quantitative indices using a multiple linear regression model to obtain predicted scores for the layout and position of chest radiograph. In the testing dataset (n = 100), the performance of the automated system was evaluated using the intraclass correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute difference (MAD), and mean absolute percentage error (MAPE). RESULTS: The stepwise regression showed a statistically significant relationship between the 10 quantitative indices and subjective scores (p < 0.05). The deep learning model showed high accuracy in predicting the quantitative indices (ICC = 0.82 to 0.99, r = 0.69 to 0.99, MAD = 0.01 to 2.75). The automatic system provided assessments similar to the mean opinion scores of radiologists regarding image layout (MAPE = 3.05%) and position (MAPE = 5.72%). CONCLUSIONS: Ten quantitative indices correlated well with the subjective perceptions of radiologists regarding the image layout and position of chest radiographs. The automated system provided high performance in measuring quantitative indices and assessing image quality. KEY POINTS: • Objective and reliable assessment for image quality of chest radiographs is important for improving image quality and diagnostic accuracy. • Deep learning can be used for automated measurements of quantitative indices from chest radiographs. • Linear regression can be used for interpretation-based quality assessment of chest radiographs.

Organ doses in preterm and full-term neonates and infants - a retrospective study on 1,064 chest radiographs.

BACKGROUND: Chest radiography is the most frequent X-ray examination performed in the neonatal period. However, commonly used dosimetric entities do not describe the radiation risk sufficiently. OBJECTIVE: The aim of this study was to investigate selected organ doses and total body dose of chest radiographs in preterm and full-term neonates and infants. MATERIALS AND METHODS: In this retrospective study, we evaluated 1,064 chest radiographs of 136 preterm and 305 full-term babies with respect to field size and centering. We calculated the entrance dose from the dose-area product. Upper and lower field borders referred to the corresponding vertebrae. We calculated individual organ doses of the thyroid, the breast, the liver and active bone marrow for each chest radiograph using the neonatal PCXMC program, a Monte Carlo program for calculating patient doses in medical X-ray examinations. RESULTS: The median field size of chest radiographs ranged from 90 cm2 in preterm neonates at birth to 290 cm2 in full-term infants at the age of 6 months. Median values of entrance dose varied, depending on age, from 15 µGy to 25 µGy. The median organ doses ranged 1-20 µSv for the thyroid, 3-30 µSv for the breast, 2-20 µSv for the liver and 0.5-3.5 µSv for the bone marrow in preterm and full-term neonates and infants, respectively. CONCLUSION: The analysis of chest radiographs in preterm and full-term neonates and infants revealed high variability in field size. By contrast, the entrance dose varied to a minor extent. Organ dose calculations using the PCXMC program might be a valuable tool to calculate the individual radiation risk in neonates and infants.

Qualitative and Quantitative Assessment of Emphysema Using Dark-Field Chest Radiography.

Background Dark-field chest radiography allows for assessment of lung alveolar structure by exploiting wave optical properties of x-rays. Purpose To evaluate the qualitative and quantitative features of dark-field chest radiography in participants with pulmonary emphysema as compared with those in healthy control subjects. Materials and Methods In this prospective study conducted from October 2018 to October 2020, participants aged at least 18 years who underwent clinically indicated chest CT were screened for participation. Inclusion criteria were an ability to consent to the procedure and stand upright without help. Exclusion criteria were pregnancy, serious medical conditions, and any lung condition besides emphysema that was visible on CT images. Participants were examined with a clinical dark-field chest radiography prototype that simultaneously acquired both attenuation-based radiographs and dark-field chest radiographs. Dark-field coefficients were tested for correlation with each participant's CT-based emphysema index using the Spearman correlation test. Dark-field coefficients of adjacent groups in the semiquantitative Fleischner Society emphysema grading system were compared using a Wilcoxon Mann-Whitney U test. The capability of the dark-field coefficient to enable detection of emphysema was evaluated with receiver operating characteristics curve analysis. Results A total of 83 participants (mean age, 65 years ± 12 [standard deviation]; 52 men) were studied. When compared with images from healthy participants, dark-field chest radiographs in participants with emphysema had a lower and inhomogeneous dark-field signal intensity. The locations of focal signal intensity loss on dark-field images corresponded well with emphysematous areas found on CT images. The dark-field coefficient was negatively correlated with the quantitative CT-based emphysema index (r = -0.54, P < .001). Participants with Fleischner Society grades of mild, moderate, confluent, or advanced destructive emphysema exhibited a lower dark-field coefficient than those without emphysema (eg, 1.3 m-1 ± 0.6 for participants with confluent or advanced destructive emphysema vs 2.6 m-1 ± 0.4 for participants without emphysema; P < .001). The area under the receiver operating characteristic curve for detection of mild emphysema was 0.79. Conclusion Pulmonary emphysema leads to reduced signal intensity on dark-field chest radiographs, showing the technique has potential as a diagnostic tool in the assessment of lung diseases. © RSNA, 2022 See also the editorial by Hatabu and Madore in this issue.

Emergency room comprehensive assessment of demographic, radiological, laboratory and clinical data of patients with COVID-19: determination of its prognostic value for in-hospital mortality.

Mortality risk in COVID-19 patients is determined by several factors. The aim of our study was to adopt an integrated approach based on clinical, laboratory and chest x-ray (CXR) findings collected at the patient's admission to Emergency Room (ER) to identify prognostic factors. Retrospective study on 346 consecutive patients admitted to the ER of two North-Western Italy hospitals between March 9 and April 10, 2020 with clinical suspicion of COVID-19 confirmed by reverse transcriptase-polymerase reaction chain test (RT-PCR), CXR performed within 24 h (analyzed with two different scores) and recorded prognosis. Clinical and laboratory data were collected. Statistical analysis on the features of 83 in-hospital dead vs 263 recovered patients was performed with univariate (uBLR), multivariate binary logistic regression (mBLR) and ROC curve analysis. uBLR identified significant differences for several variables, most of them intertwined by multiple correlations. mBLR recognized as significant independent predictors for in-hospital mortality age > 75 years, C-reactive protein (CRP) > 60 mg/L, PaO2/FiO2 ratio (P/F) < 250 and CXR "Brixia score" > 7. Among the patients with at least two predictors, the in-hospital mortality rate was 58% against 6% for others [p < 0.0001; RR = 7.6 (4.4-13)]. Patients over 75 years had three other predictors in 35% cases against 10% for others [p < 0.0001, RR = 3.5 (1.9-6.4)]. The greatest risk of death from COVID-19 was age above 75 years, worsened by elevated CRP and CXR score and reduced P/F. Prompt determination of these data at admission to the emergency department could improve COVID-19 pretreatment risk stratification.