Meta-analysis of Lung Ultrasound and Chest Radiography in the Diagnosis of Suspected Pulmonary Infections in Children

Meta-analysis of Lung Ultrasound and Chest Radiography in the Diagnosis of Suspected Pulmonary Infections in Children | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Meta-analysis of Lung Ultrasound and Chest Radiography in the Diagnosis of Suspected Pulmonary Infections in Children Xiao-Qun Liu, Yang-Yang Wang, Wanqing Jiang, Shaojun Tong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7366987/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Pulmonary infectious diseases are a major cause of morbidity and mortality in pediatrics. Lung ultrasound (LUS) has emerged as a diagnostic tool with advantages such as low cost and the absence of radiation exposure. However, its diagnostic performance compared to conventional methods like chest radiography, or called chest X-ray (CXR), needs further evaluation. Methods A comprehensive study search was conducted based on the online databases including PubMed, EMBASE, Web of Science, ScienceDirect, Wiley Online Library, and Google Scholar. Studies evaluating and comparing the diagnostic performance of LUS and CXR for pulmonary infections in children were retrieved. Relevant data were extracted, and pooled effect sizes were synthesized to assess the diagnostic accuracy of LUS versus CXR in suspected pediatric pulmonary infections. Results A total of 655 relevant articles were initially retrieved, and after exclusion, 13 articles were included in the selection, with a total of 2260 pediatric patients involved. Among them, 11 research subjects were patients with common pneumonia, and 2 research subjects were patients with pulmonary tuberculosis. The pooled analysis showed that the sensitivity of LUS and CXR in diagnosing suspected pneumonia in children was 0.94, 95%CI [0.90, 0.97] and 0.86, 95%CI [0.80, 0.89], respectively, while the specificity was 0.77, 95%CI [0.65, 0.86] and 0.74, 95%CI [0.59, 0.85], respectively; the Positive Likelihood Ratio (PLR) of LUS and CXR was 4.16, 95%CI [2.58, 6.70] and 3.29, 95%CI [2.08, 5.23], respectively, and the Negative Likelihood Ratio (NLR) was 0.08, 95%CI [0.04, 0.13] and 0.19, 95%CI [0.14, 0.26]; the areas under the sensitivity receiver operating characteristic (SROC) curve of LUS and CXR were 0.95, 95%CI [0.92 - 0.96] and 0.88, 95%CI [0.85 - 0.91], respectively. In children with suspected pulmonary infections (including pneumonia and tuberculosis), no significant difference was observed in the detection rate of consolidations between LUS and CXR, risk difference (RD)=0.07, 95%CI [-0.09; 0.23], P=0.41; but the detection rate of pleural effusion by LUS was significantly higher than that by CXR, RD=0.06, 95%CI [0.01; 0.10], P=0.02. No significant difference was found in the detection rate of tuberculosis lymph adenopathy between LUS and CXR, RD=-0.09, 95%CI [-0.45; 0.27], P=0.55. Conclusion Lung ultrasound and chest X-ray showed comparable detection rates for pulmonary consolidation in children with suspected pulmonary infections. However, LUS was significantly more effective in detecting pleural effusion. Overall, LUS demonstrated higher sensitivity, specificity, and diagnostic accuracy than CXR in the evaluation of pediatric pneumonia. Pulmonary ultrasound Chest X-ray Suspected pulmonary infection in children Meta analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Pulmonary infectious diseases are among the most common diseases in pediatrics and are primarily caused by bacteria, viruses, and atypical pathogens such as Mycoplasma and Chlamydia (1). Community-acquired pneumonia (CAP) is the most prevalent pulmonary infection in children and remains a leading cause of mortality for those ones under 5 years old (2). A meta-analysis (3) involving 125,509 pediatric pneumonia cases found that CAP is frequently associated with viral infections, with respiratory syncytial virus and rhinovirus being the top detected pathogens, accounting for approximately 22.7% and 22.1% of all cases, respectively. Pulmonary tuberculosis (PTB) is another pulmonary disease caused by infection with Mycobacterium tuberculosis (MTB) (4). Clinical manifestations for this disease in children commonly include fever, cough, chest tightness, chest pain, and hemoptysis. However, the symptoms and radiological findings of tuberculosis often overlap with those of pneumonia, making the diagnosis of this disease difficult (5). The wide variety of respiratory pathogens associated with pulmonary infections—combined with their high infectivity, rapid transmission, and potential for co-infection—poses significant diagnostic and treatment challenges. Additionally, the immature respiratory and immune systems of children make them particularly susceptible to such infections, which can lead to widespread outbreaks. Without timely and appropriate treatment, these infections can progress to severe pneumonia, cardiac and respiratory failure, or even death. Therefore, early detection and intervention are critical for improving outcomes in children with suspected pulmonary infections (6). Currently, the diagnosis of pulmonary infectious diseases in children primarily relies on clinical manifestations, laboratory tests, and imaging examinations (7). Imaging modalities commonly include chest X-ray (CXR) and chest computed tomography (CT). However, both chest X-ray and chest CT examinations are radioactive. Given children’s higher tissue radiosensitivity, the associated risk of radiation-induced malignancy is not negligible (8). In recent years, lung ultrasound (LUS) has emerged as a promising imaging modality. Compared to CXR and CT, LUS offers several advantages, including the absence of radiation, lower cost, and ease of use. These benefits are particularly notable in pediatric patients, whose thinner chest walls and smaller thoracic dimensions enhance the quality and feasibility of ultrasound imaging (9). LUS has been proved to be applicable to the diagnosis of pulmonary diseases in adults, but the diagnostic value of pulmonary infectious diseases in children still needs to be evaluated. According to the findings of Reali et al. (10), LUS can detect a greater number of subpleural consolidations and pleural effusions in children with pneumonia, with higher sensitivity and specificity compared to CXR. In contrast, Ambroggio et al. (11) reported no significant differences between LUS and CXR in the detection rates of consolidation, interstitial abnormalities, and pleural effusion. Moreover, their study suggested that the sensitivity and specificity of CXR may even exceed those of LUS. To address these conflicting results and provide more robust evidence regarding the diagnostic performance of LUS, we conducted the present meta-analysis. Materials and Methods Databases and search strategy ①Search strategy: We conducted electronic searches by keyword combination. The search keywords used were: "Ultrasound", "Chest Radiography", "chest roentgenogram", "X-ray", "pneumonia", "pulmonary infection", "covid-19", "pulmonary tuberculosis"; ②Databases: Pubmed, Embase, WOS, Science direct, Wiley online, Google scholar. We limited the search time range from the establishment of the database to April 2025. Pre-defined eligibility criteria The included studies need to meet the following criteria: ①Study type: All included studies are observational studies, regardless of whether the study has only one center or multiply centers, and the literature can be prospective or retrospective; ②The study subjects are all children with suspected lung infection, including suspected pneumonia, suspected tuberculosis, and suspected Covid-19 infection. ③Diagnostic method: All participants must have undergone both lung ultrasound (LUS) and chest X-ray (CXR), with the studies reporting corresponding sensitivity and specificity values for each modality. ④Reference standard (or gold standard): a diagnostic reference standard for pulmonary infection must be described in the study to allow for proper quality assessment. Literature with the following characteristics will be excluded: ①Clinical control studies, case series, review articles, experience sharing, case analysis, and conference records will be excluded; ②Research subjects are animals or adults will be excluded; ③Research purposes are to use ultrasound to determine the progression and prognosis of pneumonia will be excluded; ④Studies that include two combined examination methods rather than a single examination will be excluded; ⑤Those studies that cannot provide data will be excluded. Study selection After independently completing the literature search, two reviewers removed duplicate records and screened the titles, abstracts, and full texts to exclude studies that did not meet the pre-defined criteria through discussion. Quality assessment We assessed the quality of the included diagnostic studies using the QUADAS-2 tool (12). This tool consists of two main components: assessment of risk of bias and assessment of applicability. The risk of bias component evaluates the bias that may affect diagnostic accuracy, while the applicability component assesses how well the study align with the review’s objectives. Data extraction and transformation After completing the literature screening, the two reviewers read the full text of the literature again to extract study characteristics (author, publication time), research subject information (gender, age), prediction information and diagnostic data such as true positive cases (TP), false positive (FP), false negative (FN), true negative (TN). If the diagnostic data cannot be obtained from the literature directly, we attempt to derive them from the sensitivity and specificity values provided in the literature. Statistical analysis Statistical analyses were performed under STATA version 16.0 (StataCorp LLC) and Rstudio version 467 (PBC). Pooled sensitivity (Sen), specificity (Spe), and their corresponding 95% confidence intervals (CIs) were calculated. A P value (Q test) below 0.1 or I 2 over 50% reveals significant heterogeneity, and the random effects model was selected. For the comparison of discrete data, the risk difference (RD) was used as the effect size. A two-sided P-value < 0.05 was considered statistically significant. Reporting Guidelines The design,conduct, and reporting of this systematic review and meta-analysis followed the requirements of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Results Study selection The document retrieval flow chart is shown in Figure 1. An initial search yielded 655 articles, of which 520 were from online databases and 135 from Google scholar. After screening, 13 articles (10-11, 13-23) were selected, and a total of 2,260 pediatric patients with suspected lung infection participated in the study. Although the study by Rahmati MB et al. (24) also explored the differences between LUS and CXR in the diagnosis of suspected pneumonia, the quality was low because no data was provided. Study by Hassanzad M et al. (25) dived into pulmonary fibrosis, not early lung infection, and was therefore excluded. Study by Ianniello S et al. (26) explored the diagnostic performance of combined modalities, rather than separate ones, and was therefore excluded. The subjects of the study by Caroselli C, et al. (27) were adult patients, not pediatric patients, so they were excluded. Not all excluded literature were listed. Characteristics of the included studies The basic characteristics of the included literature and patient characteristics are shown in Table 1. Among them, there were 11 articles about suspected lung infection as pneumonia and 2 articles about pulmonary tuberculosis. There were 9 prospective, 2 retrospective observational studies, and 2 cross-sectional studies. The diagnostic data of the literature for pneumonia are shown in Table 2. Quality assessment of the included studies The bias risk assessment based on QUODAS-2 was conducted for the 11 included studies on suspected pneumonia, and the results are shown in Table 3. In terms of bias risk, studies (14, 17, 19, 20) lacked a description of the gold standard or did not blind the implementation of the gold standard, which resulted in "high bias", and other studies were evaluated as "low risk". In terms of applicability, since the gold standard adopted by literature (16) was CXR, and the diagnostic method to be evaluated was used as the standard, it was evaluated as "high risk". The overall risk assessment diagram is shown in Figure 2. Outcomes of diagnostic performance Sensitivity and specificity of LUS and CXR in diagnosing pediatric patients with suspected pneumonia Eleven articles were included. As LUS diagnostic sensitivity values was pooled, there was significant heterogeneity among the articles (I 2 =90.04%, df=10, P<0.001). The pooled diagnostic sensitivity was 0.94, 95%CI [0.90, 0.97] according to the random effects model. In the synthesis of LUS specificity, the literature heterogeneity was (I 2 =84.94%, df=10, P<0.001), and the pooled result was: 0.77, 95%CI [0.65, 0.86]. When CXR diagnostic sensitivity was pooled, there was also significant heterogeneity among the articles (I 2 =87.12%, df=10, P<0.001). The pooled diagnostic sensitivity was 0.86, 95%CI [0.80, 0.89] according to the random effects model. In the synthesis of specificity, the literature heterogeneity was (I 2 =90.49%, df=10, P<0.001), and the pooled result was: 0.74, 95%CI [0.59, 0.85], as shown in Figure 3. LUS and CXR diagnosis of PLR and NLR in children with pneumonia In the synthesis of positive likelihood ratio (PLR), the meta-synthesis obtained a PLR of 4.16, 95%CI [2.58, 6.70] for LUS, and 3.29, 95%CI [2.08, 5.23] for CXR; in the synthesis of negative likelihood ratio (NLR), the meta-synthesis obtained an NLR of 0.08, 95%CI [0.04, 0.13] for LUS and 0.19, 95%CI [0.14, 0.26] for CXR. As shown in Figure 4. Fagan Diagram Figure 5 is a Fagan diagram of the meta-synthesis of 11 articles. The Likelihood Ratio indicates that, under the premise of a priori probability of 20%, the child is 4 times more likely to be diagnosed with pneumonia by LUS, and the posterior probability will reach 51% (Figure5A); under the premise of a priori probability of 20%, the child is 3 times more likely to be diagnosed with pneumonia by CXR, and the posterior probability will reach 45% (Figure5B). SROC Curve As shown in Figure 6, the meta-synthesis SROC curve of 11 articles shows that the AUC value of LUS in diagnosing pneumonia in children is 0.95, 95%CI [0.92 - 0.96], while CXR is 0.88, 95%CI [0.85 - 0.91]. Publication bias The scatter plot is shown in Figure 7A, B. The results indicate that the 11 articles are evenly distributed in the four quadrants of the coordinate axis, and the results are reliable. Outcomes of lung infection characteristics A total of 6 articles reported the consolidations detection rate of LUS and CXR, which can be divided into two subgroups according to the disease type: pneumonia group and tuberculosis group. The pooled result suggested no significant difference in the detection rate of consolidations between LUS and CXR, RD=0.07, 95%CI [-0.09; 0.23], P=0.41. A P value of 0.49 was detected between the two subgroups, indicating that there was no significant difference between subgroups (Figure 8A). A total of 2 articles reported the pleural effusion detection rate of LUS and CXR. The pooled results suggested that the pleural effusion detection rate of LUS was significantly higher than that of CXR, RD=0.06, 95%CI [0.01; 0.10], P=0.02 (Figure 8B). There were 2 articles reporting the detection rates of lymph adenopathy by LUS and CXR. No significant difference was observed in the detection rates of lymph adenopathy between LUS and CXR, RD=-0.09, 95%CI [-0.45; 0.27], P=0.55 (Figure 8C). Discussion Chest X-ray (CXR) is a commonly used imaging modality for pneumonia, but there are several limitations including exposure to ionizing radiation, missed diagnosis, and variability in interpretation among different observers ( 28 ). Current clinical guidelines do not recommend routine chest X-ray imaging for children with pneumonia who are in generally stable condition ( 29 ). Lung ultrasound (LUS) began to enter the clinical exploration in the 1980s and has since gained increasing attention for screening and diagnosis of pneumonia in children. The results of lung ultrasound examination of healthy children showed that their lungs were smooth and clear, there were complete pleural lines, multiple A-lines parallel to the pleural line, the lung tissue was hypoechoic, and the lung sliding sign appeared with breathing movement ( 30 , 31 ). It was also observed in children with pneumonia that the B-line and pleural line were abnormal, and there was lung consolidation ( 32 ). With the progress of the disease, children with pneumonia can have pulmonary fibroplasia, small bronchial obstruction, resulting in varying degrees of lung parenchymal changes (including atelectasis, interlobar lung consolidation and subpleural lung consolidation) ( 33 ). This provides a theoretical basis for the diagnosis of lung infection and pneumonia in children by LUS, but its actual diagnostic performance is still lack of evaluation. In this study, 13 articles on the diagnosis of suspected pulmonary infection in children by lung ultrasound and chest X-ray were included, of which 11 articles included cases of suspected pneumonia and 2 articles included cases of suspected pulmonary tuberculosis. These studies were published between 2014 and 2024, and were conducted in four continents: Asia, Africa, North America, and Europe. They were prospective or retrospective observational studies. After synthesizing the data of the 11 articles on the early diagnosis of suspected pneumonia, it was found that LUS had a higher sensitivity (0.94 vs. 0.86) and a higher specificity (0.77 vs. 0.74) for the diagnosis of suspected pneumonia than CXR, a better PLR (4.16 vs. 3.29), and a lower NLR (0.08 vs. 0.19). When the prior probability was fixed, the posterior probability of diagnosis using LUS was higher than that of CXR (51% vs. 45%). The area under the SROC curve of LUS diagnosis was also higher than that of CXR (0.95 vs. 0.88). These data suggest that LUS has better diagnostic efficacy than CXR in diagnosing suspected pneumonia. A meta-analysis by Yan JH et al. ( 34 ) also compared LUS and CXR in diagnosing pneumonia in children and found that the sensitivity of diagnosis was (0.95 VS. 0.91), while the specificity was (0.90 VS. 1.00). The sensitivity was consistent with this study, but the specificity of LUS and CXR is higher in Yan JH et al.’s study. We assume that the meta-analysis of Yan JH et al. ( 34 ) included studies that only evaluated the diagnostic performance of LUS or CXR, while the studies included in this study were all studies that simultaneously evaluated the diagnostic performance of both LUS and CXR, resulting in less bias in patient selection and more reliable results. Pathogens can enter the lungs of children through both the respiratory tract and the bloodstream, proliferate and trigger inflammatory responses, leading to exudation within the alveoli, which may spread along the bronchi and cause bronchial obstruction ( 35 ). Lung consolidation is closely associated with the accumulation of inflammatory exudates in the alveoli. In a study by Rodríguez-Contreras FJ et al. ( 13 ), LUS in children with pulmonary consolidation revealed irregular dot- and patch-like echogenic areas as well as the bronchogram sign. In cases of inflammatory consolidation, dynamic air bronchograms were commonly observed on ultrasound. On the other side, Buz Yaşar A et al. ( 19 ) identified pleural effusion as a common finding in pediatric lung disease and reported that LUS provides higher accuracy in detecting pleural effusion compared to chest X-ray. Clinically, physicians can assess the ventilated lung area and determine the presence of pneumonia by evaluating the number and distribution of lesions in combination with ultrasound imaging findings. In this study, the detection rates of lung ultrasound (LUS) and chest X-ray (CXR) for lung consolidations (greater than 1 cm) in children with pulmonary infections were compared, and no significant difference was observed between the two modalities. As alveolar air content decreases, lung tissue becomes more parenchymal in nature, producing sonographic and radiographic patterns that resemble solid tissue, which is indicative of lung consolidation ( 36 ). Although both can detect lung consolidation, the accuracy of both is limited. If the lung consolidation is located in the perihilar and paracardial areas, but cannot reach the pleural surface, or the lung consolidation is located in the areas that are difficult to contact with the lung ultrasound, such as the scapula, supraclavicular or axillary areas, there may be missed detection; However, chest X-ray may miss diagnosis due to heart, diaphragm, ribs and other obstructions to some lung tissues ( 37 ). Wang et al. ( 38 ) reported that LUS has high specificity for lung consolidations larger than 1 cm, whereas smaller consolidations (< 0.6 cm) may go undetected. The two imaging modalities also differ in how they describe the size and location of lung consolidations. CXR reports often use generalized terms such as “bilateral lungs” or “adjacent to the cardiac silhouette,” and typically describe opacities as “patchy” without specifying size. In contrast, ultrasound examinations—often performed at the bedside—allow direct, real-time communication between the sonographer and clinician, enabling more precise localization and measurement of consolidations, as well as real-time assessment of lung aeration ( 39 ). Therefore, compared to CXR, LUS may offer greater clinical utility for guiding treatment decisions and monitoring disease progression dynamically. In this study, the detection rate of pleural effusion in children with pulmonary infection was compared between LUS and CXR, and it was found that the detection rate of LUS was significantly higher than that of CXR, suggesting that LUS offers more advantages in detecting pleural effusion. The underlying principle of LUS involves the transmission of ultrasound waves through various tissues—including skin, muscle, blood, and bone—where the waves undergo reflection, refraction, absorption, and attenuation, ultimately forming diagnostic images. The air-to-fluid ratio is commonly used to characterize the normal aeration of lung tissue. Pathological changes affecting this ratio in the pleural cavity, alveoli, or lung interstitium produce distinct ultrasound artifacts and imaging patterns that facilitate the diagnosis and evaluation of lung diseases ( 40 ). As a result, LUS is particularly sensitive for detecting pleural effusion and may serve as a more effective tool than CXR in this clinical context. Although the causative pathogens, pathological processes, and clinical manifestations of PTB and pneumonia differ, PTB can also present with imaging findings such as nodules and pleural effusion, which may resemble those observed in pediatric pneumonia. As such, lung ultrasound (LUS) may have diagnostic relevance for PTB as well ( 41 ). However, due to the lack of disease-specific imaging features, no study to date has evaluated the diagnostic performance of LUS or chest X-ray (CXR) alone in the diagnosis of PTB. Currently, the gold standard remains the culture of Mycobacterium tuberculosis from sputum samples. Lymphadenopathy is a potential distinguishing feature of PTB that is typically absent in pneumonia. However, the findings regarding its detection using LUS and CXR are inconsistent. Study ( 17 ) reported that LUS had a significantly higher detection rate of lymphadenopathy compared to CXR, whereas study ( 18 ) found the opposite. These conflicting results underscore the need for further research to clarify the imaging characteristics of lymphadenopathy in pediatric PTB diagnosis. The targeted subjects of the studies included in this meta-analysis were all children with suspected lung infections, but only two specific conditions were represented-community-acquired pneumonia and PTB. Although pneumonia caused by Covid-19 has been very common in recent years, research on its imaging features in pediatric populations remains very rare. In the study by Caroselli C et al. ( 27 ), the imaging characteristics of LUS for Covid-19 pneumonia were explored, but it was excluded from this meta-analysis because the study population consisted of adults and did not include CXR for comparison. Therefore, further research is still needed. This study still has some limitations. In the pooling of diagnostic values, there is significant heterogeneity among the 11 studies, which may be attributed to variations in study design and implementation, differences in patient selection and sample size. We noticed that the reference standards across the studies were inconsistent, and some studies did not specify a reference standard. These inconsistencies not only contribute to heterogeneity but may also affect the accuracy of the results. In addition, pediatric pulmonary infections are caused by a wide range of bacterial and viral pathogens, each of which may present with distinct imaging features. This meta-analysis focused only on two common suspected conditions. Moreover, while lung ultrasound is valuable for detecting structural lung changes, it cannot distinguish between bacterial and viral etiologies. This represents a fundamental limitation of ultrasound as a diagnostic tool. Despite these shortcomings, the results of this study are still credible. The hash diagram of publication bias also suggests that there is no significant publication bias in this study. Therefore, it is feasible to apply LUS to suspected pediatric lung infection. In clinical practice, LUS can be combined with other diagnostic measures (such as etiology) to increase the accuracy of diagnosis. Conclusion Compared with chest X-ray, lung ultrasound demonstrates higher sensitivity, specificity, and overall accuracy in the diagnosis of common pneumonia in children. For suspected pulmonary infections, lung ultrasound shows superior detection performance for pleural effusion, but equal performance for pulmonary consolidation. However, due to the substantial heterogeneity among the studies included in this meta-analysis, further high-quality research is needed to validate and expand upon these findings. Declarations Author contributions XQL: Participated in design, literature processing, data extraction and analysis, and assisted in drafting the manuscript. YYW and WQJ: quality evaluation, data verification and statistical analysis,result interpretation and chart production. SJT*: Responsible for research design and overall supervision, drafted and revised the manuscript, and is the corresponding contact. Funding This work was supported by the Zhejiang Provincial Medical and Health Science and Technology Program (Grant No. 2023KY364) Data availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. 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BMC Pulm Med. 2018 Dec 7;18(1):191. doi: 10.1186/s12890-018-0750-1. PMID: 30526548; PMCID: PMC6286612. Rahmati MB, Ahmadi M, Malekmohamadi, Hasanpur S, Zare SH, Jafari M. The significance of chest ultrasound and chest X-ray in the diagnosis of children clinically suspected of pneumonia. J Med Life. 2015;8(Spec Iss 3):50-53. PMID: 28316665; PMCID: PMC5348962. Hassanzad M, Kiani A, Abedini A, Ghaffaripour H, Emami H, Alizadeh N, Zoghi G, Hashemi S, Velayati AA. Lung ultrasound for the diagnosis of cystic fibrosis pulmonary exacerbation. BMC Pulm Med. 2021 Nov 8;21(1):353. doi: 10.1186/s12890-021-01728-8. PMID: 34743707; PMCID: PMC8572653. Ianniello S, Piccolo CL, Buquicchio GL, Trinci M, Miele V. First-line diagnosis of paediatric pneumonia in emergency: lung ultrasound (LUS) in addition to chest-X-ray (CXR) and its role in follow-up. Br J Radiol. 2016;89(1061):20150998. doi: 10.1259/bjr.20150998. Epub 2016 Jan 22. PMID: 26689098; PMCID: PMC4985480. Caroselli C, Blaivas M, Marcosignori M, Tung Chen Y, Falzetti S, Mariz J, Fiorentino R, Pinto Silva R, Gomes Cochicho J, Sebastiani S, Carlini M, Polati E, Simonini V, Malagola S, Raffaldi I, Longo D. Early Lung Ultrasound Findings in Patients With COVID-19 Pneumonia: A Retrospective Multicenter Study of 479 Patients. J Ultrasound Med. 2022 Oct;41(10):2547-2556. doi: 10.1002/jum.15944. Epub 2022 Jan 18. PMID: 35040507; PMCID: PMC9015547. Ambroggio L, Cotter J, Hall M, Shapiro DJ, Lipsett SC, Hersh AL, Shah SS, Brogan TV, Gerber JS, Williams DJ, Blaschke AJ, Cogen JD, Neuman MI. Management of Pediatric Pneumonia: A Decade After the Pediatric Infectious Diseases Society and Infectious Diseases Society of America Guideline. Clin Infect Dis. 2023 Nov 30;77(11):1604-1611. doi: 10.1093/cid/ciad385. PMID: 37352841; PMCID: PMC11487097. 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J Matern Fetal Neonatal Med. 2022 Sep;35(18):3565-3572. doi: 10.1080/14767058.2020.1830369. Epub 2020 Oct 9. PMID: 33032479. Wang N, He H, Long Y, Liu D, Wang Q, Jiang J, Xue Y, Yuan S, Chi Y, Zhao Z. Two regional ventilation-perfusion patterns of lung consolidation assessed by electrical impedance tomography and ultrasound. Crit Care. 2022 Nov 17;26(1):357. doi: 10.1186/s13054-022-04235-2. PMID: 36397063; PMCID: PMC9669526. Hansell L, Milross M, Delaney A, Tian DH, Ntoumenopoulos G. Lung ultrasound has greater accuracy than conventional respiratory assessment tools for the diagnosis of pleural effusion, lung consolidation and collapse: a systematic review. J Physiother. 2021 Jan;67(1):41-48. doi: 10.1016/j.jphys.2020.12.002. PMID: 33353830. Kok B, Tuinman PR, Haaksma ME. Lung ultrasound in pneumonia: a guide for effective implementation. Intern Emerg Med. 2025 Mar;20(2):357-367. doi: 10.1007/s11739-024-03807-0. Epub 2024 Dec 10. PMID: 39656349. Ogawa K, Kurosaki A, Miyamoto A, Takahashi Y, Murase K, Hanada S, Uruga H, Takaya H, Morokawa N, Kishi K. Clinicoradiological Features of Pulmonary Tuberculosis with Interstitial Pneumonia. Intern Med. 2019 Sep 1;58(17):2443-2449. doi: 10.2169/internalmedicine.2341-18. Epub 2019 May 22. PMID: 31118378; PMCID: PMC6761332. Tables Table 1 Basic characteristics of included literature. Study Publication year Study design Location Age(years) Sex(F) Suspected disease Confirm diagnosis Patients/Total Reali Fet al. (10) 2014 Prospective Italy 4±3 43% Pneumonia Clinical history and course, CXR, blood analysis 81/107 Rodríguez-Contreras FJ et al. (11) 2022 Prospective Spain 5 42% Pneumonia Radiologist’s chest radiograph report 59/82 Ambroggio L et al. (13) 2016 Prospective USA 0.5 ~ 18 44% Pneumonia CT 47/132 Yadav KK et al. (14) 2017 Prospective India 2.1 ± 1.7 45.9% Pneumonia NR 105 / 118 Venkatakrishna SSB et al. (15) 2024 Prospective South Africa 0.5 47% Pneumonia Agreement between general practitioner and a radiologist 51 /98 Chemeda LA et al. (16) 2024 Cross-sectional study Ethiopia <14 43% Pneumonia CXR 47 /108 Heuvelings CC et al. (17) 2019 Prospective South Africa 2.2 (1~ 5) 43% PTB NR 36 / 159 Erem G et al. (18) 2024 Cross-sectional study Uganda <14 38% PTB NR 51 / 80 Buz Yaşar A et al. (19) 2023 Prospective Turkey 2 (0.5~ 18) 54.1% Pneumonia NR 90 / 133 Yan C et al. (20) 2020 Prospective China 12.45±3.12 (2 ~ 16) 53% Pneumonia CT / 949 Yilmaz HL et al. (21) 2017  Prospective Turkey 3.3±4 46.9% Pneumonia NR 149 / 160 Iorio G et al. (22) 2018 Retrospective Italy 4.0 ± 2.5 42.6% Pneumonia NR 47 / 47 Biagi C et al. (23) 2018 Retrospective Italy 0.5 ± 0.5 47% Pneumonia clinical presentation, laboratory tests and CXR 25 / 87 Abbreviations: NR, Not reported; CAP, community-acquired pneumonia; LUS, lung ultrasound; CXR, chest x-rays; PTB, pulmonary tuberculosis. Table 2 Diagnostic data information of the included literature. Study LUS / CXR TP FP FN TN Sensitivity Specificity Reali Fet al. (10) LUS 76 1 5 25 93.8% 96.2% CXR 66 2 15 26 81.5% 92.3% Rodríguez-Contreras FJ et al. (11) LUS 35 14 4 33 89.7% 70.2% CXR 28 11 8 23 77.8% 67.5% Ambroggio L et al. (13) LUS 34 22 13 63 72.3% 74.1% CXR 38 21 9 64 80.9% 75.3% Yadav KK et al. (14) LUS 105 4 3 6 98.02% 64.71% CXR 101 5 7 5 93.5% 50.50% Venkatakrishna SSB et al. (15) LUS 41 10 10 27 80.4% 73.0% CXR 36 13 15 24 70.6% 64.9% Chemeda LA et al. (16) LUS 44 1 3 60 93.6% 98.4% CXR 29 2 18 59 61.7% 96.7% Buz Yaşar A et al. (19) LUS 88 25 2 108 98.2% 81.3% CXR 83 24 7 109 92.8% 82.2% Yan C et al. (20) LUS 745 65 31 85 90.6% 66.1% CXR 651 89 82 71 79.3% 55.9% Yilmaz HL et al. (21) LUS 142 7 5 6 96.6% 46.2% CXR 132 6 15 7 89.8% 53.8% Iorio G et al. (22) LUS 38 3 2 4 95% 57.1% CXR 36 5 2 4 94.7% 44.4% Biagi C et al. (23) LUS 25 10 0 52 100% 83.9% CXR 24 8 1 54 96% 87.1% Table 3 Risk of bias assessment based on quodas-2. Study Risk of bias Applicability Patient selection Index test Reference standard Flow & timing Patient selection Index test Reference standard Reali Fet al. (10) Low Low Low Low Low Low Low Rodríguez-Contreras FJ et al. (11) Low Low Low Low Low Low Low Ambroggio L et al. (13) Low Low Low Low Low Low Low Yadav KK et al. (14) Low Low High Low Low Low Low Venkatakrishna SSB et al. (15) Low Low Low Low Low Low Low Chemeda LA et al. (16) Low Low Low Low Low Low High Buz Yaşar A et al. (19) Low Low High Low Low Low Low Yan C et al. (20) Low Low Low Low Low Low Low Yilmaz HL et al. (21) Low Low High Low Low Low Low Iorio G et al. (22) Low Low High Low Low Low Low Biagi C et al. (23) Low Low Low Low Low Low Low Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":257868,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe flow chart of study selection according to pre-defined criteria.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7366987/v1/c2f36278c51f3ca37eaf9193.png"},{"id":92280713,"identity":"05e33ec8-de7b-4041-b5c1-70193ccf0625","added_by":"auto","created_at":"2025-09-26 16:15:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":8401,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall risk assessment diagram of diagnostic studies based on quodas-2.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7366987/v1/74f49e43f5dffaf055f5249d.png"},{"id":92279749,"identity":"1a8f8fe2-c8ae-4139-a2a9-028081b1dc82","added_by":"auto","created_at":"2025-09-26 16:07:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9591,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe sensitivity and specificity of LUS and CXR in the diagnosis of pneumonia in children. (A) LUS, (B) CXR. LUS, Lung ultrasound; CXR, Chest X-ray.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7366987/v1/cc8df64ee4288a79cc82268e.png"},{"id":92281421,"identity":"18e5fab9-a4e6-49ab-8aa3-45d69e48b437","added_by":"auto","created_at":"2025-09-26 16:31:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":8134,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe PLR and NLR of LUS and CXR in the diagnosis of pneumonia in children. (A) LUS, (B) CXR. LUS, Lung ultrasound; CXR, Chest X-ray.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7366987/v1/fb0e8d53fb813a9306a1689f.png"},{"id":92279742,"identity":"295c1970-0669-4464-bc7e-ba50be701448","added_by":"auto","created_at":"2025-09-26 16:07:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4644,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFagan diagram of LUS and CXR in the diagnosis of pneumonia in children. (A) LUS, (B) CXR. LUS, Lung ultrasound; CXR, Chest X-ray.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7366987/v1/1aad68f43092be44892893fc.png"},{"id":92281264,"identity":"460bddde-fb57-46ff-9637-f9cad96b86d7","added_by":"auto","created_at":"2025-09-26 16:23:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5047,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLUS and CXR were used to diagnose SROC in children with pneumonia. (A) LUS, (B) CXR. LUS, Lung ultrasound; CXR, Chest X-ray.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-7366987/v1/9a61c73e7b23d4c375d60b07.png"},{"id":92281266,"identity":"0e9323ee-c95c-4ed7-9987-a05a1c3cf247","added_by":"auto","created_at":"2025-09-26 16:23:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":5016,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHash plot of LUS and CXR in the diagnosis of pneumonia in children. (A) LUS, (B) CXR. LUS, Lung ultrasound; CXR, Chest X-ray.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-7366987/v1/ba2ca3e66b497c3edeb1ce28.png"},{"id":92279746,"identity":"e02520d4-46ec-4d1d-8182-055b1751c6e2","added_by":"auto","created_at":"2025-09-26 16:07:49","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":9599,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe pooled effect size of detection rate according to imaging features of lung infection. (A) Consolidations. (B) Pleural effusion. (C) Lymph adenopathy.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-7366987/v1/d75824472e3aca95cb0884fa.png"},{"id":92391467,"identity":"827b14f1-c923-4ba6-801b-4bbf1a83b674","added_by":"auto","created_at":"2025-09-29 08:40:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1643196,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7366987/v1/e634b2bc-3408-47ec-aad2-b77ef0440ae4.pdf"},{"id":92280715,"identity":"33e9d7d1-cdea-456f-a8ac-60b96a7a06c6","added_by":"auto","created_at":"2025-09-26 16:15:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":275506,"visible":true,"origin":"","legend":"","description":"","filename":"PRISMA2020checklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-7366987/v1/bc46788b30eb4a2851564435.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Meta-analysis of Lung Ultrasound and Chest Radiography in the Diagnosis of Suspected Pulmonary Infections in Children","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePulmonary infectious diseases are among the most common diseases in pediatrics and are primarily caused by bacteria, viruses, and atypical pathogens such as \u003cem\u003eMycoplasma\u003c/em\u003e and \u003cem\u003eChlamydia\u003c/em\u003e (1). Community-acquired pneumonia (CAP) is the most prevalent pulmonary infection in children and remains a leading cause of mortality for those ones under 5 years old (2). A meta-analysis (3) involving 125,509 pediatric pneumonia cases found that CAP is frequently associated with viral infections, with respiratory \u003cem\u003esyncytial virus\u003c/em\u003e and \u003cem\u003erhinovirus\u003c/em\u003e being the top detected pathogens, accounting for approximately 22.7% and 22.1% of all cases, respectively. Pulmonary tuberculosis (PTB) is another pulmonary disease caused by infection with \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (MTB) (4). Clinical manifestations for this disease in children commonly include fever, cough, chest tightness, chest pain, and hemoptysis. However, the symptoms and radiological findings of tuberculosis often overlap with those of pneumonia, making the diagnosis of this disease difficult (5).\u003c/p\u003e\n\u003cp\u003eThe wide variety of respiratory pathogens associated with pulmonary infections\u0026mdash;combined with their high infectivity, rapid transmission, and potential for co-infection\u0026mdash;poses significant diagnostic and treatment challenges. Additionally, the immature respiratory and immune systems of children make them particularly susceptible to such infections, which can lead to widespread outbreaks. Without timely and appropriate treatment, these infections can progress to severe pneumonia, cardiac and respiratory failure, or even death. Therefore, early detection and intervention are critical for improving outcomes in children with suspected pulmonary infections (6).\u003c/p\u003e\n\u003cp\u003eCurrently, the diagnosis of pulmonary infectious diseases in children primarily relies on clinical manifestations, laboratory tests, and imaging examinations (7). Imaging modalities commonly include chest X-ray (CXR) and chest computed tomography (CT). However, both chest X-ray and chest CT examinations are radioactive. Given children\u0026rsquo;s higher tissue radiosensitivity, the associated risk of radiation-induced malignancy is not negligible (8). In recent years, lung ultrasound (LUS) has emerged as a promising imaging modality. Compared to CXR and CT, LUS offers several advantages, including the absence of radiation, lower cost, and ease of use. These benefits are particularly notable in pediatric patients, whose thinner chest walls and smaller thoracic dimensions enhance the quality and feasibility of ultrasound imaging (9).\u003c/p\u003e\n\u003cp\u003eLUS has been proved to be applicable to the diagnosis of pulmonary diseases in adults, but the diagnostic value of pulmonary infectious diseases in children still needs to be evaluated. According to the findings of Reali et al. (10), LUS can detect a greater number of subpleural consolidations and pleural effusions in children with pneumonia, with higher sensitivity and specificity compared to CXR. In contrast, Ambroggio et al. (11) reported no significant differences between LUS and CXR in the detection rates of consolidation, interstitial abnormalities, and pleural effusion. Moreover, their study suggested that the sensitivity and specificity of CXR may even exceed those of LUS. To address these conflicting results and provide more robust evidence regarding the diagnostic performance of LUS, we conducted the present meta-analysis.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eDatabases and search strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e①Search strategy: We conducted electronic searches by keyword combination. The search keywords used were: \u0026quot;Ultrasound\u0026quot;, \u0026quot;Chest Radiography\u0026quot;, \u0026quot;chest roentgenogram\u0026quot;, \u0026quot;X-ray\u0026quot;, \u0026quot;pneumonia\u0026quot;, \u0026quot;pulmonary infection\u0026quot;, \u0026quot;covid-19\u0026quot;, \u0026quot;pulmonary tuberculosis\u0026quot;;\u0026nbsp;②Databases: Pubmed, Embase, WOS, Science direct, Wiley online, Google scholar. We limited the search time range from the establishment of the database to April 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePre-defined eligibility criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe included studies need to meet the following criteria:\u0026nbsp;①Study type: All included studies are observational studies, regardless of whether the study has only one center or multiply centers, and the literature can be prospective or retrospective;\u0026nbsp;②The study subjects are all children with suspected lung infection, including suspected pneumonia, suspected tuberculosis, and suspected Covid-19 infection.\u0026nbsp;③Diagnostic method: All participants must have undergone both lung ultrasound (LUS) and chest X-ray (CXR), with the studies reporting corresponding sensitivity and specificity values for each modality.\u0026nbsp;④Reference standard (or gold standard): a diagnostic reference standard for pulmonary infection must be described in the study to allow for proper quality assessment.\u003c/p\u003e\n\u003cp\u003eLiterature with the following characteristics will be excluded:\u0026nbsp;①Clinical control studies, case series, review articles, experience sharing, case analysis, and conference records will be excluded;\u0026nbsp;②Research subjects are animals or adults will be excluded;\u0026nbsp;③Research purposes are to use ultrasound to determine the progression and prognosis of pneumonia will be excluded;\u0026nbsp;④Studies that include two combined examination methods rather than a single examination will be excluded;\u0026nbsp;⑤Those studies that cannot provide data will be excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter independently completing the literature search, two reviewers removed duplicate records and screened the titles, abstracts, and full texts to exclude studies that did not meet the pre-defined criteria through discussion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe assessed the quality of the included diagnostic studies using the QUADAS-2 tool (12). This tool consists of two main components: assessment of risk of bias and assessment of applicability. The risk of bias component evaluates the bias that may affect diagnostic accuracy, while the applicability component assesses how well the study align with the review\u0026rsquo;s objectives.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData extraction and transformation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter completing the literature screening, the two reviewers read the full text of the literature again to extract study characteristics (author, publication time), research subject information (gender, age), prediction information and diagnostic data such as true positive cases (TP), false positive (FP), false negative (FN), true negative (TN). If the diagnostic data cannot be obtained from the literature directly, we attempt to derive them from the sensitivity and specificity values provided in the literature.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed under STATA version 16.0 (StataCorp LLC) and Rstudio version 467 (PBC). Pooled sensitivity (Sen), specificity (Spe), and their corresponding 95% confidence intervals (CIs) were calculated. A P value (Q test) below 0.1 or I\u003csup\u003e2\u003c/sup\u003e over 50% reveals significant heterogeneity, and the random effects model was selected. \u0026nbsp;For the comparison of discrete data, the risk difference (RD) was used as the effect size. A two-sided P-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003eReporting Guidelines The design,conduct, and reporting of this systematic review and meta-analysis followed the requirements of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eStudy selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe document retrieval flow chart is shown in Figure 1. An initial search yielded 655 articles, of which 520 were from online databases and 135 from Google scholar. After screening, 13 articles (10-11, 13-23) were selected, and a total of 2,260 pediatric patients with suspected lung infection participated in the study. Although the study by Rahmati MB et al. (24) also explored the differences between LUS and CXR in the diagnosis of suspected pneumonia, the quality was low because no data was provided. Study by Hassanzad M et al. (25) dived into pulmonary fibrosis, not early lung infection, and was therefore excluded. Study by Ianniello S et al. (26) explored the diagnostic performance of combined modalities, rather than separate ones, and was therefore excluded. The subjects of the study by Caroselli C, et al. (27) were adult patients, not pediatric patients, so they were excluded. Not all excluded literature were listed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics of the included studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe basic characteristics of the included literature and patient characteristics are shown in Table 1. Among them, there were 11 articles about suspected lung infection as pneumonia and 2 articles about pulmonary tuberculosis. There were 9 prospective, 2 retrospective observational studies, and 2 cross-sectional studies. The diagnostic data of the literature for pneumonia are shown in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality assessment of the included studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe bias risk assessment based on QUODAS-2 was conducted for the 11 included studies on suspected pneumonia, and the results are shown in Table 3. In terms of bias risk, studies (14, 17, 19, 20) lacked a description of the gold standard or did not blind the implementation of the gold standard, which resulted in \u0026quot;high bias\u0026quot;, and other studies were evaluated as \u0026quot;low risk\u0026quot;. In terms of applicability, since the gold standard adopted by literature (16) was CXR, and the diagnostic method to be evaluated was used as the standard, it was evaluated as \u0026quot;high risk\u0026quot;. The overall risk assessment diagram is shown in Figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcomes of diagnostic performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity and specificity of LUS and CXR in diagnosing pediatric patients with suspected pneumonia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEleven articles were included. As LUS diagnostic sensitivity values was pooled, there was significant heterogeneity among the articles (I\u003csup\u003e2\u003c/sup\u003e=90.04%, df=10, P\u0026lt;0.001). The pooled diagnostic sensitivity was 0.94, 95%CI [0.90, 0.97] according to the random effects model. In the synthesis of LUS specificity, the literature heterogeneity was (I\u003csup\u003e2\u003c/sup\u003e=84.94%, df=10, P\u0026lt;0.001), and the pooled result was: 0.77, 95%CI [0.65, 0.86]. When CXR diagnostic sensitivity was pooled, there was also significant heterogeneity among the articles (I\u003csup\u003e2\u003c/sup\u003e=87.12%, df=10, P\u0026lt;0.001). The pooled diagnostic sensitivity was 0.86, 95%CI [0.80, 0.89] according to the random effects model. In the synthesis of specificity, the literature heterogeneity was (I\u003csup\u003e2\u003c/sup\u003e=90.49%, df=10, P\u0026lt;0.001), and the pooled result was: 0.74, 95%CI [0.59, 0.85], as shown in Figure 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLUS and CXR diagnosis of PLR and NLR in children with pneumonia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the synthesis of positive likelihood ratio (PLR), the meta-synthesis obtained a PLR of 4.16, 95%CI [2.58, 6.70] for LUS, and 3.29, 95%CI [2.08, 5.23] for CXR; in the synthesis of negative likelihood ratio (NLR), the meta-synthesis obtained an NLR of 0.08, 95%CI [0.04, 0.13] for LUS and 0.19, 95%CI [0.14, 0.26] for CXR. As shown in Figure 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFagan Diagram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 5 is a Fagan diagram of the meta-synthesis of 11 articles. The Likelihood Ratio indicates that, under the premise of a priori probability of 20%, the child is 4 times more likely to be diagnosed with pneumonia by LUS, and the posterior probability will reach 51% (Figure5A); under the premise of a priori probability of 20%, the child is 3 times more likely to be diagnosed with pneumonia by CXR, and the posterior probability will reach 45% (Figure5B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSROC Curve\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 6, the meta-synthesis SROC curve of 11 articles shows that the AUC value of LUS in diagnosing pneumonia in children is 0.95, 95%CI [0.92 - 0.96], while CXR is 0.88, 95%CI [0.85 - 0.91].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublication bias\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe scatter plot is shown in Figure 7A, B. The results indicate that the 11 articles are evenly distributed in the four quadrants of the coordinate axis, and the results are reliable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcomes of lung infection characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 6 articles reported the consolidations detection rate of LUS and CXR, which can be divided into two subgroups according to the disease type: pneumonia group and tuberculosis group. The pooled result suggested no significant difference in the detection rate of consolidations between LUS and CXR, RD=0.07, 95%CI [-0.09; 0.23], P=0.41. A P value of 0.49 was detected between the two subgroups, indicating that there was no significant difference between subgroups (Figure 8A). A total of 2 articles reported the pleural effusion detection rate of LUS and CXR. The pooled results suggested that the pleural effusion detection rate of LUS was significantly higher than that of CXR, RD=0.06, 95%CI [0.01; 0.10], P=0.02 (Figure 8B). There were 2 articles reporting the detection rates of lymph adenopathy by LUS and CXR. No significant difference was observed in the detection rates of lymph adenopathy between LUS and CXR, RD=-0.09, 95%CI [-0.45; 0.27], P=0.55 (Figure 8C).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eChest X-ray (CXR) is a commonly used imaging modality for pneumonia, but there are several limitations including exposure to ionizing radiation, missed diagnosis, and variability in interpretation among different observers (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Current clinical guidelines do not recommend routine chest X-ray imaging for children with pneumonia who are in generally stable condition (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Lung ultrasound (LUS) began to enter the clinical exploration in the 1980s and has since gained increasing attention for screening and diagnosis of pneumonia in children. The results of lung ultrasound examination of healthy children showed that their lungs were smooth and clear, there were complete pleural lines, multiple A-lines parallel to the pleural line, the lung tissue was hypoechoic, and the lung sliding sign appeared with breathing movement (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). It was also observed in children with pneumonia that the B-line and pleural line were abnormal, and there was lung consolidation (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). With the progress of the disease, children with pneumonia can have pulmonary fibroplasia, small bronchial obstruction, resulting in varying degrees of lung parenchymal changes (including atelectasis, interlobar lung consolidation and subpleural lung consolidation) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). This provides a theoretical basis for the diagnosis of lung infection and pneumonia in children by LUS, but its actual diagnostic performance is still lack of evaluation.\u003c/p\u003e\u003cp\u003eIn this study, 13 articles on the diagnosis of suspected pulmonary infection in children by lung ultrasound and chest X-ray were included, of which 11 articles included cases of suspected pneumonia and 2 articles included cases of suspected pulmonary tuberculosis. These studies were published between 2014 and 2024, and were conducted in four continents: Asia, Africa, North America, and Europe. They were prospective or retrospective observational studies. After synthesizing the data of the 11 articles on the early diagnosis of suspected pneumonia, it was found that LUS had a higher sensitivity (0.94 vs. 0.86) and a higher specificity (0.77 vs. 0.74) for the diagnosis of suspected pneumonia than CXR, a better PLR (4.16 vs. 3.29), and a lower NLR (0.08 vs. 0.19). When the prior probability was fixed, the posterior probability of diagnosis using LUS was higher than that of CXR (51% vs. 45%). The area under the SROC curve of LUS diagnosis was also higher than that of CXR (0.95 vs. 0.88). These data suggest that LUS has better diagnostic efficacy than CXR in diagnosing suspected pneumonia. A meta-analysis by Yan JH et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) also compared LUS and CXR in diagnosing pneumonia in children and found that the sensitivity of diagnosis was (0.95 VS. 0.91), while the specificity was (0.90 VS. 1.00). The sensitivity was consistent with this study, but the specificity of LUS and CXR is higher in Yan JH et al.\u0026rsquo;s study. We assume that the meta-analysis of Yan JH et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) included studies that only evaluated the diagnostic performance of LUS or CXR, while the studies included in this study were all studies that simultaneously evaluated the diagnostic performance of both LUS and CXR, resulting in less bias in patient selection and more reliable results.\u003c/p\u003e\u003cp\u003ePathogens can enter the lungs of children through both the respiratory tract and the bloodstream, proliferate and trigger inflammatory responses, leading to exudation within the alveoli, which may spread along the bronchi and cause bronchial obstruction (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Lung consolidation is closely associated with the accumulation of inflammatory exudates in the alveoli. In a study by Rodr\u0026iacute;guez-Contreras FJ et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), LUS in children with pulmonary consolidation revealed irregular dot- and patch-like echogenic areas as well as the bronchogram sign. In cases of inflammatory consolidation, dynamic air bronchograms were commonly observed on ultrasound. On the other side, Buz Yaşar A et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) identified pleural effusion as a common finding in pediatric lung disease and reported that LUS provides higher accuracy in detecting pleural effusion compared to chest X-ray. Clinically, physicians can assess the ventilated lung area and determine the presence of pneumonia by evaluating the number and distribution of lesions in combination with ultrasound imaging findings.\u003c/p\u003e\u003cp\u003eIn this study, the detection rates of lung ultrasound (LUS) and chest X-ray (CXR) for lung consolidations (greater than 1 cm) in children with pulmonary infections were compared, and no significant difference was observed between the two modalities. As alveolar air content decreases, lung tissue becomes more parenchymal in nature, producing sonographic and radiographic patterns that resemble solid tissue, which is indicative of lung consolidation (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Although both can detect lung consolidation, the accuracy of both is limited. If the lung consolidation is located in the perihilar and paracardial areas, but cannot reach the pleural surface, or the lung consolidation is located in the areas that are difficult to contact with the lung ultrasound, such as the scapula, supraclavicular or axillary areas, there may be missed detection; However, chest X-ray may miss diagnosis due to heart, diaphragm, ribs and other obstructions to some lung tissues (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Wang et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) reported that LUS has high specificity for lung consolidations larger than 1 cm, whereas smaller consolidations (\u0026lt;\u0026thinsp;0.6 cm) may go undetected. The two imaging modalities also differ in how they describe the size and location of lung consolidations. CXR reports often use generalized terms such as \u0026ldquo;bilateral lungs\u0026rdquo; or \u0026ldquo;adjacent to the cardiac silhouette,\u0026rdquo; and typically describe opacities as \u0026ldquo;patchy\u0026rdquo; without specifying size. In contrast, ultrasound examinations\u0026mdash;often performed at the bedside\u0026mdash;allow direct, real-time communication between the sonographer and clinician, enabling more precise localization and measurement of consolidations, as well as real-time assessment of lung aeration (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Therefore, compared to CXR, LUS may offer greater clinical utility for guiding treatment decisions and monitoring disease progression dynamically.\u003c/p\u003e\u003cp\u003eIn this study, the detection rate of pleural effusion in children with pulmonary infection was compared between LUS and CXR, and it was found that the detection rate of LUS was significantly higher than that of CXR, suggesting that LUS offers more advantages in detecting pleural effusion. The underlying principle of LUS involves the transmission of ultrasound waves through various tissues\u0026mdash;including skin, muscle, blood, and bone\u0026mdash;where the waves undergo reflection, refraction, absorption, and attenuation, ultimately forming diagnostic images. The air-to-fluid ratio is commonly used to characterize the normal aeration of lung tissue. Pathological changes affecting this ratio in the pleural cavity, alveoli, or lung interstitium produce distinct ultrasound artifacts and imaging patterns that facilitate the diagnosis and evaluation of lung diseases (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). As a result, LUS is particularly sensitive for detecting pleural effusion and may serve as a more effective tool than CXR in this clinical context.\u003c/p\u003e\u003cp\u003eAlthough the causative pathogens, pathological processes, and clinical manifestations of PTB and pneumonia differ, PTB can also present with imaging findings such as nodules and pleural effusion, which may resemble those observed in pediatric pneumonia. As such, lung ultrasound (LUS) may have diagnostic relevance for PTB as well (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). However, due to the lack of disease-specific imaging features, no study to date has evaluated the diagnostic performance of LUS or chest X-ray (CXR) alone in the diagnosis of PTB. Currently, the gold standard remains the culture of Mycobacterium tuberculosis from sputum samples.\u003c/p\u003e\u003cp\u003eLymphadenopathy is a potential distinguishing feature of PTB that is typically absent in pneumonia. However, the findings regarding its detection using LUS and CXR are inconsistent. Study (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) reported that LUS had a significantly higher detection rate of lymphadenopathy compared to CXR, whereas study (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) found the opposite. These conflicting results underscore the need for further research to clarify the imaging characteristics of lymphadenopathy in pediatric PTB diagnosis.\u003c/p\u003e\u003cp\u003eThe targeted subjects of the studies included in this meta-analysis were all children with suspected lung infections, but only two specific conditions were represented-community-acquired pneumonia and PTB. Although pneumonia caused by Covid-19 has been very common in recent years, research on its imaging features in pediatric populations remains very rare. In the study by Caroselli C et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), the imaging characteristics of LUS for Covid-19 pneumonia were explored, but it was excluded from this meta-analysis because the study population consisted of adults and did not include CXR for comparison. Therefore, further research is still needed.\u003c/p\u003e\u003cp\u003eThis study still has some limitations. In the pooling of diagnostic values, there is significant heterogeneity among the 11 studies, which may be attributed to variations in study design and implementation, differences in patient selection and sample size. We noticed that the reference standards across the studies were inconsistent, and some studies did not specify a reference standard. These inconsistencies not only contribute to heterogeneity but may also affect the accuracy of the results. In addition, pediatric pulmonary infections are caused by a wide range of bacterial and viral pathogens, each of which may present with distinct imaging features. This meta-analysis focused only on two common suspected conditions. Moreover, while lung ultrasound is valuable for detecting structural lung changes, it cannot distinguish between bacterial and viral etiologies. This represents a fundamental limitation of ultrasound as a diagnostic tool.\u003c/p\u003e\u003cp\u003eDespite these shortcomings, the results of this study are still credible. The hash diagram of publication bias also suggests that there is no significant publication bias in this study. Therefore, it is feasible to apply LUS to suspected pediatric lung infection. In clinical practice, LUS can be combined with other diagnostic measures (such as etiology) to increase the accuracy of diagnosis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eCompared with chest X-ray, lung ultrasound demonstrates higher sensitivity, specificity, and overall accuracy in the diagnosis of common pneumonia in children. For suspected pulmonary infections, lung ultrasound shows superior detection performance for pleural effusion, but equal performance for pulmonary consolidation. However, due to the substantial heterogeneity among the studies included in this meta-analysis, further high-quality research is needed to validate and expand upon these findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXQL: Participated in design, literature processing, data extraction and analysis, and assisted in drafting the manuscript.\u003c/p\u003e\n\u003cp\u003eYYW and WQJ: quality evaluation, data verification and statistical analysis,result interpretation and chart production.\u003c/p\u003e\n\u003cp\u003eSJT*: Responsible for research design and overall supervision, drafted and revised the manuscript, and is the corresponding contact.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Zhejiang Provincial Medical and Health Science and Technology Program (Grant No. 2023KY364)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eThis study was registered in the PROSPERO (registration number ID: CRD420251023176).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eYao Y, Liu H, Yuan L, Du X, Yang Y, Zhou K, Wu X, Qin L, Yang M, Xiang Y, Qu X, Qin X, Liu C. 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PMID: 28316665; PMCID: PMC5348962.\u003c/li\u003e\n \u003cli\u003eHassanzad M, Kiani A, Abedini A, Ghaffaripour H, Emami H, Alizadeh N, Zoghi G, Hashemi S, Velayati AA. Lung ultrasound for the diagnosis of cystic fibrosis pulmonary exacerbation. BMC Pulm Med. 2021 Nov 8;21(1):353. doi: 10.1186/s12890-021-01728-8. PMID: 34743707; PMCID: PMC8572653.\u003c/li\u003e\n \u003cli\u003eIanniello S, Piccolo CL, Buquicchio GL, Trinci M, Miele V. First-line diagnosis of paediatric pneumonia in emergency: lung ultrasound (LUS) in addition to chest-X-ray (CXR) and its role in follow-up. Br J Radiol. 2016;89(1061):20150998. doi: 10.1259/bjr.20150998. Epub 2016 Jan 22. PMID: 26689098; PMCID: PMC4985480.\u003c/li\u003e\n \u003cli\u003eCaroselli C, Blaivas M, Marcosignori M, Tung Chen Y, Falzetti S, Mariz J, Fiorentino R, Pinto Silva R, Gomes Cochicho J, Sebastiani S, Carlini M, Polati E, Simonini V, Malagola S, Raffaldi I, Longo D. Early Lung Ultrasound Findings in Patients With COVID-19 Pneumonia: A Retrospective Multicenter Study of 479 Patients. J Ultrasound Med. 2022 Oct;41(10):2547-2556. doi: 10.1002/jum.15944. Epub 2022 Jan 18. PMID: 35040507; PMCID: PMC9015547.\u003c/li\u003e\n \u003cli\u003eAmbroggio L, Cotter J, Hall M, Shapiro DJ, Lipsett SC, Hersh AL, Shah SS, Brogan TV, Gerber JS, Williams DJ, Blaschke AJ, Cogen JD, Neuman MI. Management of Pediatric Pneumonia: A Decade After the Pediatric Infectious Diseases Society and Infectious Diseases Society of America Guideline. Clin Infect Dis. 2023 Nov 30;77(11):1604-1611. doi: 10.1093/cid/ciad385. PMID: 37352841; PMCID: PMC11487097.\u003c/li\u003e\n \u003cli\u003eBradley JS, Byington CL, Shah SS, Alverson B, Carter ER, Harrison C, Kaplan SL, Mace SE, McCracken GH Jr, Moore MR, St Peter SD, Stockwell JA, Swanson JT; Pediatric Infectious Diseases Society and the Infectious Diseases Society of America. The management of community-acquired pneumonia in infants and children older than 3 months of age: clinical practice guidelines by the Pediatric Infectious Diseases Society and the Infectious Diseases Society of America. Clin Infect Dis. 2011 Oct;53(7):e25-76. doi: 10.1093/cid/cir531. Epub 2011 Aug 31. PMID: 21880587; PMCID: PMC7107838.\u003c/li\u003e\n \u003cli\u003eGuitart C, Bobillo-Perez S, Rodr\u0026iacute;guez-Fanjul J, Carrasco JL, Brotons P, L\u0026oacute;pez-Ramos MG, Cambra FJ, Balaguer M, Jordan I. Lung ultrasound and procalcitonin, improving antibiotic management and avoiding radiation exposure in pediatric critical patients with bacterial pneumonia: a randomized clinical trial. Eur J Med Res. 2024 Apr 6;29(1):222. doi: 10.1186/s40001-024-01712-y. PMID: 38581075; PMCID: PMC10998368.\u003c/li\u003e\n \u003cli\u003eStadler JAM, Andronikou S, Zar HJ. Lung ultrasound for the diagnosis of community-acquired pneumonia in children. Pediatr Radiol. 2017 Oct;47(11):1412-1419. doi: 10.1007/s00247-017-3910-1. Epub 2017 Sep 21. PMID: 29043420; PMCID: PMC5608773.\u003c/li\u003e\n \u003cli\u003eCiuca IM, Dediu M, Pop LL. Pediatric pneumonia (PedPne) lung ultrasound score and inflammatory markers: A pilot study. Pediatr Pulmonol. 2022 Feb;57(2):576-582. doi: 10.1002/ppul.25760. Epub 2021 Nov 23. PMID: 34786878.\u003c/li\u003e\n \u003cli\u003eMa HR, Deng BY, Liu J, Jiang P, Xu YL, Song XY, Li J, Huang LH, Bao LY, Shan RY, Fu W. Lung ultrasound to diagnose infectious pneumonia of newborns: A prospective multicenter study. Pediatr Pulmonol. 2023 Jan;58(1):122-129. doi: 10.1002/ppul.26168. Epub 2022 Oct 6. PMID: 36169007.\u003c/li\u003e\n \u003cli\u003eYan JH, Yu N, Wang YH, Gao YB, Pan L. Lung ultrasound vs chest radiography in the diagnosis of children pneumonia: Systematic evidence. Medicine (Baltimore). 2020 Dec 11;99(50):e23671. doi: 10.1097/MD.0000000000023671. PMID: 33327356; PMCID: PMC7738074.\u003c/li\u003e\n \u003cli\u003eMartin RM, Bachman MA. Colonization, Infection, and the Accessory Genome of \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e. Front Cell Infect Microbiol. 2018 Jan 22;8:4. doi: 10.3389/fcimb.2018.00004. PMID: 29404282; PMCID: PMC5786545.\u003c/li\u003e\n \u003cli\u003eKessler D, Zhu M, Gregory CR, Mehanian C, Avila J, Avitable N, Coneybeare D, Das D, Dessie A, Kennedy TM, Rabiner J, Malia L, Ng L, Nye M, Vindas M, Weimersheimer P, Kulhare S, Millin R, Gregory K, Zheng X, Horning MP, Stone M, Wang F, Lancioni C. Development and testing of a deep learning algorithm to detect lung consolidation among children with pneumonia using hand-held ultrasound. PLoS One. 2024 Aug 27;19(8):e0309109. doi: 10.1371/journal.pone.0309109. PMID: 39190686; PMCID: PMC11349203.\u003c/li\u003e\n \u003cli\u003eGao YQ, Qiu RX, Liu J, Zhang L, Ren XL, Qin SJ. Lung ultrasound completely replaced chest X-ray for diagnosing neonatal lung diseases: a 3-year clinical practice report from a neonatal intensive care unit in China. J Matern Fetal Neonatal Med. 2022 Sep;35(18):3565-3572. doi: 10.1080/14767058.2020.1830369. Epub 2020 Oct 9. PMID: 33032479.\u003c/li\u003e\n \u003cli\u003eWang N, He H, Long Y, Liu D, Wang Q, Jiang J, Xue Y, Yuan S, Chi Y, Zhao Z. Two regional ventilation-perfusion patterns of lung consolidation assessed by electrical impedance tomography and ultrasound. Crit Care. 2022 Nov 17;26(1):357. doi: 10.1186/s13054-022-04235-2. PMID: 36397063; PMCID: PMC9669526.\u003c/li\u003e\n \u003cli\u003eHansell L, Milross M, Delaney A, Tian DH, Ntoumenopoulos G. Lung ultrasound has greater accuracy than conventional respiratory assessment tools for the diagnosis of pleural effusion, lung consolidation and collapse: a\u0026nbsp;systematic review. J Physiother. 2021 Jan;67(1):41-48. doi: 10.1016/j.jphys.2020.12.002. PMID: 33353830.\u003c/li\u003e\n \u003cli\u003eKok B, Tuinman PR, Haaksma ME. Lung ultrasound in pneumonia: a guide for effective implementation. Intern Emerg Med. 2025 Mar;20(2):357-367. doi: 10.1007/s11739-024-03807-0. Epub 2024 Dec 10. PMID: 39656349.\u003c/li\u003e\n \u003cli\u003eOgawa K, Kurosaki A, Miyamoto A, Takahashi Y, Murase K, Hanada S, Uruga H, Takaya H, Morokawa N, Kishi K. Clinicoradiological Features of Pulmonary Tuberculosis with Interstitial Pneumonia. Intern Med. 2019 Sep 1;58(17):2443-2449. doi: 10.2169/internalmedicine.2341-18. Epub 2019 May 22. PMID: 31118378; PMCID: PMC6761332.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Basic characteristics of included literature.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"791\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eStudy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003ePublication year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eStudy design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eSex(F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eSuspected disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eConfirm diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003ePatients/Total\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eReali Fet al. (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eProspective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e4\u0026plusmn;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eClinical history and course, CXR, blood analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e81/107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eRodr\u0026iacute;guez-Contreras FJ\u0026nbsp;et al. (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eProspective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eRadiologist\u0026rsquo;s\u003c/p\u003e\n \u003cp\u003echest radiograph report\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e59/82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eAmbroggio L\u0026nbsp;et al. (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eProspective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.5 ~ 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e47/132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eYadav KK\u0026nbsp;et al. (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eProspective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e2.1 \u0026plusmn; 1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e45.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e105 / 118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eVenkatakrishna SSB\u0026nbsp;et al. (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eProspective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eAgreement between\u0026nbsp;general practitioner and a radiologist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e51 /98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eChemeda LA\u0026nbsp;et al. (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eCross-sectional study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eCXR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e47 /108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eHeuvelings CC\u0026nbsp;et al. (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eProspective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e2.2 (1~ 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003ePTB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e36 / 159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eErem G\u0026nbsp;et al. (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eCross-sectional study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eUganda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003ePTB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e51 / 80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eBuz Yaşar A\u0026nbsp;et al. (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eProspective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e2 (0.5~ 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e54.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e90 / 133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eYan C et al. \u0026nbsp; \u0026nbsp; (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eProspective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e12.45\u0026plusmn;3.12 (2 ~ 16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e/ 949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eYilmaz HL et al.\u0026nbsp;(21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;Prospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e3.3\u0026plusmn;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e46.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e149 / 160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eIorio G et al.\u0026nbsp;(22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eRetrospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e4.0 \u0026plusmn; 2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e42.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e47 / 47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eBiagi C et al.\u0026nbsp;(23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eRetrospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.5 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eclinical presentation, laboratory tests and CXR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e25 / 87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: NR, Not reported; CAP, community-acquired pneumonia; LUS, lung ultrasound; CXR, chest x-rays; PTB, pulmonary tuberculosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Diagnostic data information of the included literature.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"584\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eStudy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eLUS / CXR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003eFP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eFN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 105px;\"\u003e\n \u003cp\u003eReali Fet al. (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e93.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e96.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eCXR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e81.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e92.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 105px;\"\u003e\n \u003cp\u003eRodr\u0026iacute;guez-Contreras FJ\u0026nbsp;et al. (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e89.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e70.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eCXR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e77.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e67.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 105px;\"\u003e\n \u003cp\u003eAmbroggio L\u0026nbsp;et al. (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e72.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e74.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eCXR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e80.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e75.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 105px;\"\u003e\n \u003cp\u003eYadav KK\u0026nbsp;et al. (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e98.02%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e64.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eCXR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e93.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e50.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 105px;\"\u003e\n \u003cp\u003eVenkatakrishna SSB\u0026nbsp;et al. (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e80.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e73.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eCXR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e70.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e64.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 105px;\"\u003e\n \u003cp\u003eChemeda LA\u0026nbsp;et al. (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e93.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e98.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eCXR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e61.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e96.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 105px;\"\u003e\n \u003cp\u003eBuz Yaşar A\u0026nbsp;et al. (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e98.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e81.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eCXR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e92.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e82.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 105px;\"\u003e\n \u003cp\u003eYan C et al.\u0026nbsp;(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e90.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e66.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eCXR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e79.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e55.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 105px;\"\u003e\n \u003cp\u003eYilmaz HL et al.\u0026nbsp;(21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e96.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e46.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eCXR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e89.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e53.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 105px;\"\u003e\n \u003cp\u003eIorio G et al.\u0026nbsp;(22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e57.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eCXR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e94.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e44.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 105px;\"\u003e\n \u003cp\u003eBiagi C et al.\u0026nbsp;(23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e83.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eCXR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e96%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e87.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Risk of bias assessment based on quodas-2.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"456\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eStudy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 207px;\"\u003e\n \u003cp\u003eRisk of bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 161px;\"\u003e\n \u003cp\u003eApplicability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003ePatient selection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eIndex test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eReference standard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eFlow \u0026amp; timing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003ePatient selection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eIndex test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eReference standard\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eReali Fet al. (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eRodr\u0026iacute;guez-Contreras FJ\u0026nbsp;et al. (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eAmbroggio L\u0026nbsp;et al. (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eYadav KK\u0026nbsp;et al. (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eVenkatakrishna SSB\u0026nbsp;et al. (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eChemeda LA\u0026nbsp;et al. (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eBuz Yaşar A\u0026nbsp;et al. (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eYan C et al.\u0026nbsp;(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eYilmaz HL et al.\u0026nbsp;(21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eIorio G et al.\u0026nbsp;(22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eBiagi C et al.\u0026nbsp;(23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pulmonary ultrasound, Chest X-ray, Suspected pulmonary infection in children, Meta analysis","lastPublishedDoi":"10.21203/rs.3.rs-7366987/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7366987/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003ePulmonary infectious diseases are a major cause of morbidity and mortality in pediatrics. Lung ultrasound (LUS) has emerged as a diagnostic tool with advantages such as low cost and the absence of radiation exposure. However, its diagnostic performance compared to conventional methods like chest radiography, or called chest X-ray (CXR), needs further evaluation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003eA comprehensive study search was conducted based on the online databases including PubMed, EMBASE, Web of Science, ScienceDirect, Wiley Online Library, and Google Scholar. Studies evaluating and comparing the diagnostic performance of LUS and CXR for pulmonary infections in children were retrieved. Relevant data were extracted, and pooled effect sizes were synthesized to assess the diagnostic accuracy of LUS versus CXR in suspected pediatric pulmonary infections.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003eA total of 655 relevant articles were initially retrieved, and after exclusion, 13 articles were included in the selection, with a total of 2260 pediatric patients involved. Among them, 11 research subjects were patients with common pneumonia, and 2 research subjects were patients with pulmonary tuberculosis. The pooled analysis showed that the sensitivity of LUS and CXR in diagnosing suspected pneumonia in children was 0.94, 95%CI [0.90, 0.97] and 0.86, 95%CI [0.80, 0.89], respectively, while the specificity was 0.77, 95%CI [0.65, 0.86] and 0.74, 95%CI [0.59, 0.85], respectively; the Positive Likelihood Ratio (PLR) of LUS and CXR was 4.16, 95%CI [2.58, 6.70] and 3.29, 95%CI [2.08, 5.23], respectively, and the Negative Likelihood Ratio (NLR) was 0.08, 95%CI [0.04, 0.13] and 0.19, 95%CI [0.14, 0.26]; the areas under the sensitivity receiver operating characteristic (SROC) curve of LUS and CXR were 0.95, 95%CI [0.92 - 0.96] and 0.88, 95%CI [0.85 - 0.91], respectively. In children with suspected pulmonary infections (including pneumonia and tuberculosis), no significant difference was observed in the detection rate of consolidations between LUS and CXR, risk difference (RD)=0.07, 95%CI [-0.09; 0.23], P=0.41; but the detection rate of pleural effusion by LUS was significantly higher than that by CXR, RD=0.06, 95%CI [0.01; 0.10], P=0.02. No significant difference was found in the detection rate of tuberculosis lymph adenopathy between LUS and CXR, RD=-0.09, 95%CI [-0.45; 0.27], P=0.55.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003eLung ultrasound and chest X-ray showed comparable detection rates for pulmonary consolidation in children with suspected pulmonary infections. However, LUS was significantly more effective in detecting pleural effusion. Overall, LUS demonstrated higher sensitivity, specificity, and diagnostic accuracy than CXR in the evaluation of pediatric pneumonia.\u003c/p\u003e","manuscriptTitle":"Meta-analysis of Lung Ultrasound and Chest Radiography in the Diagnosis of Suspected Pulmonary Infections in Children","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-26 16:07:44","doi":"10.21203/rs.3.rs-7366987/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-09-23T08:33:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115243534546145248212883519437947981218","date":"2025-09-17T15:47:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296111530659930601404328860639501660334","date":"2025-09-17T13:40:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-17T12:43:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-02T17:04:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-28T08:09:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-28T07:44:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2025-08-28T07:39:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a1c9bb50-038a-44ba-8de9-06a48665ed42","owner":[],"postedDate":"September 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-26T16:07:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-26 16:07:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7366987","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7366987","identity":"rs-7366987","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}